Home tienganhcaocap Rain, Rain, Go Away!
And now, it's time for the VOA Learning English program Words and Their Stories.In some parts of the world, April is a time of thunderstorms and lots of rain. In the U.S. we even have a rhyme to describe it: April showers bring May flowers.
We will talk about flowers expressions another time. Today, let's talk about rain.
To rain on someone's parade is to question their positive attitude or to ruin their good time. Someone who rains on another person's "parade" is usually trying to introduce a note of caution or practical advice. This person may not mean to make the other person feel bad, but raining on someone's parade can really kill a good mood.
By the way, this is a fixed expression. So, you shouldn't say, "Don't rain on my picnic!" or "Don't rain on my baseball game!" People will have no idea what you're talking about.
In the musical "Funny Girl," Barbara Streisand's character just wants to live her life in her way. She doesn't want others to stop her from trying to achieve her dreams. So, she warns people, "Don't rain on my parade."
"Don't bring around a cloud to rain on my parade. Don't tell me not to fly. I've simply got to. If someone takes a spill it's me not you. Who told you you're allowed to rain on my parade!"
Here is singer Ella Fitzgerald complaining that she is not getting more than her fair share of suffering. Too many bad things are happening in her life. Too much rain!
"Into each life some rain must fall. But too much is falling in mine."
When it rains, it pours is another popular expression. This means when one bad thing happens, many other things often follow. Now, there isn't any science to support this. Perhaps it just feels that way when it's happening to you.
"When it rains it pours. It all seems so sad. Before it's over, it will wash away all I had."
Come rain or come shine means you are not going to let anything stop you. Many people have sang the Johnny Mercer song "Come Rain or Come Shine." Here, it is Ray Charles who tells his love that he will love her no matter what.
"I'm gonna (going to) love ya (you), like no one's loved you, come rain or come shine. High as a mountain, deep as a river, come rain or come shine."
Sometimes, though, you can't do what you want come rain or shine. Sometimes rain does affect your plans.
Let's say you plan a big outdoor party for all your friends. Unfortunately, on the day of the party, it rains cats and dogs – in other words, it rains really heavily. You will have to reschedule. Your guests can take a rain check and come back another time.
"Rain check" comes from the early days of baseball. If a baseball game was canceled because of rain, attendees were given a piece of paper -- a rain check -- allowing them to come back on a different day to see the game.
One exception is the expression right as rain. If something is "right as rain" it is perfectly fine.
For example, let's say you hurt your back playing sports. You could tell your friends not too worry and that you'll be right as rain in a week or two. Some word experts say that "rain" is used in this expression simply for its "r" sound. "Right" and "rain" sound good together.
"And I've heard that into every life a little rain must fall, but you'll never catch me complaining about too much of that southern rain."
Anna Matteo wrote this story for VOA Learning English. Kelly Jean Kelly was the editor. The Cowboy Junkies sing "Southern Rain" at the end of the show.
0 Comment "Rain, Rain, Go Away!"


text:
Posted on December 21, 2011 June 22, 2017 by DeathlessProse1
It's not difficult to understand what Anna saw in Harry Lime, the crooked American whose death is at the center of this film. Amid the ruined city of Vienna, the grim faces of its inhabitants, the climate of suspicion and paranoia that prevails throughout the occupied zones, regardless of which army is in charge, Harry stood out. He never grew up, "the world grew up around him, that's all," Anna explains to Harry's old friend, Holly Martins. She refuses to face the truth: that Harry's black market dealing caused the deaths of countless children. She prefers to hold onto her romantic illusions.
Can you blame her? Three years after the Second World War ended, Europe — as embodied by the once shimmering cultural capital of Vienna — is bombed out, exhausted, physically and morally. The grand ideals for which the war was fought are nowhere in evidence. Freedom seems to be particularly lacking: Holly is shadowed, manipulated and lied to, pushed around by the British occupiers, threatened by the black marketeers.
But there's Harry, staging his own death to free himself from surveillance. "He could fix things… [he knew] how to avoid this and that," Holly says admiringly. Holly himself is hopeless at fixing anything, barely able to look after himself in corrupt postwar Vienna. Straightforward American integrity is a liability in this setting. What's needed is a clever liar like Harry.
And our first glimpse of him, sheltered in a doorway, unexpectedly illuminated by a streetlight, confirms the impression that he alone is at ease in the darkly-lit world. Look at how he's smiling.
Seductive, the way he doesn't let his guard down even when he knows you. Even when you love him. Irresistible to Anna, for sure. I suspect that Harry Lime is Graham Greene's alter ego. "I like to have a secret love affair, a hidden life. Something to lie about," the author of "The Third Man" once said.
Anything is possible from a man like that: betrayal, certainly, but also excitement. He gets his thrills from danger, and danger was everywhere in those days, but his zest for life keeps him one step ahead of the police. Time and again you think he's cornered during the dramatic chase in the sewers, but he manages to outwit his pursuers. We see his fingers poking up through the grate the way a plant sends out new shoots. Toward escape, toward the air. Toward life.
Posted in Drama, NoirTagged Carol Reed, Graham Greene, Noir, Orson Welles
Next Post The Rose Tattoo

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction}

Technological developments throughout history have fundamentally changed how people communicate and interact with one another. With new successes come new challenges, as the rapid proliferation of the internet has led to a phenomenon known as cyberbullying. Many questions may arise, such as how cyberbullying can propagate and how it interacts with global crises such as the COVID-19 pandemic. In this paper, we combine natural language processing and Bayesian time series analysis methods to provide a systematic assessment of the trends of cyberbullying for a span of three years. We begin this exposition with an informal description of cyberbullying before describing the connection with COVID-19 and Twitter.

\subsection{Describing Cyberbullying}

 One definition for cyberbullying is "\emph{An aggressive, intentional act carried out by a group or individual, using electronic forms of contact, repeatedly and over time against a victim who cannot easily defend him or herself}" \citep{smith2008cyberbullying}. And with increases in computer use, the possibility for cyberbullying grows

An immediate question is what sets cyberbullying and traditional in-person bullying apart. These differences are paramount when considering a theoretical approach to studying cyberbullying, as models made with traditional bullying in mind may not tell the two apart \citep{barlett2017theory}. One major point is that cyberbullies can maintain anonymity online which makes it difficult to locate perpetrators \citep{bonanno2013cyber}. Due to the reach of social networks, cyberbullying may also persist far beyond the reaches of normal bullying and can proliferate to large swaths of people, often attaining viral status \citep{aboujaoude2015cyberbullying}. Cyberbullying is a perpetual phenomenon that constantly places stress on the victim.

Cyberbullying has been studied to be a cause of many negative outcomes in victims. A meta-analysis conducted in the topic reveals correlations with low self-esteem, depression, and drug abuse \citep{kowalski2014bullying}. Many episodes of attempted suicide and self-harm have been directly attributed to cyberbullying \citep{kwan2020cyberbullying}. Interestingly, according to surveys, only a small percentage of cyberbullying victims are not bullied in a traditional, in-person manner \citep{olweus2018some}. Regardless, the many negative outcomes and pervasiveness of cyberbullying has led to some to suggest it is a serious public health threat, and its danger can only grow with increased mobile device and social media usage \citep{aboujaoude2015cyberbullying}.

\subsection{COVID-19 and Cyberbullying}
The proliferation of COVID-19 has significantly changed the way many people live. Lockdowns have been put in place to curb the spread, but not without consequences. As humans thrive in social situations, the isolation of many from the day-to-day affairs has led some to posit that people's mental health will worsen, noting consequences such as maladaptive behaviors, loneliness, and depression \citep{talevi2020mental}. Direct research shows that quarantines and self-isolation are linked with higher prevalence of issues like depression and insomnia \citep{wang2021impact}. When comparing time periods before and after the COVID-19 pandemic, \cite{barlett2021comparing} suggest that important components of their cyberbullying model, such as cyberbullying attitude, cyberbullying behavior, and belief in irrelevance of muscularity in online bullying, have significantly changed between these time points. It is also observed that cyberbullying is correlated with COVID-19 experiences \citep{barlett2021cyberbullying}.

The interaction of COVID-19 and cyberbullying in academic settings is a topic of great interest. As universities and schools around the world shifted to online instruction to deter spread of the virus, approximately 1.5 billion students have had their education interrupted \citep{bozkurt2020global}. The effects on isolation on university students is an important matter, as these groups show high proportions of common mental disorders. Such literature reveals these groups were associated with more frequent internet use and may thus have increased probability of being involved in cyberbullying \citep{mota2021mental}. Quantitative analysis of such groups in India \citep{jain2020has} have shown that 80\% of those between 17 and 18 years old were bullied during the pandemic, and 79\% of those experiencing traditional bullying before the pandemic were cyberbullied during pandemic, corroborating the notion that victims of traditional bullying are also victims of cyberbullying. Other categories of victims show an increase in percentage of those cyberbullied from before to during the pandemic as well.

\subsection{Using Twitter to Understand Social Phenomena and Cyberbullying}
As in the aforementioned studies, much research in cyberbullying involves the use of survey data. However, if one relies on a responder's willingness to self-report, then that leaves the door open to problems such as responder bias and invalid responders. In a study on adolescent regarding risk behavior, it is found that responders who purposefully answer wrongfully "showed" higher rates of such behavior, such as alcohol and drug consumption \citep{cornell2012effects}. Such a happening may be present in bullying surveys as well, which can be exacerbated by sample-size limitations imposed by cost and time. Additionally, if a researcher was interested in studying the impact of COVID-19 on cyberbullying, they ideally have to collect data before the pandemic began, as done in previous research \citep{barlett2021comparing}. Since the magnitude and impact of global crises are sometimes unforeseeable, it can be difficult to know when to start such a longitudinal study.

Social media can serve as rich data source that remedies some of the issues that affect survey collection. One major advantage is the scope of social media. Twitter, the choice for our study, had approximately 186 million users, 36 million of which from the United States~\footnote{\url{https://www.businessofapps.com/data/twitter-statistics/}} in 2020. Furthermore, the availability of web scraping technology and even an official API allow individuals to use Twitter's public archive of tweets dating back to 2006. This software is often free to use, making large-scale studies much more affordable. Information harvested from these tweets can be used for many purposes, such as monitoring disease spread and forecasting elections \citep{signorini2011use, tumasjan2010predicting}. This data source enables us to perform a study reminiscent of longitudinal study, with the ability to collect previous years of data without the associated cost of maintaining a large-scale study for many years.

However, one may question the efficacy of using social media such as Twitter as a data source to understand social phenomena. For example, \cite{tumasjan2010predicting} showed that using Twitter traffic to predict vote share in German errors resulted in very low prediction error. \cite{signorini2011use} demonstrate a correspondence between Twitter data and the H1N1 at the overall national level, as well as smaller geographic regions. Based on real-time data, their estimates could be produced earlier than regular health reports. While these present advantages, one must also consider the possible limitations. For instance, predicting elections using Twitter faces two major issues, one being that sampling data from social media does not match the sophistication of more developed polling processes, and that spam, propagandists, and fake accounts can easily manipulate data \citep{gayo2011limits}. In the influenza study conducted by \cite{signorini2011use}, the researchers faced limitations in the lack of uniformity of Twitter usage by different locales and in different time periods. They also could not generalize to a population beyond some form of Twitter population. These limitations are also present in the current study. Brief use of the search endpoint for an arbitrary query may show a few spam or bot posts, heightening the importance of filtering these posts out. Additionally, since Twitter's sampling algorithm is unknown, it is difficult to identify the exact population the study's results can be generalized to. There are methods to reduce this uncertainty by using geo-tagged tweets, as retrieving tweets of this nature results in a more complete sample \citep{morstatter2013sample}.

Despite limitations, there are a variety of points to make in justifying Twitter as a data source to study cyberbullying. \cite{mchugh2019omg} suggests that Twitter is a hotbed for "intentionally aggressive, harmful communication." They explain results from surveys showing that about 70\% of college students use Twitter, and the amount of cyberbullying on Twitter was gauged to be higher than other platforms such as Facebook and Instagram. Additionally, the official Twitter API~\footnote{\url{https://developer.twitter.com/en/docs/twitter-api}} allows users to perform a variety of queries and searches on its public archive. With this technology, we can collect swaths of tweets satisfying certain specifications, such as containing particular keywords or hashtags, in order to study the cyberbullying problem.

To analyze the tweets retrieved from Twitter in the first place, many researchers turn to natural language processing (NLP). The usage of NLP in this study is similar to that of previous work done regarding Twitter cyberbullying and COVID-19 \citep{babvey2021using}. The study by \cite{babvey2021using} uses NLP as a pre-processing step to filter out tweets that have low probability of being abusive speech, and then examine the difference in number of cyberbullying tweets before and after a fixed time period. In this study, we use NLP to perform a similar pre-processing step, but instead of comparing a fixed time point, we study each day from 2019 to 2021. Few studies have attempted to study cyberbullying in response to COVID-19 from a continuous perspective. Researchers using Google Trends time series data \citep{bacher2022covid} demonstrate that cyberbullying was actually disrupted by COVID-19, while others, using Twitter data \citep{karmakar2020evaluating}, claim that it led to an increase. We seek to address these studies by broadening the time frame of analysis and using more thorough methods, namely NLP, to obtain better samples to study cyberbullying patterns with.

\subsection{Summarizing Our Contribution}
For our study, we collected 1,004,466 tweets from January 1st, 2019, to December 31st, 2021, with the Twitter API's search endpoint based on keywords associated with hateful and abusive speech. To clean the data, a pre-trained NLP model tuned to classification of offensive and hateful tweets models the probability of a given tweet being offensive or hateful. Because of irregularities in the search endpoint's returned sample sizes, the count endpoint is used with the same keywords as before to obtain a more consistent and comprehensive count of all tweets that the search API could have picked from. The tweets from the search endpoint are then filtered using certain probability thresholds to select relevant tweets. Then, for each day in the study, the fraction of relevant tweets out its respective sample is calculated. This fraction is multiplied by the number reported by the count endpoint, generating a time series that takes into account the different proportions of abusive content on each day while addressing issues with the search endpoint's sampling procedure. This data collection and filtering is described in section 3.

Visual analysis of the data collected is in section 4 and motivates our usage of a Bayesian time series model in section 5. The results demonstrate that abusive speech exhibit strong weekly and year seasonality, which is not as evident in the unfiltered data. Patterns such as two distinct increases in mean trend during the first and second halves of the year remain constant throughout 2019 through 2021, but with slight differences. The effect of the pandemic on these trends, if at all, is to decrease the scale of abusive tweets, as well as widen the second peak of the year. The proportion of these potentially abusive tweets from their respective daily samples also seems to roughly decrease over time. The results can help guide developers of Twitter or other social media to ramp up mitigation technology at the appropriate time by taking the strong seasonal behavior into account.

The work is concluded by a discussion of the issues and limitations of the study, the implications of the results found, and avenues for future research.

\section{Related Work on Internet Data, Cyberbullying, and COVID-19}
This study is motivated by the findings of \cite{karmakar2020evaluating}, which employed a Bayesian, time-varying linear Poisson autoregressive model to tweet counts containing keywords related to cyberbullying. Such an analysis was first of its kind in this field. Their study, confined to the first half of 2020, concluded a rise in mean trend from March to April similar to that of COVID-19 cases and that the first lag accounted for most of the correlation. However, there is criticism to be made in that preliminary analysis. The choice of keywords along with lack of text analysis confined the results to cyberbullying discourse rather than actual cyberbullying events. Cyberbullying attacks may precipitate awareness and discourse, but they do not follow the same time series. While their work collected data using web scraping, the current study pulls data directly from the Twitter API.

Another work by \cite{bacher2022covid}, instead of using Twitter data, opts to use Google Trends, a site that provides time series data for search intensities of search terms~\footnote{\url{https://trends.google.com/}}. This work studies search intensity of cyberbullying and bullying. Like the previous study, they do not depict the frequency of cyberbullying events exactly. Further, since the main model in our study is based on a Poisson distribution, which requires count data, we cannot use the TVBARC model on Google Trends data in attempt to replicate the study.

However, consider the two following similar findings from these works. The model fit by \cite{karmakar2020evaluating} reveals an increase in mean trend from around March to May of 2020, while the model constructed by \cite{bacher2022covid} shows that the deviation from predicted log search intensity increases roughly around the same time frame. One important distinction, though, is that the Bacher-Hicks study includes data from before January 2020 and slightly after. Bacher-Hicks claims, further, that this rise in the March-May period was just an increase back to levels before the onset of the pandemic, and that COVID-19 had disrupted cyberbullying. In the absence of a wider time frame in Karmakar's paper, one may conclude an increase in cyberbullying discourse on Twitter, but this may very well suggest a return to pre-pandemic levels as Bacher-Hicks describes. Again, one cannot say that this necessarily extends to cyberbullying events, but how it becomes a trending topic over time. Furthermore, social media and search engines are used with different motives in mind, which may result in discrepancies in findings \citep{li2021comparing}.

The work done by \cite{babvey2021using} motivates our decision to employ an NLP model in an attempt to retain true cyberbullying events. They query Twitter for keywords associated with abusive speech and then run a machine learning model to discard tweets that are likely not abusive. By using such methods, they are able to have more confidence that their data can represent actual cyberbullying events. They compare two sets of data collected before and after March 2020 to gauge the effect of COVID-19 and the associated interactions with cyberbullying. Their results show an increase in prevalence of abusive and hateful tweets once the pandemic-era lockdowns began.

Thus, to address these above-mentioned issues, we instead opt to use NLP methods to avoid the issue of covering cyberbullying discourse rather than potential cyberbullying events. The use of a larger time frame along with a more continuous time series approach allows one to see whether COVID-19 has a sustained effect on Twitter cyberbullying, or if previous findings may have been coincidences or one-time occurrences. Now, the data collection and NLP filtering procedure are discussed.

\section{Data Collection and Cleaning}
A straight-forward way to access to Twitter data is by using the official Twitter API. To make the most use of the Twitter API, we were provided with an Academic Research License, granting us features such as the full-archive search. This is critical to our research, as it lets us search many years' worth of data quite easily. We used the R programming language to work with the API and perform ensuing analyses.

\subsection{A Foreword on the Twitter API}

Unfortunately, the algorithm used by the Twitter API to sample tweets is unknown. As studied by \cite{thelwall2015evaluating}, the search endpoint may not be comprehensive. However, the tweets that were not retrieved by its sampling procedure are more likely to be spam. Other works \citep{morstatter2013sample} point out that the sampling tweets may result in decreased accuracy (as compared to alternative, costly methods to acquire every single tweet), but interestingly, the sampling algorithm recovers a higher proportion of tweets that are geo-tagged .

The nature of time series analysis emphasizes these sampling issues. The subset of tweets taken from all matching tweets may not be a fixed percent, so the relative sizes between daily counts is not preserved. Simple repetitions of identical requests may sometimes return more tweets seemingly at random. Comparison with the number reported by the count endpoint is an enticing option for a couple reasons. The count endpoint returns much more consistent results through runs, and since it does not have to go through additional compliance that the search endpoint does, the data returned may be more complete~\footnote{\url{https://developer.twitter.com/en/docs/twitter-api/tweets/counts/introduction}}. To this end, we take the percentage of relevant tweets from a given daily sample and multiply that percentage to the number reported by the count endpoint. Selection of relevant tweets is described below.

\subsection{Collection Procedure}
To collect a representative dataset, we first sample tweets using the search endpoint to access their textual content. A list of keywords must be assembled to query for in both the search and count endpoints. As a starting point, we reference a lexicon provided by \cite{wiegand2019inducing}. It contains a list of words each with a score rating its abusiveness according to their trained model. One may notice that identical words appear more than once (as a verb and as a noun, for instance). The Twitter API cannot distinguish keywords based on it being a noun or verb, so if a given word appears more than once, we only keep the highest score and discard the other entries. From this adjusted list, the 100 highest ranking words were taken.

For comparison purposes, we reference two similar studies of cyberbullying analysis \citep{nand2016bullying, cortis2015analysis}. Their lists of keywords are different and are only composed of about 25 and 10 words respectively. To test the efficacy of our keywords, we sample tweets in January 2020 using our original list of words, specifying no retweets, written in English, and based in the United States. We then count the number of tweets each keyword in our list appeared in, including keywords in the other studies that were not in our original list. Based on numbers of tweet occurrences, we again take the 100 highest performing words.

With this new list, we use the search endpoint to query the entirety of 2019, 2020, and 2021. Like before, we specify no retweets, tweets written in English, and tweets from the US, but this time we also specify no promotional tweets. Each day contains anywhere between 400 and a couple thousand tweets with their textual content. In total, we collected 1,004,466 tweets. The same query is also used for the count endpoint to collect daily counts. Once the full data set is assembled into a data frame, the results are written into CSV files for storage.

\subsection{A Pre-trained NLP Model for Pre-processing Data}
Another issue is that the tweets we collect, no matter how good our query is, may not necessarily contain all abusive content. This is one issue in previous work, as the keywords used reflected discourse about cyberbullying, but not necessarily cyberbullying events themselves. Furthermore, with no filtering mechanism besides removing duplicates, it is possible that spam tweets are included in the data set \citep{karmakar2020evaluating}. Along with using more stringent parameters for querying tweets, we also opt for a pre-trained natural language model to predict the offensiveness and hatefulness (against women or immigrants) of a given tweet. The models are derived from the work by \cite{barbieri2020tweeteval}, and such models are publicly available for use on HuggingFace~\footnote{\url{https://huggingface.co/cardiffnlp/twitter-roberta-base-offensive}}~\footnote{\url{https://huggingface.co/cardiffnlp/twitter-roberta-base-hate}}. Thus, depending on how we threshold tweets based on their offensiveness/hatefulness score, the ensuing analysis can lead to stronger results.

To use these models, we work with the Python language in Google Colaboratory~\footnote{\url{https://research.google.com/colaboratory/}}, which provides a high performance cloud computing environment and greatly simplifies set-up of packages and other dependencies. Instructions for basic set-up to use HuggingFace are found on the website \footnote{\url{https://huggingface.co/course/chapter0/1?fw=pt}}. Template code for using the models is found on the model pages. While there is a text pre-processing step that is part of the template code, it does not contain the additional step of removing line-breaks that the authors of the model, \cite{barbieri2020tweeteval}, used in their own analysis, so it was added to the text pre-processing function. We also converted all text to lowercase to handle erratic capitalization and removed duplicate white space. The fitting procedure was generalized to collections of text using a for loop.

Using two of their models, we can produce the probability of tweet being offensive and the probability of a tweet being hateful. To filter out irrelevant data, we must properly select thresholds for each of these scores.

\subsection{Subsetting Truly Offensive or Hateful Tweets}

\begin{figure}
    \centering
    \hspace*{-15mm}
    \includegraphics[scale=1, width=100mm]{Figures/DistributionOfMetrics.png}
          \caption{Modeled probabilities of offensiveness/hatefulness of tweets according to the NLP model}
    \label{fig:DistributionOfMetrics}

\end{figure}

To begin a discussion on thresholds, we first explore the distribution of the scores themselves. In figure \ref{fig:DistributionOfMetrics}, one may notice that the scores of offensiveness are bimodal with peaks 0 to 1, with fewer presences of tweets near the center. The high presence of tweets near 1 is likely related to the nature of the query. In the construction of the model by \cite{barbieri2020tweeteval}, they used a dataset created by a different group for a similar task. The creators of said dataset, \cite{zampieri2019semeval}, describe a tweet as offensive if "it contains any form of non-acceptable language (profanity) or a targeted offense ... This category includes insults, threads, and posts containing profane language or swear words." Thus, if our query contains many profane keywords, we are likely to see many tweets with a high predicted probability for offensiveness.

On the other hand, hatefulness scores are mostly lower than 0.25, and much fewer tweets have scores beyond. To reiterate, the model was fine-tuned to detect hatefulness against two target groups, being women and immigrants. The lower presence of high-probability tweets can be partially explained by queried words, as they lack many words that are explicitly targeting women or immigrants, such as those used by the authors of the dataset \citep{basile2019semeval}.

There are some tweets which the model failed to fit. These tweets all contained copious amounts of emojis which caused issues with the model's tokenizer. Out of the 1,004,466 tweets in this study, only 7 failed to process. Each of these tweets occurred on a different day, making it exceedingly unlikely for them to affect analysis.

Now we observe the effects of subsetting tweets with scores strictly greater than a certain probability threshold, getting the percentage of those tweets from their respective daily sample, then multiplying that percent to the number reported by the count endpoint. Different thresholds are used, and the series is superimposed by a GAM smoother. The results for this process, varying offensiveness while fixing hatefulness, are displayed in figure \ref{fig:DeltaOffensive}. The local maxima are kept intact until accepting only larger scores of greater than 0.8. Likewise, for filtering on hatefulness in figure \ref{fig:DeltaHateful}, the data is much more sensitive to applying greater thresholds. When applying a threshold of 0.05 on hatefulness, the values in January 2019 were around 60,000, and then hovered around 45,000 until 2021. A similar pattern is exhibited when thresholding at the much greater value of 0.5 on offensiveness. These results are a consequence of the distribution of overall scores as shown in figure \ref{fig:DistributionOfMetrics}, with offensiveness scores being bimodal near 0 and 1, while hatefulness clusters near 0 and tapers off rapidly.

\begin{figure}
    \centering
    \includegraphics[scale=0.6]{Figures/CountEndpointData.png}
          \caption{Daily count of total tweets containing queried keywords, 2019-2021}
    \label{fig:CountEndpointData}

\end{figure}

\begin{figure}[!tbp]
  \centering
  \hspace*{-10mm}
  \begin{minipage}[b]{0.5\textwidth}
  \hspace*{-20mm}
    \includegraphics[scale=0.5]{Figures/DeltaOffensive.png}
          \caption{Results of Subsetting Method Using Offensiveness}
    \label{fig:DeltaOffensive}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.5\textwidth}
  \hspace*{10mm}
    \includegraphics[scale=0.5]{Figures/DeltaHateful.png}
           \caption{Results of Subsetting Method Using Hatefulness}
    \label{fig:DeltaHateful}
  \end{minipage}
\end{figure}

Now we are tasked to choose a threshold for our dataset, then feed this data into the TVBARC model. In some problems, 0.5 may be used, but we know from reality that the occurrence is of cyberbullying events is not as common as that would suggest. However, given that our query contains keywords associated with abusive behavior, it's possible that the proportion of cyberbullying events among the collected tweets will be higher. Other considerations include the NLP models' performance on their associated test data. The M-F1 score for the hatefulness model is around 50, whereas for offensiveness, it is around 80 \citep{barbieri2020tweeteval}. An additional factor is the TVBARC model itself, which may fail to converge if the data points are too large in magnitude. From above, one can observe that increasing the minimum offensiveness threshold does not greatly alter the structure of the time series, so it can be used to cut down on the scale as necessary. Note that these threshold parameters can be freely tuned to a desired sensitivity. However, making the threshold too high may result in many 0s in the series, which is especially true when filtering on hatefulness. Not all offensive speech is cyberbullying, but hate speech can contain offensive speech like slurs, in this case directed to women or immigrants. That being said, the data set has a non-zero number of tweets that lack offensive content but have a high probability of being hateful. Therefore, it would be wise to increase the threshold on offensiveness as necessary to allow model convergence and primarily focus on altering the hatefulness threshold.

In this study, we will use two separate thresholds for comparison. First, some notation is established. An $x/y$ filter will refer to accepting only tweets with offensiveness probability greater than $\frac{x}{100}$ and hatefulness probability greater than $\frac{y}{100}$. In this notation, the two filters used are $25/0$ and $25/50$. Using these, of interest is rudimentary comparison with prior results before we fit to the TVBARC model. To motivate the usage of this TVBARC model, a visual analysis on the counts is performed.

\section{Visual Analysis on the Raw and Filtered Counts}
A visual analysis, similar to that done by \cite{karmakar2020evaluating}, allows one to deduce some trends and patterns. However, certain issues will limit the efficacy of such an analysis and justify implementation of a statistical model. First, we focus on the counts provided by the count endpoint with no thresholding, observed in figure \ref{fig:CountEndpointData}.

Starting in 2019, one sees a sudden drop in counts, which levels out until the end of 2020. In 2020, there is a prominent peak in April, which roughly agrees with the findings of \cite{karmakar2020evaluating}. Throughout the rest of 2020, there are several spikes, but they are not persistent. When 2021 begins, the counts drop yet again, but unlike 2019, the counts stay at these lower levels.

In both 2019 and 2021, one observes a decrease in counts in the beginning of the year, though this does not occur in 2020. Instead, there are many large peaks though with an overall downward trend. In 2021, the downtrend accelerates and soon levels off. It is possible that with the advent of COVID-19, the typical downward trend was disrupted as lockdowns and social isolations precipitated increased internet use \citep{candela2020impact}. The significant decrease may also be related to the findings of \cite{bacher2022covid} in their study, which uses Google search frequencies and bullying surveys to study the change in cyberbullying-related searches over time. The study shows that the log search intensity of school bullying and cyberbullying significantly decrease near the end of 2020 and beginning of 2021. But since one time series involves profanity and potentially hateful language on social media, and the other search engine data about bullying, it's possible that this is a coincidence. Additionally, due to the lack of thresholding, one cannot say that the frequency of cyberbullying events also follows the same trend.

We revisit the effects of thresholding, but now in the context of identifying trends. In figure \ref{fig:DeltaOffensive}, as the offensiveness threshold increases, the peaks shrink, and the trend begins to flatten. Note that it takes a threshold of 0.9 to induce some significant flattening. However, when thresholding on hatefulness, it only takes a threshold of 0.2 to achieve a similar degree of flattening. By the time we increase it to 0.5, the time series may appear constant, as shown in figure \ref{fig:Filtered2020Legend}, which shows the different threshold settings in 2020. In both the raw and 25/0 time series, the peaks are easily identifiable, and thresholding on offensiveness works to reduce the scale while preserving the peaks. When using a large hatefulness threshold such as 0.5, prominent peaks disappear.

We are not necessarily satisfied with the analysis of the raw or 25/0 data since it may not represent the actual frequency of cyberbullying events. Further, it is also clear that applying any reasonable threshold on hatefulness may cause the time series to flatten out, making it difficult to identify trends visually. Therefore, to identify trends in abusive tweets, we must employ a statistical model.

\begin{figure}
    \centering

    \includegraphics[scale=0.65]{Figures/Filtered_Data_2020_Legend.png}
          \caption{Total counts and filtered counts in 2020}
    \label{fig:Filtered2020Legend}

\end{figure}

\section{Statistical Modeling and Analysis}
While there are many models we can choose from to study this data, we opt to employ a Bayesian model. This model is a time-varying Bayesian autoregressive count (TVBARC) model based on Poisson random variables \citep{roy2020bayesian}. There are many advantages to using this form of model, such as mitigating small sample-size, accounting for dependence, modeling subtle rather than abrupt change, and only needing a single parameter. More information on the motivations of the model, including in the context of Twitter data, is available in previous works of one of the authors \citep{karmakar2020evaluating,das2020change,karmakar2021understanding}.

Suppose $Z_t$ represents the true counts of all tweets, satisfying the query, that are as hateful and offensive as our threshold dictates. Let the time series provided by the count endpoint be $Y_t$. Now let $p_t$ be the proportion of sample tweets on day $t$ that meet the threshold requirements, with $0 \leq p_t \leq 1$ for all $t$. Then, we estimate series $Z_t$ by

$$\hat{Z_t} = X_t = p_t Y_t$$

For this model, the conditional distribution for a count time series $X_t$ given $\mathcal{F}_{t-1}=\{X_i: i\leq (t-1)\}$ is

$$ X_t|\mathcal{F}_{t-1}\sim \mathrm{Poisson}(\lambda_t)$$

$$ \text{where }   \lambda_t=\mu(t/T) + \sum_{i=1}^p a_i(t/T) X_{t-i}$$

Furthermore, $\mu(t/T)$ is the mean trend at time $t$, and $a_i(t/T)$ is the effect of the $i$th lag at time $t$. Additional details on restrictions and priors can be found in the work by \cite{karmakar2020evaluating}, as the very same model is used. We apply this model to the estimation $X_t$ of the true counts $Z_t$.

The fitting of the model over a count time series provides the mean trend of the series over time, as well as the coefficient values of the different autoregressive terms. From this one can deduce how the frequency of offensive/hateful tweets changed over time, as well as which lags are most related to each other. In the fitting procedure, we generate two models that differ in up to how many lags are represented. One contains up to lag 10, and the other 15. Generally, the mean trend captured is the same between these two models, though the width of the credible intervals may change. Exceptions to this will be mentioned when relevant.

\subsection{Analysis of Model Fits}
For 2019, there is no significant difference between the results of the lag 10 and lag 15 model, so we only focus on lag 10.

Figure \ref{fig:Lag10-25/0-2019} displays the mean trend and coefficient values of the count data in 2019 by filtering on tweets with offensiveness greater than or equal to 0.25. As shown in figure \ref{fig:DeltaOffensive}, for low thresholds, this filter reduces the scale of the original data set, preserving all structure, so this can be interpreted as a scaled down version of the original count data. Here the credible intervals are quite small, so if the true counts were to be distributed by a 10-lag process, the mean trend would look like this. However, we also see no dependence on any lag besides the first. In the analogous model fit for the 25/50 data in figure \ref{fig:Lag10-25/50-2019}, which focuses more on hateful tweets, we see a similar mean trend, capturing the peak between July and October. However, lag 7 becomes a lot more significant, and an earlier peak in the month is revealed. This process shows that the modeling procedure can uncover local extrema even when the process looks almost constant, like in figure \ref{fig:Filtered2020Legend} with the 2020 data.

\begin{figure}[!tbp]
  \centering
  \hspace*{-10mm}
  \begin{minipage}[b]{0.5\textwidth}
  \hspace*{-20mm}
    \includegraphics[scale=0.5]{Figures/Lag_10_Model_Fit_on_25-0_Filter_2019.png}
          \caption{Mean trend and AR coefficients of Lag 10 model on 2019 data with 25/0 filter}
    \label{fig:Lag10-25/0-2019}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.5\textwidth}
    \includegraphics[scale=0.5]{Figures/Lag_10_Model_Fit_on_25-50_Filter_2019.png}
          \caption{Mean trend and AR coefficients of Lag 10 model on 2019 data with 25/50 filter}
    \label{fig:Lag10-25/50-2019}
  \end{minipage}
\end{figure}

The fits for 2020 are in figures \ref{fig:Lag10-25/0-2020} and \ref{fig:Lag10-25/50-2020}. The trend captured between the two threshold settings is roughly the same. However, the credible intervals for the model passing only more hateful tweets are much wider. In the 25/50 setting, lags around 7 start to get a bit more significant. One may also directly compare this to previous results. In Karmakar's work, the peak of cyberbullying discourse occurred in April-May 2020, while in this setting, the peak in offensive and hateful tweets occurred later in the year, between July and October \citep{karmakar2020evaluating}. A smaller peak near the beginning of the year is also observed. Compared to previous work, the fits here have much wider credible intervals as well.

\begin{figure}[!tbp]
  \centering
  \hspace*{-10mm}
  \begin{minipage}[b]{0.5\textwidth}
  \hspace*{-20mm}
    \includegraphics[scale=0.5]{Figures/Lag_10_Model_Fit_on_25-0_Filter_2020.png}
          \caption{Mean trend and AR coefficients of Lag 10 model on 2020 data with 25/0 filter}
    \label{fig:Lag10-25/0-2020}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.5\textwidth}
    \includegraphics[scale=0.5]{Figures/Lag_10_Model_Fit_on_25-50_Filter_2020.png}
          \caption{Mean trend and AR coefficients of Lag 10 model on 2020 data with 25/50 filter}
    \label{fig:Lag10-25/50-2020}
  \end{minipage}
\end{figure}

In the year of 2021, fitting the data through the 25/50 filter to the TVBARC model resulted in very different outcomes when using lag 10 (figure \ref{fig:Lag10-25/50-2021}) as opposed to 15 (figure \ref{fig:Lag15-25/50-2021}). For the 25/0 data, there is no major difference besides credible intervals and mean trend magnitude. There is an exceedingly rough similarity between the lag 10 and lag 15 model, but it is obfuscated by the very wide credible intervals in the lag 10 model. Yet in both models, lag 7 is important, as the coefficient value for this lag overtakes lag 1 during certain periods. In the lag 10 model, lag 6 is relevant, while in the lag 15 model, lag 13 is important. The results are similar, since if there is a weekly effect, one could also observe a "biweekly" relationship as well. Any dependence on lag $p$ may cause lag $p+7$ to be significant.

\begin{figure}[!tbp]
  \centering
  \hspace*{-10mm}
  \begin{minipage}[b]{0.5\textwidth}
  \hspace*{-20mm}
    \includegraphics[scale=0.5]{Figures/Lag_10_Model_Fit_on_25-50_Filter_2021.png}
          \caption{Mean trend and AR coefficients of Lag 10 model on 2021 data with 25/50 filter}
    \label{fig:Lag10-25/50-2021}
  \end{minipage}
  \hfill
  \begin{minipage}[b]{0.5\textwidth}
    \includegraphics[scale=0.5]{Figures/Lag_15_Model_Fit_on_25-50_Filter_2021.png}
          \caption{Mean trend and AR coefficients of Lag 15 model on 2021 data with 25/50 filter}
    \label{fig:Lag15-25/50-2021}
  \end{minipage}
\end{figure}

In general, when modeling the data on the 25/0, a scaled version of the raw counts, we most often do not see any weekly effect. However, when only considering tweets with hatefulness probability greater than 0.50, there is a very noticeable weekly effect, bringing out lags like as 5, 6, 7, 13, and 14. From this, we can infer that the frequency hateful tweets similar to those captured in the study have noticeable weekly seasonality that goes otherwise unobserved when looking at the raw counts. Additionally, the yearly pattern of two peaks in the beginning and end of the year roughly holds for 2019, 2020, and 2021. This implies that this time series also has a strong yearly seasonality. Furthermore, the mean of the hateful tweets, in most cases, follows a similar mean trend as the 25/0 series. Since this is just a scaled down version of the total data, the trend of hateful tweets mirrors that of the raw counts. This makes some sense since the filtering process takes a percentage of these counts. However, it may also reveal that the proportion of hateful tweets out of all tweets on a certain day remains about constant throughout time.

Next, correspondences with COVID-19 case counts are discussed in order to gauge possible relationships between the two time series.

\subsection{Comparison to COVID-19 Case Counts}
By analyzing only tweets sourced from the United States, we may focus on COVID-19 counts from the same country. Depicted in figure \ref{fig:COVID-19 2020-2021} are the daily confirmed COVID-19 cases, taking a 7-day rolling average, provided by \cite{owidcoronavirus}. Beginning in 2020, there are consecutively larger peaks at around March, July, and October-December. We see a steep drop beginning near 2021, picking up during August-October. There is an exponential increase during the beginning of 2022.

COVID-19 did not gain much traction in the United States until 2020. Therefore, the Twitter data from 2019 could be seen as a form of experimental control, or what the time series typically looked like before the drastic changes brought about by the pandemic. In figure \ref{fig:Lag10-25/50-2019}, There is a peak in the first few months of the year, and then another during July-October. This same structure is also observed in 2020, as shown in figures \ref{fig:Lag10-25/0-2020} and \ref{fig:Lag10-25/50-2020}, as well as 2021, in figures \ref{fig:Lag10-25/50-2021} (roughly) and \ref{fig:Lag15-25/50-2021}. While the trend structure is similar, indicating the importance of seasonality, one distinction is that the secondary peak is largest in magnitude in 2020 (mean trend of 3000), followed by 2019 (about 2500) and then 2021 (about 1600). The largest peak occurring in 2020 is slightly corroborated by previous findings \citep{karmakar2020evaluating}. Further, with each year the second peak persists for longer. This may be related to a variety of things, such as increased transmission during the winter, as COVID-19 proliferation rates can change significantly with temperature \citep{mcclymont2021weather}, prompting more isolation and thus internet use, or increased cyber-aggression as a psychological response to the external stress of the pandemic \citep{wang2022covid}.

The mean trend structure's similarity across these three years begs the question of whether earlier results were more of a coincidence. More specifically, previous results show that cyberbullying discourse increased roughly the same time as COVID-19 cases began to rise \citep{karmakar2020evaluating}. While these peaks occur at about the same time every year, it is important to note the change in magnitude throughout the years, dipping significantly in 2021. One may argue that the beginning of the pandemic may have brought about a sudden increase in abusive content, but as time progressed, the overall effect was to reduce such content. This angle is supported by findings using Google Trends \citep{bacher2022covid}.

It is also possible to study the proportions of hateful tweets, represented by $p_t$ in the model definition. These proportions give a better idea as to the relative frequency of abusive events, as the previous time series studied can be affected by the overall number of active users. Figure \ref{fig:PropTrend25/50} displays the proportion of daily tweets in the daily sample that meet the 25/50 threshold setting with a GAM smoother. The data is extremely noisy, but the smoother reveals a slight downward trend beginning just before 2020, suggesting a steady decrease in the proportion hateful content as the pandemic progressed.

\begin{figure}
    \centering
    \hspace*{-10mm}
    \includegraphics[scale=0.6]{Figures/ProportionTrend_25-50.png}
          \caption{Proportion of tweets meeting the 25/50 threshold out of the entire daily sample superimposed by a GAM smoother}
    \label{fig:PropTrend25/50}

\end{figure}

\begin{figure}

    \includegraphics[scale=0.1]{Figures/daily_covid-19_cases_2020-2021.png}
          \caption{7-day average of confirmed COVID-19 cases in 2020-2021}
    \label{fig:COVID-19 2020-2021}

\end{figure}

\section{Discussion}
In this work, we use the official Twitter API's search endpoint to collect tweets contained keywords associated with cyberbullying over a period of 3 years. After running these tweets on an NLP model, we calculate the proportion of relevant tweets out of all tweets sampled that day. This proportion is then multiplied to the count endpoint's associated number, which is shown to be more reliable and thorough. From this we can construct a time series that is insensitive to the irregularities in daily sample sizes and better approximates the true number of hateful or offensive tweets.

The results show that the mean trend pattern of hateful tweets remains very similar through 2019, 2020, and 2021. Previous work posits that increased cyberbullying discourse happens in parallel with larger case counts \citep{karmakar2020evaluating}, but we notice that these peaks occur in similar time frames in each of these years, including 2019 when the pandemic was not in full force. Other work suggests that cyberbullying may have been disrupted by COVID-19, as the log search intensity on Google of cyberbullying and bullying terms decreases in 2021 \citep{bacher2022covid}. Our work shows that the first study's findings may have been a result of lack of data and suggests that hateful content may have decreased overall, agreeing with the second study. One must take these correspondences with a grain of salt, due to the important differences between social media and search engine data \citep{li2021comparing}.

While the mean trend may have remained roughly similar, there are slight differences across the years. The secondary peak during the latter half of the year grows increasingly wide every year, which may be a consequence of virus proliferation in winter months and lifting of non-pharmaceutical interventions like lockdowns and mask mandates \citep{mcclymont2021weather, singh2021impacts}. However, these findings may be a consequence of more active Twitter users, rather than a true increase in abusive content.

Additionally, from the important seasonal lags revealed in the model fit's AR coefficients, the seasonal effect is likely the most significant factor in determining the quantity of potential cyberbullying events. The effect of COVID-19 is not the clearest, but it is observed that the mean trend is suddenly grows with the introduction of the pandemic but then cuts down significantly a year into it. This may be due to the raw number of potential hateful tweets decreasing as shown in figure \ref{fig:CountEndpointData}, while the proportion of hateful tweets out of a given day's sample decreases across the same time frame (figure \ref{fig:PropTrend25/50}).

\subsection{Limitations and Suggestions for Future Research}

It must be stressed that data of this nature cannot imply causal relationships between COVID-19 case counts and cyberbullying trends. Consider the finding of decreased hate speech quantity as well as proportion over the course of recent years. For this, one may have to address several sources of confounding, such as increase internet usage precipitated by lockdown measures \citep{candela2020impact}.

There are many limitations to this study. The study's immediate results can only be used to infer about the state of cyberbullying events in the United States among English-speaking Twitter users due to how the data was collected. One also cannot be certain that the selection of keywords produces a good sample to pull out potentially hateful tweets from. While pulling geo-tagged tweets helps in collecting a more thorough sample \citep{morstatter2013sample}, Twitter's sampling algorithm remains unknown and produces unique issues for time series analysis, such as whether it maintains a fixed sampling rate to maintain structural relationships in the count series. Additionally, the NLP model used \citep{barbieri2020tweeteval} is not necessarily state-of-the-art and was used for its availability and ample documentation, rather than seeking the best performing algorithm known. Thus, the model's predictions on what it considers offensive or hateful may not necessarily be correct. The hate speech this model is also concerned about is primarily against women and immigrants as opposed to a broader scope of hate speech, like including racial motivations.

Due to the importance of the weekly and yearly seasonality in the studied time series, employing a seasonal model in future work would better reflect the dynamics of the data. One may also consider integrating spatial methods as well, where the data set is broken down into several regions where the analysis is performed independently, like the work done by \cite{babvey2021using}. This spatial analysis can be augmented with a similar daily count time series for a continuous analysis across several regions. It is also possible to use methods of Vector Auto-regression (VAR) to model several time series simultaneously, such as COVID-19 cases, abusive tweets, and search engine data. Studies using VAR methods show the possibility of predicting suicides using search engine data \citep{taira2021predicting} and COVID-19 cases with a great variety of variables \citep{wang2021vector}.

\section{Data Availability Statement}
The search endpoint data with modeled probabilities, count endpoint data, and the query used for the API requests are made available at Harvard Dataverse~\footnote{\url{https://dataverse.harvard.edu/privateurl.xhtml?token=8b866f9e-3a0c-4897-a839-b0fe6a0c2fc8}}.

\section{Acknowledgements}
We thank the Twitter team for granting us an academic research license to carry out this study. The second author's research is supported by NSF DMS 2124222.

\bibliographystyle{apalike}

redpajama_set_name: RedPajamaArXiv


What Car Battery Size Is Best For My Mercedes-Benz GLE350?
Cars (and their batteries) may be created equal, but they certainly don't stay that way. Choose Firestone Complete Auto Care for the most durable Mercedes-Benz GLE350 battery, plus quick and courteous battery testing and replacement. We only sell and install the top automotive battery brand in the U.S., Interstate Batteries. That means when you visit the experienced technicians at your neighborhood Firestone Complete Auto Care you're getting the right GLE350 car battery and at a super price. Choose your GLE350's model below for help figuring out the car battery size that matches your vehicle. Schedule an appointment for a quick car battery replacement today! For your convenience, your neighborhood Firestone Complete Auto Care is open late and on weekends.


text:
AboutAdvertise With Us
Top Markets
Real-Estate Frenzy Overwhelms Small-Town America: 'I Came Home Crying'
Buyers far from big cities lose out to investors and deep-pocket rivals in places where properties until a year ago offered affordable entry to the middle class
By Candace Taylor | Photographs by Michelle Gustafson for The Wall Street Journal
| Originally Published On May 20, 2021 | The Wall Street Journal
House hunters waiting their turn at an open house this month in Northampton, Pa., north of Allentown.
Dominic Pollock, still in his work boots, stood on the lawn of a 1960s-era three-bedroom house for sale in the former steel town of Bethlehem, Pa., 60 miles north of Philadelphia. It was listed at $250,000.
"I really, really like it," Mr. Pollock told his real-estate agent Danny Hazim, a buddy from high school in neighboring Allentown, Pa. Groups of other interested buyers huddled nearby and whispered to their agents in urgent tones, casting sly glances at rivals.
Mr. Pollock, 25 years old, was willing to go above the asking price. He and his fiancée, Brooke Terplan, 26, had made more than 20 offers on houses over nine months. Each time, they were outbid.
The couple had hoped to land a home by their wedding this week and begin a life together. Mr. Pollock lived with his brother, and Ms. Terplan, a labor-and-delivery nurse, lived with her parents.
Like many would-be buyers, they braced for disappointment.
More:Buying in Phoenix? Don't Count on the Flood of New Developments Dampening Prices
Home prices in the U.S. have shot up in the past year, driven by limited supply, record-low interest rates and buyer demand. Bidding wars have spread from such high-profile locations as Palm Beach, Fla., and the suburbs outside New York City to smaller cities and towns, including long-neglected locales where properties typically sat on the market for months.
Brooke Terplan and her fiancé, Dominic Pollock, at an open house in Whitehall, Pa.
Local buyers bid against one another as well as against investors who now comprise about a fifth of annual home sales nationally. Online platforms such as BiggerPockets and Fundrise make it easier for out-of-town investors to buy real estate in smaller cities across the U.S., said John Burns of California-based John Burns Real Estate Consulting.
Often, Mr. Burns said, "the cash flows are better in the Tulsas and Allentowns of the world" for those seeking to rent out properties. In the fourth quarter of 2020, nearly a fifth of homes sold in the Allentown area were bought by investors, according to Mr. Burns's data.
The median listed price for a house jumped 24% in January from a year earlier in the metropolitan area surrounding Allentown, the Rust-Belt city whose decline was memorialized in a 1982 Billy Joel song, according to data from Realtor.com. It was the same in such spots as Martin, Tenn., a small city 150 miles from Nashville, where the median asking price went up 159% over the same period; in Kendallville, Ind., about 30 miles outside Fort Wayne, it climbed 56%.
The average price for a house in the Allentown metro area, which includes Bethlehem, was about $225,000 a year ago, Mr. Campbell said. It has since shot past $270,000 in a market so hot that open houses trigger traffic jams, and properties sell in 48 hours.
More:A Weak Dollar Will Help Foreigners Snatch up U.S. Property More Cheaply—But There Is More to a Good Deal Than Currency
Many homeowners want to sell while prices are high but hesitate for fear they won't find an affordable place to move. Housing supplies can't meet demand.
In Bethlehem's 18018 ZIP Code, the average monthly number of homes for sale has fallen 65% in the past year to 25 from 2017-2019 levels, according to Realtor.com. (News Corp, owner of The Wall Street Journal, operates Realtor.com under license from the National Association of Realtors.)
Buyers feel pressure to make snap decisions, and some forego routine home inspections for fear of losing to another bidder. "If you're a buyer, this is the most frustrating time," said Jonathan Campbell, vice president of DLP Realty in Bethlehem. The local market, he said, is outpacing the mid-2000s housing boom.
With the exception of a few urban markets, including Manhattan and San Francisco, the U.S. is seeing "a chronic shortage of inventory, heavy sales volume and prices rising at levels wildly ahead of income growth," said Jonathan Miller of New York-based Miller Samuel Real Estate Appraisers and Consultants. The Case-Shiller U.S. National Home Price NSA Index reported a 12% annual gain in February, a figure seen only a few times in the history of the index, said Craig Lazzara, a managing director at S&P DJI.
The ranch-style house where Mr. Pollock stood with Mr. Hazim on Kensington Road had been listed a day and already had several offers. It was 1,900 square-feet with a full bathroom and two half baths. The sellers would only take bids until 11 p.m. the following day.
More:These Luxury Markets Are Set to Get a Post-Vaccine Boost
Mr. Pollock, who installs fire sprinklers, agreed to submit an offer of $270,500, adding $20,500 to the asking price. To sweeten the deal further, he waived his right to an inspection of the plumbing, roof, foundation or any other part of the house.
"We will know by tomorrow," his agent, Mr. Hazim, told him.
Old blast furnaces at the long-defunct Bethlehem Steel plant rise over the Lehigh River, 90 miles west of New York City. The city of Bethlehem and neighboring Allentown were major industrial centers until a wave of factories moved overseas in the 1980s and 1990s.
In the center of Allentown, population 121,000, Victorian row houses once occupied by middle-class families were divided into low-cost apartment rentals. The city's downtown has recently been redeveloped, but crime remains a common complaint, said Mr. Hazim, 25, who grew up there.
Healthcare and universities are now major employers in the region, which is known as the Lehigh Valley. Much of the area is still farmed. The road a few minutes drive outside of downtown Allentown is flanked by cornfields, and the air smells of manure.
From left, Dominic Pollock, Brooke Terplan and real-estate agent Danny Hazim about to tour a house in Bethlehem, Pa.
Mr. Pollock and Ms. Terplan grew up a few houses apart in an Allentown subdivision called Midway Manor. They started dating when they were 21. It was on New Year's Eve 2016, at the pub where Mr. Pollock where still tends bar on weekends. Someone asked if he was going to kiss Ms. Terplan at midnight, and he did.
The couple started looking for a house around the time they got engaged in August. They needed a place with room to start a family. Ms. Terplan said she wants to get pregnant as soon as they marry. "She really wants the American dream, the white picket fence, the two dogs out back," Mr. Pollock said.
They had been saving for years and planned on making a 10% down payment when they started looking. "A couple people told us that it was going to be tough," Mr. Pollock said, "but we couldn't even fathom what we'd walk into."
Before the pandemic, home prices in the Lehigh Valley began at around $15,000 for a mobile home and went to $400,000 for a two-story house in a newer subdivision. Historic homes in the West End of Allentown sold for as much as $450,000. For years, buyers had their pick, Mr. Hazim said.
From Penta:A 1933 Babe Ruth Baseball Card Could Fetch a Record US$5.2 Million at Auction
After the first round of pandemic lockdowns eased, real-estate offices reopened last June, Mr. Hazim said, and agents noticed a shift in the market right away.
First, there were fewer home listings than normal because of Covid-19 fears. "No one wants to list their house because no one wants random people coming in," he said of the scariest period. "You never know who has it."
Supply also was down because the government's mortgage forbearance program sharply reduced home foreclosure sales. At the same time, Mr. Hazim said, demand for homes ballooned.
A Lehigh Valley Health Network facility in Allentown, Pa.
Investors are attracted to the Lehigh Valley, Mr. Hazim said, because home prices and taxes are low relative to market-level rents. He recently worked with a New York City-based investor who bought a single-family house in the town of Northampton, about 20 minutes from Allentown.
The New Yorker paid $15,000 over the asking price—all cash, with no inspections—and planned to rent it out. The buyer, Mr. Hazim said, still marveling, took it sight unseen.
'Shoot me now'
Mr. Pollock and Ms. Terplan first started looking in Bethlehem, close to their parents, and in nearby Easton, where Mr. Pollock lived with his brother. When one offer after another was rejected, they started looking farther afield and lifted their budget ceiling to $300,000 from $250,000.
Nearly once an hour on Ms. Terplan's days off, she checked Zillow or Realtor.com for property listings, wondering, she said, "Are there any new ones?"
Their families were baffled at first. Ms. Terplan's father "was getting so aggravated with us, like, 'Hey, you're doing something wrong!' " Mr. Pollock recalled. His future father-in-law couldn't comprehend the market frenzy until they showed him the paperwork for one of the offers they had made above the asking price, Mr. Pollock said.
Mr. Hazim trudged alongside them. He showed the couple about 50 houses and shared their disappointment after rejected offers. He attended Dieruff High School in Allentown with Mr. Pollock and years later joined DLP Realty.
Mr. Hazim, 25, said he was working 12-hour days, seven days a week, and closing maybe five deals a month, about half of what he would expect while serving such a large pool of clients. "With so many hours required to close," he said, "it's going to turn out to be minimum wage."
On a recent Wednesday, he took a two-hour drive to show his client Lisa Hanna a couple of listings she had found online. Both houses needed major repairs, and Ms. Hanna spent only minutes inside each of them.
More:This Friday, attend a Barron's Live discussion on forces currently shaping the prime global real estate market
"Shoot me now," Ms. Hanna said, after exiting one in rural Ruscombmanor Township, Pa. "The pictures of this house are nothing like what the house is."
Ms. Hanna, 48, who works for an insurance company in Bethlehem, was looking for a three-bedroom house with enough land to start breeding Great Danes. In two weeks of house-hunting, she saw 10 to 15 properties and made three failed offers. "I'm getting very discouraged," she said.
She bought her current home six years ago, paying $116,000 for a new three-bedroom. It took her just a day to find it. Now, she worried she couldn't find any house to move into. "It's nerve-racking," she said.
Many sellers have the same concern. "These people can't list because they have nowhere to go," Mr. Hazim said. He listed Ms. Hanna's house for $189,000, and it sold within days in an all-cash purchase just under the asking price.
Danny Hazim, a real-estate agent at DLP Realty.
She still hadn't found another house, but the buyers agreed to give her 45 days to keep looking. After a month with no luck, "I was freaking out," Ms. Hanna said.
Afraid she would lose her buyers, Ms. Hanna put in an offer on a two-bedroom house—smaller than she had hoped—in the Poconos, and it was accepted. The two transactions are slated to close on the same day in June.
For Mr. Pollock and Ms. Terplan, the rejections have made for an emotional roller coaster. "It's tough," said Mr. Pollock, especially for his fiancée, who falls in love with each house they bid for.
"There were days that I came home crying," Ms. Terplan said.
More:If Time—or Control—Is of the Essence, U.K. Homeowners Should Consider an Auction
When they were first engaged, Ms. Terplan said, she didn't imagine they would still be looking for a house by the wedding. A few weeks ago, she said, "I had the realization that we're probably not going to have a house before we're married. I had a little bit of a breakdown."
Rents in the area have gotten as high as mortgage payments, but it may come to that, the couple said.
After hours of waiting for a response to their bid on the Kensington Road house, Mr. Hazim got an email from the listing agent.
"Thank you for your offer," it said. "We had 14 offers, and my sellers chose one that worked the best for them. Best of luck to your buyers."
Dominic Pollock and his fiancée, Brooke Terplan, at an open house in Northampton, Pa.
Delivers the most important property news around the world to your inbox each weekday
U.S. PRICE TRENDS NA
Read Next Story
Follow Mansion global
About Mansion GlobalAdvertise With UsSign up to Newsletters
All IndexHomes for SaleHomes for RentNews TopicsThe Library
Privacy Notice Cookie Notice Your Ad Choices Copyright Policy Data Policy Terms of Use
COPYRIGHT © 2022 MANSION GLOBAL. ALL RIGHTS RESERVED.1211 AVE OF THE AMERICAS NEW YORK, NY 10036 | info@mansionglobal.com
DISCLAIMER: The currency conversion is provided for illustration purposes only. It is meant only as an approximation based on the latest information available and should not be relied upon for any other purposes. We are not responsible for any loss that you may incur as a result of relying on these currency conversions. All property prices are as stated by the listing agent.


One in three people in the world are overweight. What used to be a condition associated with high-income countries is now global.
In low- and middle-income countries the number of overweight and obese adults almost quadrupled between 1980 and 2008. Much of this rise occurred in countries where incomes were also rising, such as Egypt and Mexico, but it was also seen in sub-Saharan Africa. Hunger is still rife in this region, but the percentage of overweight adults doubled from 12 per cent in 1980 to 24 per cent in 2008.
Nutrition is a question of quality as much as quantity. Underlying deficiencies in vitamins and minerals can be masked by weight gain, but afflict both the underfed and the overfed. Two of the most common are iron-deficiency anaemia and vitamin A deficiency, which continues to cause widespread blindness in children under five years old.
Obesity in low- and middle-income countries is a result of a series of changes in diet, physical activity, health and nutrition, collectively known as the 'nutrition transition'. For a start, increasing importation of foods from the industrialised world means that traditional diets featuring grains and vegetables are giving way to meals high in fat and sugar.
Urban areas have higher rates of obesity because cities offer a greater range of food choices, generally at lower prices. And as more and more women work away from home, they may be too busy to shop for, prepare and cook healthy meals at home.
In rural areas increased mechanisation on farms has reduced physical activity. Moreover, many farmers have given up subsistence farming of multiple crops that provide a more balanced diet in favour of cultivating a single, high-yielding cash crop.
In many countries being overweight has reversed its cultural meaning. In Mexico and Brazil being overweight used to be a sign of wealth, but now that the poor have access to a diet high in fat and sugar it often signals poverty. The elite, who have better access to nutrition education, healthy foods and exercise facilities, tend to be slimmer.
But cultural context is important: in South Africa it has been suggested that overweight people may be deterred from losing weight because thinness is associated with HIV/AIDS, a disease with a high degree of social stigma attached to it.
This resource was first published in 'Obesity' in January 2005 and reviewed and updated in June 2014.


Private Owned Vacation Villas and Apartments for Rental in Catalunya. Late Deals, Special Offers.
Intraspain can offer you tens off different apartments in Barcelona. These apartments are suitable for 2 to 6 persons.
Barcelona by the mediterranean sea.
New furnished apartment in front of the sea.
3 bed rooms: two double (one en suite) and one triple.
This flat is 50m2. with one bedroom, a spacious lounge area with sofa bed, bathroom and kitchen. The kitchen-dining area is fully equiped with vitroceramic hob, oven and fridge.
Sheets and towels for four people are provided.
Barcelona, 32 apartments to rent in the city.
Situated in the Catalan Pyrennees. Bungalows to hire.
Spacious Beautiful apartment with lovely swimming pool.
Costa Brava: accommodations to rent!
The pyrenees in the Costa Daurada. Mystic place.


text:
KidneyIntelX
Payors
Payor Resources
Order Test Kit
Atrium Health, Wake Forest Baptist Health, Wake Forest School of Medicine and Renalytix Partner to Advance Kidney Health
Partnership marks a long-term commitment to reducing impact of kidney disease in large populations via integration of earlier risk assessment paired with comprehensive disease management
NEW YORK, CHARLOTTE AND WINSTON-SALEM, N.C., May 19, 2021 — Renalytix AI plc (LSE: RENX) (NASDAQ: RNLX), Atrium Health, Wake Forest Baptist Health and Wake Forest School of Medicine announced a partnership to implement an advanced clinical care model designed to improve kidney health and reduce kidney disease progression and kidney failure in high-risk populations.
The KidneyIntelX bioprognostic platform for early-stage diabetic kidney disease risk assessment will be available through Atrium Health's electronic health record (EHR) system, providing access to primary care physicians, endocrinologists, nephrologists and care teams throughout 37 hospitals and more than 1,350 care locations across the Carolinas and Georgia. Charlotte, North Carolina-based Atrium Health serves more than 7 million people in the region, providing care under the Wake Forest Baptist Health name in the Winston-Salem, N.C. region and Atrium Health Navicent in Georgia.
The partnership with Renalytix enables focused population health initiatives in kidney disease and will provide real-world evidence of optimized delivery of care, improved quality of life, and reduced healthcare costs.
Renalytix will co-locate personnel in Winston-Salem to support the partnership program with Wake Forest School of Medicine at the Innovation Quarter's iQ Healthtech Labs, which is the core industry partnership hub of the innovation district. Renalytix and Atrium Health believe the partnership is closely aligned with the core precision medicine and healthy aging sectors that are a focus of iQ Healthtech Labs.
The new program initially targets diabetes-associated kidney disease, a critical healthcare need in the region served by Atrium Health and designated by the Centers for Disease Control and Prevention as the "diabetes belt" and the "kidney disease belt." In this region in particular, but also nationally and internationally, costs and patient suffering related to diabetes and its associated complications are increasing at unsustainable rates.
"Detecting patients at risk for rapid progression of kidney disease at early, treatable stages is challenging. This effort is critical to optimizing care and improving outcomes using kidney protective medications, specialist referrals and lifestyle changes," said Barry I. Freedman, M.D., John Felts Professor and Chief of Nephrology at Wake Forest School of Medicine and Chief Medical Officer of Health Systems Management, Inc., the operator of Wake Forest Baptist's outpatient dialysis clinics. "KidneyIntelX can help us better allocate clinical resources across Atrium Health's southeastern service region. Patients with diabetic kidney disease at high risk for progression to the need for dialysis or a kidney transplant can be detected earlier and receive intensive blood pressure, blood sugar, cholesterol and lifestyle management and referral to specialists. We expect the outcomes to be very positive for our patients and for our health system."
"The partnership with Renalytix is a significant milestone in carrying out our commitment to continually innovate to improve population health in our region," said Terry G. Williams, Chief Population, Corporate and Government Affairs Officer of Atrium Health and executive leader in iQ Healthtech Labs. "Initiatives focused on the generation of unique data-driven insights to improve patient outcomes enable us to deliver effective education and care pathways to improve care and outcomes. Together, we are innovating to achieve clinical excellence and are laying the foundation for patient and physician access to advanced prognostic precision medicine in a major chronic disease."
"Our commitment to advancing kidney health with KidneyIntelX is strategically aligned with Atrium Health and Wake Forest Baptist Health's goal to advance care delivery using innovative precision medicine guided care," said James McCullough, Chief Executive Officer, RenalytixAI. "Renalytix values this opportunity to demonstrate clear, informative evidence of the value of a comprehensive care solution in a region of the United States particularly challenged by diabetic kidney disease. That evidence is important for clinicians, patients, regulators and payers."
KidneyIntelX is a novel bioprognostic solution that interprets the combination of laboratory results from a simple blood draw and selected electronic health record (EHR) information using a machine learning algorithm. Unlike other solutions available today, the KidneyIntelX risk score assesses a patient's near-term risk for progressive kidney function decline and failure in the earliest stages of the disease when steps can be taken to preserve kidney health.
The partnership also powers a five-year, multi-center evidence development program across the region, in which patients with chronic kidney disease will receive the KidneyIntelX test as part of a comprehensive care solution to drive quantifiable improvements in patient outcomes. KidneyIntelX will also be made broadly available to patients with both private and government insurance, including Medicare beneficiaries under the Medicare Coverage of Innovative Technology expected to be fully implemented in December 2021. RenalytixAI will be providing financial support to Atrium Health in the form of collaborative research and development, digital health technology deployment, education program delivery and third-party resources in each of these critical areas.
About Kidney Disease
Kidney disease is now recognized as a public health epidemic affecting over 850 million people globally. The Centers for Disease Control and Prevention (CDC) estimates that 15% of US adults, or 37 million people, currently have chronic kidney disease (CKD). Further, the CDC reports that 9 out of 10 adults with CKD do not know they have it and one out of two people with very low kidney function who are not on dialysis do not know they have CKD.2 Kidney disease is referred to as a "silent killer" because it often has no symptoms and can go undetected until a very advanced stage. Each year kidney disease kills more people than breast and prostate cancer. Every day, 13 patients in the United States die while waiting for a kidney transplant.
About KidneyIntelX
KidneyIntelX, is a first-in-class in vitro diagnostics platform, that employs a proprietary artificial intelligence-enabled algorithm to combine diverse data inputs, including validated blood-based biomarkers, inherited genetics, and personalized patient data from electronic health record, or EHR, systems, to generate a unique patient risk score. This patient risk score enables prediction of progressive kidney function decline in chronic kidney disease, or CKD, allowing physicians and healthcare systems to optimize the allocation of treatments and clinical resources to patients at highest risk.
About RenalytixAI
RenalytixAI (LSE: RENX) (NASDAQ: RNLX) is a developer of artificial intelligence-enabled clinical in vitro diagnostic solutions for kidney disease, one of the most common and costly chronic medical conditions globally. The Company's lead product is KidneyIntelX, which has been granted Breakthrough Designation by the U.S. Food and Drug Administration and which is being designed to help make significant improvements in kidney disease prognosis, transplant management, clinical care, patient stratification for drug clinical trials, and drug target discovery (visit www.kidneyintelx.com). For more information, visit www.renalytixai.com
About Atrium Health
Atrium Health is a nationally recognized leader in shaping health outcomes through innovative research, education and compassionate patient care. Atrium Health is an integrated, nonprofit health system with nearly 70,000 teammates serving patients at 37 hospitals and more than 1,350 care locations. It provides care under the Wake Forest Baptist Health name in the Winston-Salem, North Carolina, region and Atrium Health Navicent in Georgia. Atrium Health is renowned for its top-ranked pediatric, cancer and heart care, as well as organ transplants, burn treatments and specialized musculoskeletal programs. A recognized leader in experiential medical education and groundbreaking research, Wake Forest School of Medicine is the academic core of the enterprise, including Wake Forest Innovations, which is advancing new medical technologies and biomedical discoveries. Atrium Health is also a leading-edge innovator in virtual care and mobile medicine, providing care close to home and in the home. Ranked among U.S. News & World Report's Best Hospitals for cancer treatment and in eight pediatric specialties, Atrium Health has also received the American Hospital Association's Quest for Quality Prize and was the recipient of the 2020 Centers for Medicare & Medicaid Services Health Equity Award for its efforts to reduce racial and ethnic disparities in care. With a commitment to every community it serves, Atrium Health seeks to improve health, elevate hope and advance healing – for all, providing more than $2 billion per year in free and uncompensated care and other community benefits.
1 United States Renal Data System
2 https://www.cdc.gov/kidneydisease/publications-resources/2019-national-facts.html
Statements contained in this press release regarding matters that are not historical facts are "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995, as amended. Examples of these forward-looking statements include statements concerning: the potential benefits of the partnership, the potential for KidneyIntelX to receive regulatory approval from the FDA, the commercial prospects of KidneyIntelX, if approved, including whether KidneyIntelX will be successfully distributed and marketed, our expectations regarding reimbursement decisions and the ability of KidneyIntelX to curtail costs of chronic and end-stage kidney disease, optimize care delivery and improve patient outcomes. Words such as "anticipates," "believes," "estimates," "expects," "intends," "plans," "seeks," and similar expressions are intended to identify forward-looking statements. We may not actually achieve the plans and objectives disclosed in the forward-looking statements, and you should not place undue reliance on our forward-looking statements. Any forward-looking statements are based on management's current views and assumptions and involve risks and uncertainties that could cause actual results, performance or events to differ materially from those expressed or implied in such statements. These risks and uncertainties include, among others: that KidneyIntelX is based on novel artificial intelligence technologies that are rapidly evolving and potential acceptance, utility and clinical practice remains uncertain; we have only recently commercially launched KidneyIntelX; and risks relating to the impact on our business of the COVID-19 pandemic or similar public health crises. These and other risks are described more fully in our filings with the Securities and Exchange Commission (SEC), including the "Risk Factors" section of our annual report on Form 20-F filed with the SEC on October 28, 2020, and other filings we make with the SEC from time to time. All information in this press release is as of the date of the release, and we undertake no obligation to publicly update any forward-looking statement, whether as a result of new information, future events, or otherwise, except as required by law.
© 2022 Renalytix, Inc.


Being an actor simply might be among the coolest tasks in the world. Yet on the other hand, it is also one of the most affordable. No matter whether you're a novice, or an experienced expert star, you require to have a great star resume if you are going to beat out the competition.
Having an actor resume template can go a lengthy means towards obtaining you that attention-getting resume.
1. Something to remember when you're seeking a star resume theme, is that star returns to tend to be very various from standard resumes. In fact, a number of the products you will certainly place on a star resume you would certainly never put on a typical resume. For example you could include your height, weight, eye color, and also hair color.
While it would be improper to consist of such info on a typical resume, it is essential details for a star resume layout, due to the fact that such details is important when casting for a particular duty.
2. Like every other resume, nonetheless, your acting resume ought to include a listing of information about your past acting experience. Noting your acting work experience in a sequential fashion is an excellent way to present the details.
3. In addition to your acting experience, your star resume design template will call for that you provide any type of special achievements, or words that you have one. Listing your accomplishments as well as awards will make a substantial difference in helping you land a function that you desire.
4. A terrific way to list all of your experience and also achievements on your resume is with bullet points. Bullet indicate attract a readers guide to one of the most important elements of your resume and also can truly aid your resume to stand out.
5. Naturally, much like in a standard resume, you must provide all of your training as well as education. If you have a training in acting if you truly help to distinguish you from your rivals. Definitely consist of any type of unique training you have actually needed to help with your acting. This is particularly true if you have actually bought acting lessons or training.
6. For most people, your acting resume need to never exceed one-page in size. The only exception to this policy would be if you have comprehensive experience or training. Nevertheless, also if this is the case, you could consider ending older or bit parts in order to make other information extra prominent.
The fundamental is this, much like every resume, a star resume needs to offer your capability to a potential employer. With that said in mind, place your best foot onward in your star resume.


text:
La Coppa di Moldavia (in rumeno Cupa Moldovei) è la seconda competizione calcistica per importanza del campionato moldavo di calcio dopo la Super Liga.

Organizzata dalla Federaţia Moldovenească de Fotbal, ha carattere ufficiale dal 1992, anno dell'indipendenza della Repubblica di Moldavia dall'URSS, mentre dal 1945 al 1992 aveva carattere regionale. Dal 1945 si è sempre svolta, tranne che nel 1981.

La squadra più titolata dal 1992 è lo Sheriff Tiraspol.

Albo d'oro
Si riporta unicamente il risultato della finale.

Dall'indipendenza (1992-)

Vittorie per squadra (dal 1992)

Voci correlate
 Campionato moldavo di calcio
 Supercupa Moldovei
 Kubok SSSR

Collegamenti esterni

redpajama_set_name: RedPajamaWikipedia


Over 200 mobile startups applied to participate in the MobileBeat 2013 innovation showdown and just two emerged as winners.
Helpshift won the early stage category. Helpshift is a mobile help desk that powers customer service for hundreds of mobile applications. It has built a native mobile SDK that developers can download and code into mobile applications. It connects mobile customers with companies' support staff to provide a "native support experience." People on their phones can file a customer service ticket without leaving the app, so they don't have to visit a website, place a phone call, or send an e-mail to get help. Representatives can create custom alerts and view information about the customers' mobile devices, account information, debug logs, and transaction history to understand (and respond) to each ticket more specifically. The goal is to boost engagement and sales while reducing customer churn, support ticket volume, overhead costs, and bad reviews. Read more on VentureBeat.
The winner of the mid-stage competition was Capriza. Capriza extends web-based business applications to mobile devices with no coding, no integrations, and no APIs. Despite the growth of mobile enterprise apps, there is still a huge portion of business applications not optimized for mobile phones. Capriza allows employees to quickly pick-and-choose the elements they need from desktop workflows and create an HTML5 app that is easy to use and performs well. In 2011, it was reported that Capriza was raising $10.5 million from Andreessen Horowitz, but SEC filings show less than $2 million raised.
Helpshift and Capriza won 5,000 American Airlines Business ExtrAA points, a strategic bootcamp session with an executive from the IBm Venture Capital Group, a one-hour coaching session with a partner at Emergence Capital Partners, and an editorial feature on VentureBeat.
The competition was tough, and the other eight companies (below) also seemed to impress the judges with their presentations. The panel consisted of director of Silicon Valley Bank Jason Mok; Sean Jacobsohn of Emergence Capital Partners; Deborah Magid, Director of software strategy at IBM Venture Capital Group; partner at Lightspeed Ventures Jeremy Liew; and Sequoia Partner Tim Lee.
Lightt is a mobile app for capturing moments in your everyday life and turning them into videos that you actually watch. Using Lightt, users can take videos and can edit, trim and filter them on the go. The videos are easily saved and shared, and can be searched through later for the pieces you want to watch.
Lettuce Apps is an online inventory- and order-management system that enables businesses to capture, track, and process orders from anywhere in the world in real time. It is designed for small wholesalers and e-commerce sites. The system helps its clients stay organized and manage all their orders. Using its mobile app, sales reps can fill orders in seconds and all the data is automatically synced online, as well as automated packing, shipping, invoicing and more. Founder Raad Mobrem said that the order process involves multiple back office systems that do not talk to each other, and Lettuce pulls them all together.
The company launched last year and announced a $2.1 million seed round.
Instabug is a tool that runs in the background of an app, enabling users to send their feedback or bugs instantly by just shaking their devices, without interrupting their experience. Instabug runs in the background, gathering all of the details needed for the developer to trace those bugs later. The platform features a dashboard with all relevant information, like operating system, device, version, data, carrier etc… It also integrates with existing bug tracking and/or project management software. The founders are Egyptian and won the MIT Arab Enterprise forum, which had 4000 participants.
People Plus is a rolodex for Google Glass. CEO Peter Berger said the company has "reimagined the contact list for wearable computing." People wearing glass can survey a room and see potential contacts with contextual information about who they are. For example, an entrepreneur at a tech event can get help from People Plus with identifying VCs in the room, or pinpointing people that they should meet. The goal is to create a "zen-like" experience on Glass that helps you get the most out of networking opportunities, and connect with the right people.
MobileSpaces takes yet another approach to BYOD by placing a strong emphasis on employee privacy. Its software lets phone owners mark all applications they use for work and only those apps can access business data and systems. This is convenient for the consumers because they get to pick their own apps, as well as for the IT teams that are concerned with securing corporate data. MobileSpaces has raised $3 million to date from Accel Partners.
Followapps is mobile application management platform is designed specifically to help chief marketing officers measure the return on investment on their mobile marketing spend and engage their mobile audience. It links a consumer's mobile app activity with a company's CRM to measure relevant KPIs and activate mobile audiences.
Open Garden's service wants to make it possible to crowd-source mobile and Wi-Fi bandwidth. Basically, if people voluntarily pool their bandwidth together, there's a much better chance of never being without access to data again. By joining the Open Garden mesh network, you get faster Internet access, stronger coverage, and free mobile broadband.
NewAer is built as an SDK for Android, iPhone, OS X, and Windows, is a software-based proximity platform that works inside of other applications to make them environmentally aware. The platform for machine-to-machine discovery enables any smartphone application or API to be automated from the presence of radio signals received on a device.


text:
BuroHappold awarded first WELL certification in Poland
The International WELL Building Institute™ (IWBI™) has awarded our Warsaw office WELL Certification at the Gold level.
This prestigious honour, the first to be awarded to any project in Poland, was given to the building for meeting the IWBI's WELL Building Standard™. The standard focuses on improving the health and wellness of people in the buildings where we live, work and play.
Taking an innovative approach, the office design focused on ensuring the health and wellbeing of our employees. Subsequently, the space has become a 'living lab' where cutting-edge office design ideas can be tested.
BuroHappold has applied innovative technical solutions in several areas including:
Visual comfort – the new healthy office features dimmable LED lighting with pre-programmed light level for every workstation
Clean air – BuroHappold has ensured generous volumes of fresh air supply based on CO2concentration
Optimised temperature – a highly efficient system allows for thermal comfort with full control of temperature in 30 zones.
Design for wellbeing – the office is an enjoyable space which uses natural colours and materials
Extensive monitoring of water and energy use – aiming at reduced levels of consumption
Re-use of a significant part of the existing interior features – resulting in a positive impact on the budget and the environment.
The lighting and acoustic performance of the office space were key to the award of this certification. The office exceeds European best practice in terms of occupant satisfaction. According to Comfortmeter methodology, 66% of employees are happy about sound levels (compared to a best practice benchmark of 59%), and 85% of employees are satisfied with the lighting solutions (exceeding the European best practice benchmark of 78%).
We are delighted to have achieved WELL Certified™ Gold for our Warsaw office as recognition of our focus on the health and wellness of our own employees.
Ian Booth, Partner
BuroHappold receiving the certification: (l-r) Hauke Jungjohann, James Bruce, Magdalena Markowska, Zbigniew Czajewski, Jan Cieśla, Ann-Marie Aguilar, Ian Booth, Katarzyna Chwalbińska-Kusek, Robert Winterhager
Warsaw office director and partner Ian Booth, commented: "BuroHappold provides solutions that are end-user oriented. Our new office is proudly being used as a showcase for health and wellbeing in an open-plan office environment. The survey results also demonstrate that people feel good in our space, proof that the project has been successful. We are delighted to have achieved the WELL Certified™ Gold certification in recognition of our focus on human health and wellness."
The certification is based on seven categories of building performance— Air, Water, Light, Nourishment, Fitness, Comfort and Mind. Created over seven years of rigorous research and development working with leading physicians, scientists, and industry professionals, the WELL Building Standard™ is a performance-based certification system that marries best practices in design and construction with evidence-based scientific research.
"We congratulate BuroHappold for achieving the first WELL certification in Poland," said Rick Fedrizzi, Chairman and CEO of the International WELL Building Institute™. "BuroHappold has demonstrated outstanding leadership, supporting healthier people through better buildings and helping to advance this movement in Poland and globally."
WELL is grounded in a body of evidence-based research that explores the connection between the buildings where we spend approximately 90 percent of our time, and the health and wellness impacts these buildings have on us. To be awarded this certification, the Warsaw office underwent rigorous testing. A final evaluation was carried out by Green Business Certification Inc. (GBCI), the third-party certification body for WELL, to ensure the office met all WELL Certified™ Gold performance requirements.
Reaching net zero
Engineering new build offices with sustainability first and foremost
BuroHappold is growing in Pittsburgh
Leeds office relocates to sustainable urban quarter
BuroHappold Expands Presence in Chicago
Claire Smith appointed Bath Office Director
Our Los Angeles office achieves WELL Certified™ Gold
An exciting time to be a bridge engineer
BuroHappold at MIPIM 2019
Projects to look out for in 2019: Part II
Partner : Ian Booth


Elko area. - Highly recommended!
Recently, manufactured steel and aluminum have also been used. The wall may have places to attach belay ropes, but may also be used to practice lead climbing or bouldering. Thank you.
Want to know how to win the biggest bonus at casino? Visit the best casino bonus guide right here! Learn all you need right now!