text:
\section{Introduction}

Social media have become a crucial communication channel during mass political or civic events by shaping "a civic and democratic discourse in a vacuum of opportunities" \cite{howard2010digital}. As academic debates contest the nature of social media as an alternative public sphere \cite{papacharissi2009virtual}, it is important to study the \emph{interrelationships between mainstream media and social networks} in shaping public opinion during mass protests, especially in regards to the origin and dissemination of news frames \cite{oates2013twilight}. It is also of interest to consider how \emph{propaganda and manipulation} in the information sphere work: where partisan language and frames originate, how they spread, and whether there are certain markers that would allow to trace the distribution and paths of such content.

In this study, we analyze the role of social and mainstream media in shaping and disseminating partisan content frames during social unrest and crisis. Specifically, we focus on the mass protests in Ukraine during 2013-2014 - known as \emph{"Euromaidan"} - in which social media played a remarkable role, helping to raise awareness, cover, and discuss ongoing events; and the post-Euromaidan Russian occupation of Crimea and the conflict between Russian, pro-Russian and Ukrainian forces in eastern Ukraine (2014-2015), periods that were characterized by a parallel information and propaganda war occurring in mainstream media and online together with military action on the ground.

We explore the extent to which lingual choices in online discourse can illuminate the \emph{partisan confrontation} between political factions during mass crises through the analysis of the two complementary datasets of Twitter posts and news articles. More specifically, we exploit natural language processing to single out language that points to a particular political leaning and to observe whether these markers are detectable in both mainstream media language and social media posts at scale. Our contributions can be summarized as follows:

\begin{itemize}
	\item We exploit the word embedding approach~\cite{mikolov2013distributed,mikolov2013efficient} to identify the indicators of partisan slant in news articles and validate it over the text corpora of around 4M news articles collected during the Ukrainian conflict. Our analysis reveals a \emph{strong use of highly polarized partisan content frames} in news articles on both sides of the conflict.
	\item Next, we design a machine learning approach for \emph{detecting the markers of partisan rhetoric} in news articles with minimal efforts required for supervision. This is achieved by a "coarse-grained" labeling of the articles based on the partisan slant of the news agencies they originate from. Our approach - trained on a collection of articles from 15 representative news agencies -	is able to achieve $60$--77\% accuracy in distinguishing between the news articles with pro-Ukrainian, Russian pro-government and Russian independent slants during the Ukrainian conflict.
  \item Finally, we study the inter-relation between traditional and social media during conflicts through an analysis of individual news sharing patterns among Twitter users and find that most of the users are \emph{exposed to a variety of news sources but with a strong partisan focus}. Using our machine learning approach, we are also able to predict the partisan leaning of Twitter users from the content of the tweets with an accuracy up of 70\%.
\end{itemize}

In summary, we demonstrate that studying the lingual choices of Twitter users and news media adds a new dimension to understanding the dynamics of information flows and partisan idea dissemination in the space between social networks and mainstream media. We also demonstrate that lingual choices-based machine learning models can be highly effective at automatically predicting the political slant of mainstream media and Twitter users, which can have serious implications for political expression in repressive and authoritarian regimes.

\vspace{-1.1em}
\section{Related Literature}
\vspace{-1.1em}

Computational social scientists have given substantial attention to the mainstream and social media activity around political and social change, and to the role information shared on these platforms plays in influencing political and social agendas, protest movements and events, and public opinion. Researchers have explored the role of social media and mainstream media actors in information diffusion and protest message amplification in networks through the prism of the \emph{collective action theory} \cite{gonzalez2013broadcasters}, as well as the role of social networks in recruitment and mobilisation during protest, revealing connections between online networks, social contagion, and collective dynamics \cite{gonzalez2011dynamics}.

A broad swathe of quantitative studies have focused on determining the factors that influence political leanings of social network users and metrics that allow to classify and predict this kind of political bias. Several studies have considered the \emph{predictive power} of political news sharing habits on Twitter \cite{an2014sharing}, the influence of partisan information sharing on political bias among Facebook users \cite{an2014partisan} and Twitter actors \cite{hegelich2016social,conover2011predicting}, and compared differences and biases in news story coverage, dissemination, and consumption among online mainstream and social media \cite{chakraborty2016dissemination}. Others have noted the difficulty of connecting selective exposure to political news on Facebook to partisanship levels of users \cite{an2013fragmented}. At the same time, researchers suggest that analysis of information consumption and distribution habits of social network users does provide data on media exposure, the relationship between various classes of media, and the diversity of media content shared on social networks \cite{an2011media}.

Some studies have noted that reliably inferring the political orientation of Twitter users and generalizing the findings is notoriusly difficult due to it being one of the ``hidden'' attributes in social network data and due to differences between politically active groups of users and the general population \cite{cohen2013classifying}. However, other academics suggest that studying some relationships on social networks, like the co-subscriptions relationships inferred by Twitter links \cite{an2012visualizing}, allows for some understanding of the underlying media bias — and subsequently, political bias — of social network users. Another study showed that applying machine learning techniques to classify political leanings on Twitter based on political party messages can reveal partisanship among users \cite{boutet2013s}. A number of studies present a comparison between the predictive power of the users' social connections and their content sharing patterns for inferring political affiliation, ethnicity identification and detecting affinity for a particular business \cite{pennacchiotti2011democrats,pennacchiotti2011machine}.

While the research described above uses a fairly large spectrum of methods to study, classify and find connections between social media users' behavior and their media and political preferences, most of the studies referenced employ social network analysis or related methods, focusing on relationships between actors or their behaviors within the network, such as sharing links, following other actors, etc. More recent studies have used computational methods to assess forms of political organization on social media \cite{aragon2016movement}, employed machine learning models to classify rumor and misinformation in crisis-related social media content \cite{zeng2016unconfirmed}, and used deep neural networks to identify and analyze election-related political conversations on Twitter on a longitudinal scale \cite{vijayaraghavan2016automatic}.

We propose augmenting these approaches with a focus on the \emph{linguistic variables} present in the data, and using natural language processing and machine learning techniques to gain further insight into how political and ideological messages travel between mainstream and social media, and how these lingual choices reflect the partisan nature of mainstream media outlets and, subsequently, social media users and their content consumption and sharing habits. Such an approach would allow for a more granular understanding of how language changes allow to detect both important events and partisan leanings in mainstream and social media data.

\vspace{-1em}
\section{Background and Datasets}
\vspace{-1em}

The Euromaidan protests and  subsequent political crisis are the outcome of a continuing trend in the post-Soviet arena. The last decade or so has seen an increase in mass protest actions in the region, with protests erupting in Russia, Belarus, Ukraine, etc. From the 2004 \emph{Orange Revolution} in Ukraine to the \emph{Bolotnaya rallies} of 2011-2012 in Moscow to the Euromaidan protests in Ukraine, a gradual increase in the use of digital technologies and media platforms by citizens has become evident \cite{goldstein2007role,oates2013twilight,onuch2015euromaidan}. At the same time, the region is characterized by a problematic media climate, with mainstream media often co-opted or controlled by the state or the oligarchy. The interplay and mutual influence of mainstream and social media emerge as crucial for understanding the political and civic developments in the region and thus demand more scholarly attention.

\begin{figure*}
	\vspace{-1em}
	\hspace{-1em}\includegraphics[width=1.1 \textwidth]{figures/All_datasources.pdf}
	\vspace{-1em}
	\caption{\textbf{Description of the Datasets.} The Twitter dataset was collected via \emph{Twitter Streaming API} (spikes in daily volume correspond to higher discussion volumes around Ukraine-related topics, February-March is the most active phase of the protest, spike in July corresponds to the MH17 airplane tragedy); the News-RU dataset was crawled from the \emph{news.Rambler.ru} news aggregator website (periodic pattern reflects the weekly cycle); and the News-UA dataset was provided by the developers of the \emph{Kobzi} mobile app.}
\vspace{-1em}
	\label{tab:dataset-description}
\end{figure*}

A number of researchers have already examined some of the more general aspects of Euromaidan, such as the reasons for the protest \cite{diuk2014euromaidan}, who the protesters were \cite{zelinska2015were}, how the protest came together and evolved \cite{onuch2015euromaidan}. However, few have investigated the use of civic media by Euromaidan participants beyond simply saying that social media were used in the protest as `tools' for mobilization and information dissemination \cite{onuch2015euromaidan}. A deeper and more large-scale analysis of the relationship between mainstream media coverage of the crisis and the grassroots social media data around the political unrest, enabled by computational and big data tools and complemented by qualitative analysis, could reveal more about how the partisan agenda during the protests was formed and transformed through lingual choices and memorable memes, and who was able to exert influence on the lingual frames used by the multitudes of social media users and media outlets. Such investigation could also shed light on the reasons and mechanisms of the information war between Ukraine, Russia, and the West that gained in scale after the Euromaidan protests shifted into the crisis characterized by Russia's annexation of Crimea and the pro-Russian uprising in eastern Ukraine.

To research these questions in this paper we analyze two complementary datasets: A \emph{social media} dataset which consists of over $40$M tweets collected for the three most prominent hashtags during and after the Euromaidan protests - \#euromaidan \#ukraine \#kyiv (and their Ukrainian and Russian equivalents) via Twitter Streaming API and a dataset of more than $4$M \emph{news articles} collected from a large Russian news aggregator (Rambler.ru) and its Ukrainian counterpart (smartphone app Kobzi). All three datasets were collected in the period between December 2013 and July 2014. The parameters of the datasets are summarized in \reffig{tab:dataset-description}.

\section{Exemplar indicators of partisan slant}

\subsection{Methodology}

Our initial interest in exploring the \emph{lingual choices} made by mainstream media sources and social media users was sparked by observing the emergence of a number of memes and buzzwords introduced during the events. In this section, we aim to measure the presence of these keywords in the rhetoric of the parties involved in the conflict and explore the extent to which this analysis can be automated.

To analyze the lingual choices of online media sources during the Ukrainian conflict we first exploit the \emph{word embedding} methodology proposed in \cite{mikolov2013distributed,mikolov2013efficient}. The proposed approach devises vector representations of words by analyzing the textual context in which they appear. This is achieved by training a model which for a given word, represented by a vector $X_i$, aims to infer the most likely $2 \times j$-surrounding words vectors which constitute the lingual context where the word was used~\footnote{We used the Skip-Gram model as it provided more interpretable results.}, e.g, $f(X_i) = (X_{i-j}, ..., X_{i-1}, X_{i+1}, ..., X_{i+j})$. Intuitively, semantically closed words are expected to appear in similar contexts and so should produce similar outcomes when applied as the arguments of the function $f(X)$. Thus, if trained on a significantly large text corpus the algorithm is able to assign close-by vectors to the words with similar meanings, thereby providing a powerful framework for analyzing the semantics of the word choices.

Equipped with the Word2Vec implementation\footnote{Gensim natural language processing library https://radimrehurek.com/gensim/}, we build word representations for the textual corpora extracted from our datasets. Note that we mainly focus on vector spaces extracted from the news corpora (News-UA and News-RU) as Twitter's limit of $140$ characters significantly constrains the applicability of this approach. In pre-processing, we remove rare words with less than $10$ occurrences in each of the corpora and end up with a dictionary of $87$K words trained from a content corpus of $600$M words. Note that while the Ukrainian media space is generally bilingual, in our analysis we only focus on Russian-language news articles, in order to allow for a fair cross-comparison of the results, i.e., we focus on the Russian-language media sphere as it presents a sufficient diversity of news sources, with political views spread across the spectrum, ranging from pro- to anti-Kremlin and from pro- to anti-Ukraine, including both Ukrainian and Russian media outlets.

\subsection{Mining semantics of word choices}

In \reftab{tab:word2vec-examples} we present several examples of word associations which were mined from both our news corpora. We pick several loaded terms which were the prominent indicators of partisan rhetoric during the conflict and match these words to the most similar ones according to the trained Word2Vec dictionaries (as measured by the cosine similarity). The results illustrate that the trained model corresponds to our understanding of the semantics of chosen terms.
For instance, the word \emph{referendum (референдум)}, frequently used in the context of the Russian-backed annexation referendum in Crimea, lies very close to its synonyms, e.g., `plebiscite', `voting', etc., in the devised vector space in both the News-RU and News-UA corpora, from both sides of the conflict.

On the other hand, we also notice that some of the synonyms discovered by Word2Vec reflect the propagandistic rhetoric of official Russia and Ukraine during the conflict. For instance, in the News-UA corpus (but not in the News-RU corpus), the word \emph{referendum (референдум)} is associated with the words `non-legitimate' and `fake referendum', and reflect the official Ukrainian state's position on the plebiscite. Similarly, the word \emph{aggressor (агрессор)} as used in association with Russia's actions in eastern Ukraine and Crimea is associated with the words `cynical' and `unprovoked' and captures the attitude of the Ukrainian government in regards to the events.

A similar partisan rhetoric is also observed in Word2Vec word representations mined from the Russian news corpus (News-RU). For instance, we observe that the loaded term \emph{junta (хунта)}, which was extensively used by some Russian media sources to demonize the transitional government formed in Ukraine after the Euromaidan protests, is strongly associated with the words `neo-fascist' and `pro-Ukrainian' in the Russian news corpus (News-RU). Similarly, the word \emph{punisher (каратель)} which was used by some Russian media sources to label the Ukrainian Armed Forces and their operations in the conflict in eastern Ukraine is closely associated with `fascism' and `terrorize', as well as with Stepan Bandera, a controversial figure who has been acknowledged by some in the new Ukrainian government.

Such biased lingual choices are in alignment with recent findings that politically charged rhetoric and biased language were central to the discourse around the Euromaidan protests in Ukraine and the subsequent conflict in eastern Ukraine, both of which featured interference by Russian political forces~\cite{szostek2014media,zhukov2016reporting}.

\vspace{-1em}
\begin{table}[!htbp]
\centering
\begin{tabular}{ | l | c | l | c |}
\multicolumn{4}{c}{\large \textbf{News-RU}} \\ \hline
\multicolumn{2}{|c|}{\textbf{каратель [punisher]}} & \multicolumn{2}{c|}{\textbf{хунта [junta]}} \\ \hline
бандера [Bandera~\footnote{Stepan Bandera - the leader of the western Ukrainian resistance during World War II, accused by Soviets of collaborating with Nazis.}]  & 0.79 & восстание [uprising] & 0.76 \\
безоружный	[unarmed] 	& 0.79 & проукраинский [pro-Ukrainian] & 0.72 \\
расстреливать [shoot]	& 0.77 & неофашист [neo-fascist] & 0.72 \\
терроризировать [terrorize] & 0.77 & неонацист [neo-Nazi] & 0.71 \\
оккупант [invader] & 0.76 & путчист [coupist] & 0.71 \\
фашист [fascist] & 0.76 & самозванец [imposter] & 0.71 \\ \hline
\multicolumn{4}{c}{} \\

\multicolumn{4}{c}{\large \textbf{News-UA}} \\ \hline
\multicolumn{2}{|c|}{\textbf{референдум [referendum]}} &\multicolumn{2}{c|}{\textbf{агрессор [aggressor]}} \\ \hline
псевдо-референдум [fake referendum] & 0.72 & неспровоцированный [unprovoked] & 0.72\\
плебисцит [plebiscite] & 0.71 & враг [enemy] & 0.70\\
голосование [voting] 	&  0.67 & циничный [cynical] & 0.66 \\
автономный [autonomous] & 	 0.66 & развязанный [launched] & 0.66\\
нелегетимный [non-legitimate]	& 0.64& извне [external] & 0.65\\
присоединение [attachment]& 0.63 & оккупационный [invasive] & 0.65\\
отсоединение [separation] &  0.62 &	террор [terror] & 0.64\\ \hline
\end{tabular}
\vspace{2em}
\caption{\textbf{Examples of word embeddings mined from the news datasets during the Ukrainian conflict.} The table presents the word associations mined using the Word2Vec algorithm for several loaded terms (bold font) in Russian (top) and Ukrainian (bottom) news.}
\label{tab:word2vec-examples}
\vspace{-3em}
\end{table}

\vspace{-1em}
\section{Identifying Slant of News Stories}

Inspired by the observations from the previous section we next question the power of the word choices to characterize the difference in partisan media at scale, i.e. are the linguistic choices of partisan media substantially different such that we can automatically differentiate them? We address this question by developing a classification machine learning model and conducting an extensive validation of the model over our news datasets.

\subsection{Methodology}

Using the Word2Vec word representations from the previous section, we train a supervised learning algorithm to find the best indicative words which characterize the language style of a given party. To this end, we first manually classify the Top-30 most popular Russian news agencies\footnote{as ranked by the Medialogia rating agency http://goo.gl/JNvx0Y} as having a strong pro-government or opposition slant and complement this list by the Top-5 Russian-language sources from the Ukrainian internet segment. We achieve this by manually examining 20 or more articles per each news source for qualitative signs of slant, and by investigating public ownership records and publicly available info about the media outlets, their owners and affiliations. While granular and manual, such an approach can be replicated in other studies using media sources, as public ownership records are usually available and provide enough context for classification, while manual examination for signs of slant is based on a designated set of relevant keywords. We also remove all neutral sources from our analysis, i.e those that have not indicated a particular partisan slant. Finally, we only consider news articles related to the Ukrainian unrest and conflict\footnote{This is achieved through filtering the corpora by the relevant keywords, i.e., "kyiv", "ukraine", "donbass", "maidan", "crimea", "luhansk", "dnr" and "lnr". Adding a wider set of keywords had little effect on improving the recall of filtering.}. Our classification results in three categories of media outlets: UA, RU-ind, and RU-gov, exhibiting pro-Ukrainian, Russian-independent and Russian-pro-government points of view on the Ukraine unrest and conflict respectively.

Next, we cluster the Word2Vec vectors obtained from the previous section, and use the produced clusters as a feature space to describe the content of each article. In more detail, we apply k-means clustering with $N = 1000$ clusters to the word vectors of the combined News-UA and News-RU corpus. Then, for each article we calculate a $1000$-items long feature vector $X$ that represents the normalized frequencies of occurrences of words from each cluster that occur in the article, and train a function $f(X) \rightarrow \{RU-gov, Ru-ind, UA\}$ to identify the partisan slant of the article (e.g., whether coming from Russian pro-government (Ru-gov), Russian independent (RU-ind) or Ukrainian (UA) news source\footnote{We refer to each of these three classes as `party' or 'parties' in the rest of this paper.}).

\vspace{-1em}
\begin{table}
\centering
\begin{tabular}{ | c | c | c | c |}
\hline
\textbf{word} & \textbf{$\rho_w^s$} & \textbf{$\bar{\rho_w^s}$} & \textbf{News source} \\ \hline
ъ [Ъ] & 0.38 & 1.0 & Ъ-Kommersant \\
m24.ru [m24.ru] & 0.30 & 1.00 & Moscow 24\\
известиям [izvestiam] & 0.31 & 0.93 & Izvestia \\
господин [gentlemen] & 0.29 & 0.79  & Ъ-Kommersant \\
подробнее [more] & 0.36 & 0.69 & Ъ-Kommersant \\
рбк [RBC] & 0.27  & 0.68 & RBC\\
\hline
\end{tabular}
\vspace{0.5em}
\caption{\textbf{Exemplar markers of individual news sources} as identified by the words with the highest relative frequencies $\bar{\rho_w^s}$ across all words and news sources.}
\label{tab:source-bias}
\vspace{-3.5em}
\end{table}

\subsection{Reducing news source bias}

One crucial aspect to account for in the proposed approach is its ability to learn linguistic patterns that generalise across all news sources of a given partisan slant, rather than markers of individual news outlets. Since the training data in the above method is sourced from a selection of few news sources, without a generalisable approach, the classifier could simply learn to label the partisan slant of an article based on unique words, or \emph{news source markers}, that are specific to a particular biased news source.

For example, it is common that the name of the news source or its correspondents are explicitly mentioned in the byline or text of the article, making it easily distinguishable among other texts. Supposing the training data contains a biased Russian pro-government news source $B$ whose name appears in every article from $B$, we might learn a model that the word $B$ is indicative of a Pro Russian-government partisan slant. Although this is useful to classify other articles from $B$, it does not help identify other pro-russian news sources or articles. We therefore need to adapt the method to learn labels that generalise to all news sources by ignoring news source markers.

\vspace{-1em}
\subsubsection{Description of the problem.}

To show that news source markers are indeed widespread among the news articles in our dataset, we measure the relative frequencies of words appearing in articles from each individual news source. More formally, we define the frequency $\rho_w^s = \frac{N_w^s}{N_s}$ of word $w$ in news source $s$ as a share of all articles $N^s$ from news source $s$ in which word $w$ appears at least once and compare it to the sum\footnote{Note that this is equivalent to using the mean $\frac{\sum_{s \in S}{\rho_w^s}}{|S|}$, since the sum for all words is computed over the same set of sources $S$.} $\sum_{s \in S}{\rho_w^s}$ of the observed frequencies of $w$  across all news sources $s \in S$, i.e., we define the ratio $\bar{\rho_w^s} = \frac{\rho_w^s}{\sum_{s \in S}{\rho_w^s}}$ to identify words which are highly unique to particular news sources.

The top news source markers are extracted as the words with the highest ratio $\bar{\rho_w^s}$ across all words and all news sources.  Table~\ref{tab:source-bias} shows the top few markers. As expected, we observe that articles from some of the news sources - such as Ъ-Kommersant, Moscow 24, Izvestia, and RBC - contain very vivid word markers of that news source. For instance, the words "Ъ" and "m24.ru" used as abbreviations of the Ъ-Kommersant and Moscow 24 news papers appear only within the news articles originating from these two sources. More interestingly, we observe a very high relative frequency of mentioning other general  words such as "господин [gentlemen]" and "подробнее [more]" in articles originating from Ъ-Kommersant, suggesting that there might be other word markers -- beyond just the obvious names of newspapers -- which indicate the writing style of an individual news source.

\vspace{-1em}
\begin{table}
\normalsize
\centering
\begin{tabular}{ |l | c | c | c |}
 \hline
  \textbf{Metric} & \specialcell{\textbf{RU-gov} vs. \\ \textbf{RU-ind}} & \specialcell{\textbf{RU-gov} vs. \\ \textbf{UA}}
			& \specialcell{\textbf{RU-gov} vs. \\ \textbf{RU-ind} vs. \textbf{UA}} \\ \hline
  Precision & 0.66 & 0.78  & 0.57 \\ \hline
  Recall & 0.65 & 0.77 & 0.60 \\\hline
  Accuracy & 0.65 & 0.77 & 0.60 \\\hline
\end{tabular}
\vspace{2em}
\caption{\textbf{Predicting partisan slant in news articles.} The results of a supervised machine learning experiment to identify whether news articles were published by either a Ukrainian (UA), a Russian pro-government (RU-gov) or Russian independent (RU-ind) news agency. }
\label{tab:articles-prediction-performance}
\vspace{-3em}
\end{table}

\vspace{-1em}
\subsubsection{Suggested solution.}
To eliminate the aforementioned news source bias in our prediction model we develop the following approach\footnote{We note that a straightforward approach of removing the most prominent news source markers -- as measured by the relative frequency introduced in the previous section -- has proved to be inefficient for the considered classification problem. In contrast, the method we introduce in the rest of this section provides a more nuanced approach in estimating the relevance of each classification feature.}. We use Random Forest classifiers known for a good performance on modeling high dimensional data and modify the underlying mechanism for constructing individual decision trees. By default, the trees of the Random Forest algorithm are constructed via a greedy search for the optimal split of the training data $D$ which minimizes the entropy of the label classes (i.e., parties in the conflict), i.e.,

\vspace{-1em}
\begin{align}
\underset{split}{\text{minimize }} H_t(D_{left}) + H_t(D_{right})
\end{align}

where the entropy $H_t(D) = - \sum_{t \in T}{\rho_t^D log(\rho^D_t)}$ is defined on the label spaces $t \in T$ in the left $D_{left}$ and the right $D_{right}$ branches of the split ($\rho^D_t$ indicates the share of instances of class $t$ in the dataset $D$). In principle, an optimal split by this definition may be found around the word markers specific to individual news outlets (e.g., the one from Table~\ref{tab:source-bias}). To penalize this unwanted behavior of the algorithm  we introduce the entropy of a news source $s$ as $H_s(D) = - \sum_{s \in S}{\rho_s^D log(\rho^D_s)}$ which - unlike the entropy defined on labels $H_t(D)$ - characterizes the purity of the split in terms of news sources $s$ rather than parties $t$ (i.e., $\rho^D_s$ indicates the share of instances from news source $s$ in the dataset $D$). Intuitively, we aim for a split that discriminates by the party $t$ but not by the news source $s$ and, so, we aim to find the split that minimizes the entropy $H_p(D)$ while preserving a high entropy of individual news sources within the party $H_s(D)$, i.e.:

\vspace{-1em}
\begin{align}
\underset{split}{\text{minimize }} H_t(D_{left}) + H_t(D_{right}) - \alpha (H_s(D_{left}) - H_s(D_{right}))
\label{eq:entropy}
\end{align}

where $\alpha$ is a constant controlling the effect of the proposed adjustment\footnote{The proposed approach was implemented by adapting the internal implementation of the Random Forest algorithm from the open source scikit-learn libary.}.

\vspace{-1em}
\subsection{Cross-source validation}

In summary, we represent each article as a 1000-long feature vector $X$ based on relative word frequencies corresponding to N=1000 clusters induced by a Word2Vec representation of the entire news corpus. We then learn a function $f(X) \rightarrow \{RU-gov, Ru-ind, UA\}$ that labels the partisan slant (Russian pro-government (Ru-gov), Russian independent (RU-ind) or Ukrainian (UA) news source), whilst at the same time ensuring  (using Eq.~\ref{eq:entropy}) that the model  $f(\cdot)$ generalises beyond learning to distinguish markers specific to individual  sources.

To properly validate the proposed approach we conduct a \emph{cross-source validation} as follows. From the news agencies labeled in the previous step we select all with at least 329 articles resulting in a dataset of five agencies from each party. The number of articles from each news agency is balanced by down-sampling the over-represented agencies. We further conduct a five-fold cross validation such that at each step we pick four news agencies from each class to train the classifier and use the remaining one for testing. Since we test the model over a news agency which has not been used for training we are able to control for over-fitting to the writing style and markers of specific outlets.

In \reftab{tab:articles-prediction-performance} we report the average values of accuracy, precision, and recall of the proposed cross-source validation. The results in the table indicate a strong prediction performance of the algorithm (accuracy of $77$\%) in classifying the content of the news articles as coming from Russian pro-government or Ukrainian news sources. This result confirms a sharp difference in the linguistic choices characteristic for the content of Russian and Ukrainian news articles as observed in Table~\ref{tab:word2vec-examples} of the previous section. More interestingly, the accuracy is also high (i.e., 66\%) for the more difficult problem of distinguishing between Russian pro-government and Russian independent news sources which often shared a common view on individual episodes of the conflict (e.g., the annexation of Crimea). For the more general problem of discriminating between all three groups of news agencies, the algorithm is able to achieve an accuracy of 60\%. Note that this is significantly better than a na\"{\i}ve baseline of randomly guessing between the three classes (with expected accuracy of 33\%) signifying the presence of a sharp partisan slant in traditional media.

\vspace{-1em}
\section{Understanding partisan slant in Twitter}

Having studied the difference in linguistic choices characteristic for news agencies during the Ukrainian conflict we now switch to the analysis of the related discourse in social media. The focus of our analysis is on understanding the interrelation between the level of exposure to different news sources among Twitter users and the linguistic choices in their posts.

\subsection{Methodology}

To analyze the level of exposure to various news sources among Twitter users, we rely on an established approach in the literature~\cite{an2014sharing,boutet2013s} and look at the news sharing patterns. To this end, we identify $248$K Russian-language tweets which retweet or mention articles from one of the $Y = 22$ most popular news agencies classified in one of the three groups in our dataset (e.g., whether coming from Russian pro-government (RU-gov), Russian independent (RU-ind) or Ukrainian (UA) news sources). Next, we measure the user focus on a partisan media and a specific news agency by computing the share of the articles he/she shared from his/her most preferred agency/party, correspondingly. We note that a similar approach has been previously used to analyze geographic bias of content access in social media~\cite{brodersen2012youtube}.

More formally, for each user, we measure the number of times $n_y$ she/he shared an article from a news source $y \in Y$ and calculate the user's \emph{news focus} as the share of all times he/she has shared any news article from any news source in $Y$, i.e.,
$\beta = max_{y \in Y}\left(\frac{n_{y}}{\sum_{y \in Y}{n_{y}}}\right)$.
The larger $\beta$ is, the larger the fraction of the user's shares that come from a single source.
Similarly, we measure the user's \emph{party focus} $\beta_{party}$ as the fraction of news articles shared from one of the three ``parties'':  Russian pro-government (RU-gov), Russian independent (RU-ind) or Ukrainian (UA) news sources. Finally, we measure the \emph{diversity of news sources} with which the user expressed alignment by computing the cardinality of the subset $Y_{n_y > 0}$ of all sources in $Y$ from which a user has shared at least one article, i.e., $\gamma = |Y_{n_y > 0}|$.

\vspace{-1em}
\subsection{Patterns of news sharing}

The results of the analysis are presented in \reffig{ref:fig-twitter-partisanship}. Firstly, we note that for the majority of users, less than half of all their shares come from a single news source (\reffig{ref:fig-twitter-partisanship}, left) and that the majority of users express alignment with more than $6$ news sources (\reffig{ref:fig-twitter-partisanship}, middle). At the same time, we note that although more active users tend to focus more of their shares on a single news source, they also (occasionally) share a bigger number of news sources (as indicated by the higher diversity values for the users with more than 50 and 100 tweets in \reffig{ref:fig-twitter-partisanship}.middle).

However, this diversity of news sources is not seen when we look at the more coarse-grained picture at the level of 'parties'  (\reffig{ref:fig-twitter-partisanship}, right):  users' shares tend to be heavily focused on just one party -- on average, more than $85$\% ($90$\% for the very active users) of the shares of a user are for news sources in alignment with that user's main `focus' party. In other words, although users exhibit sophisticated behaviours such as a relatively high level of diversity in sharing from multiple news sources, most of these sources have a single partisan alignment, whether Independent Russian, Pro-Russian, or Pro-Ukrainian Government. Furthermore, both the variety of news sources and the partisan focus increase as user activity levels (number of tweets made) increase.

\vspace{-2em}
\begin{figure*}
 \centering
  \includegraphics[width=0.32 \textwidth]{figures/news_links_articles.pdf}
  \includegraphics[width=0.32 \textwidth]{figures/news_links_diversity.pdf}
  \includegraphics[width=0.32 \textwidth]{figures/news_slant_per_user_kremlin_vs_ukraine_vs_independent}
  \caption{\textbf{Partisan news focus among Twitter users.} Distributions of (left) the user focus on preferred news sources; (middle) the diversity of news sources a user has expressed alignment with; (right) the user focus on partisan media.}

  \label{ref:fig-twitter-partisanship}
\vspace{-3em}
\end{figure*}

\subsection{Predicting political slant from Twitter posts}

Next, we attempt to draw the link between news exposure among Twitter users - as inferred by the news articles they share - and the language they use in their posts. From the results of the analysis in the previous section, we build on the fact that the vast majority of users have a strong partisan focus in the article they share and use that as a label of their political slant. To understand whether and to which extent the language choices in Twitter posts can indicate the political slant of the users we formulate a machine learning classifier to infer the latter from the former and validate it over our Twitter dataset.

To this end, we focus on the $5.9$K users with at least $5$ tweets, at least $3$ of which contain news sources and balance the dataset by choosing an equal number of users from each party. To predict political slant of a Twitter user we formulate a supervised machine learning problem where we model language in a user's tweets with a feature vector $X$, using a methodology similar to the one described in the previous section, and train binary classifiers $f(X) \rightarrow \{RU-gov, RU-ind\}$ and $f(X) \rightarrow \{RU-gov, UA\}$ to identify whether a user is predominantly exposed to Russian pro-government (RU-gov), Russian independent (RU-ind) or Ukrainian (UA) news sources.
Note that we remove all tweets that contain headlines of news and all retweets when constructing the language model of a user $X$. This ensures that we concentrate on the lingual choices in the tweets originating from the user, rather than the messages/sources that he/she (re)tweets. Also, as in the previous section, we only consider Russian-language tweets from Russian-speaking Twitter users, to ensure a fair comparison across all sides of the conflict. However, note that a large number of Ukrainian tweets are also in Russian, and our dataset contains representation from all three parties.

\vspace{-2em}
\begin{table}
\centering
\normalsize
\begin{tabular}{ |l | c | c | c |}
 \hline
  \textbf{Metric} & \specialcell{\textbf{RU-gov} vs. \\ \textbf{RU-ind}} & \specialcell{\textbf{RU-gov} vs. \\ \textbf{UA}}
			& \specialcell{\textbf{RU-gov} vs. \\ \textbf{RU-ind} vs. \textbf{UA}} \\ \hline
  Precision & 0.66 & 0.71 & 0.52\\ \hline
  Recall & 0.66 & 0.70 & 0.52\\\hline
  Accuracy & 0.66 & 0.70 & 0.52 \\\hline
\end{tabular}
\vspace{1em}
\caption{\textbf{Predicting partisan slant in Twitter.} The results of a supervised machine learning experiment to identify partisan slant among Russian-speaking Twitter users during the Ukrainian conflict.}
\label{tab:twitter-prediction-performance}
\vspace{-3em}
\end{table}

\reftab{tab:twitter-prediction-performance} presents the averaged results of the 10-fold cross-validation of the proposed model. We note that the model has good performance, achieving an average accuracy of $70$\% and $66$\% in distinguishing between the users with RU-gov vs. UA slant and between the users with RU-gov vs. RU-ind slant, correspondingly. Comparing these results with the results for predicting the slant of news articles (Table~\ref{tab:articles-prediction-performance}), we note that the performance of \emph{RU-gov vs. RU-ind} classifier is comparable between the two cases whereas the performance of \emph{RU-gov vs. UA} classifier as well as the three-class classifier (accuracy of 52\% in comparison to expected 33\% for a random baseline) is slightly lower for the Twitter case. This can be probably attributed to the fact that the size of the text piece in an average news article is larger than in a collection of ten 140-character-long posts collected for a median Twitter user in our training and testing sets and, so, inferring political leaning from Twitter posts seems to be a harder problem than it is for news articles.

\vspace{-1em}
\section{Conclusions and Discussion}
\vspace{-1em}
In this study we investigated the linguistic indicators of partisan confrontation in mainstream and social media during times of political upheaval by analyzing a dataset of more than 40M tweets and more than 4M news articles from the mass protests in Ukraine during 2013-2014 — known as "Euromaidan" — and the post-Euromaidan conflict between Russian, pro-Russian and Ukrainian forces in eastern Ukraine and Crimea. We designed a natural language processing algorithm to analyze at scale the linguistic markers which point to a particular political leaning in online media and showed that partisan slant in news articles can be automatically inferred with an accuracy of $60$--77\%. This difference in language in traditional news media is reflected in the word choices made by the supporters of similar partisan bent on Twitter — those who tweet news sources of a particular slant can be identified with an up to $70$\% accuracy based on their lingual choices in tweets other than the retweets of particular news sources. Our results have two implications:

First, our results  contribute to the debate on the role of lingual choices in traditional and social media in fostering political frames and partisan discourse during political crises, and confirm that both traditional news sources and users on social media are identifiably partisan. It would be interesting to conduct a more general study into whether the reinforcing partisan nature of the discourse and the political divisions we observe arise from a general lack of empathy and trust in conflict situations, or whether this is specific to the Ukraine conflict.

Second, the results reveal the extent to which partisan rhetoric and political leanings can be automatically inferred from lingual choices, which has implications for the use of social media as a safe platform for free speech in dangerous conflicts. Furthermore, we are able to infer all this with only a ``coarse-grained'' approach: The party labels (i.e., pro-Ukrainian, Russian pro-government or Russian independent) are assigned to the news articles (or social media profiles) based on the polarity of the news agencies they originate from/retweet. The advantage of this approach is that it requires minimal manual efforts — it only requires to label the political slant of a number of news agencies (such as the 15 considered in this paper). However, the polarity and partisan rhetoric of articles may also vary between different topics and authors within a single news source, which we do not directly account for. We partially address this concern in the current paper by focusing only on the articles related to the Ukrainian events which are known for highly polarized rhetoric in both Russian and Ukrainian online media \cite{szostek2014media,zhukov2016reporting}. Although we settled on the coarse-grained approach as a proof-of-concept model, it is clear that a more fine-grained approach could allow for greater accuracy in identifying partisan tweets and political leanings of users and news media.

\vspace{-1em}
\section*{Acknowledgements}

The 'A Shared Space and A Space for Sharing' project (Grant No. ES/M00354X/1) is one of several funded through the EMoTICON network, which is funded through the following cross-council programmes: Partnership for Conflict, Crime and Security Research (led by the Economic and Social Research Council (ESRC)), Connected Communities (led by the Arts and Humanities Research Council (AHRC)), Digital Economy (led by the Engineering and Physical Sciences Research Council (EPSRC)). We would also like to thank the developers of the Kobzi application for providing the News-UA dataset.

\bibliographystyle{splncs03}

redpajama_set_name: RedPajamaArXiv


Asthma as defined by National Asthma Education and Prevention Program is a chronic inflammatory disorder of the airways in which many cells and cellular elements play a role, in particular, mast cells, eosinophils, T lymphocytes, neutrophils and epithelial cells.
When mast cells activate, there is infiltration of inflammatory cells, edema, denudation and disruption of the bronchial epithelium, goblet cell hyperplasia and smooth muscle thickening resulting to asthmatic inflammation.
Bronchodilators should be given every 4 to 6 hours for 24 hours for patient with asthma and viral infection.
Regular monitoring and assessment of the symptoms in the past 2 weeks.
Assess for exacerbation, proper compliance to medications.
Assess for difficulty with feedings, changes in respiratory rate, altered sleep patterns, presence of retractions, decreased appetite and weight loss.
Teach the parents to control or minimize the exposure to allergens.
Emphasize the need for flu vaccine for patients with no restrictions.
Encourage to use air conditioners at home and keep doors and windows closed to minimize the exposure to these triggers.
Encourage a regular visit to the physician and evaluate the proper usage of equipment.
Provide a written instruction for management plans when at home, at school or care givers.
Remind the parents or care givers about the medications, its expiry date as well as having a reserve of it in times of exacerbations.
Give an easy telephone access when the patient must be taken into the hospital.


text:
Radio Kerry
On-Air Now
Kerry's Full Breakfast
The latest Kerry Death Notices from Radio Kerry.The Radio Kerry death notices air on the radio 4 times daily, after the news at 8am and 9am and during the news at 1pm and 5.30pm weekdays (6pm at weekends)For updates call 1550 715 838. Calls cost 97c per minute from landlines.
Michael (Mikey) Garvey, Brackluin, Annascaul & formerly Maum, Camp
Obituaries receptionradiokerry - 26th December 2016
Reposing at the Gleasure Funeral Home on Tuesday from 4pm to 5:30pm. Removal at 5:30pm on Tuesday to the Church of the Sacred Heart,...
Joan O' Connor née Landers, Curraheen, Tralee & formerly of Ballinahunt, Annascaul.
Reposing at Hogan's Funeral Home, North Circular Road, Tralee tomorrow Tuesday from 4:30pm until 6:30pm - followed by removal to St. Brendan's Church, Curraheen....
Denis Den Mangan, Dromin, Milltown, Killorglin
Reposing at Flynn's Funeral Home Killorglin on Tuesday from 7pm to 9pm. Removal on Wednesday to St. James Church, Killorglin. Requiem mass will take...
John Kelly, Ard Mhuire, Rathmore & formerly of Gullane, Gneeveguilla.
Reposing at O'Keeffe's Funeral Home, Rathmore today (St. Stephen's Day) from 5pm - 7.30pm, followed by removal to St. Joseph's Church, Rathmore. Requiem Mass...
Sr. Noella Cowley, O.P. Dominican Sisters, Our Lady of Fatima Convent, Oakpark, Tralee &...
Reposing at The Chapel of Our Lady of Fatima Home today (St.Stephen's Day) from 5pm - 6pm. Evening Prayer @ 6pm. Requiem Mass will...
Michael (Mick) Dineen, Cleandries West, Causeway.
Reposing at Casey's Funeral Home today (St. Stephen's Day) from 5pm -7pm, followed by removal @ 7pm to St. John's Church, Causeway. Requiem Mass...
Mary Elizabeth (Betty) Murphy née Whelan, Monastery Gardens, Killarney & Dublin.
Reposing at O'Shea's Funeral Home, Cathedral Place, Killarney this evening from 4pm to 6pm followed by removal at 6pm to St. Mary's Cathedral, Killarney. ...
Maureen Brosnan (Nee Molyneaux), Knockaclogher, Ballyroe & formerly of Leam, Kilflynn.
Reposing at The Gleasure Funeral Home on Monday (Dec 26th) from 3pm - 5pm followed by removal at 5pm to Our Lady & St.,...
Mary McKenna (Nee Moore), formerly of Glin Road, Moyvane, Armagh & Co., Down.
Funeral will take place on Monday (Dec. 26th ) in Co., Down. Enquiries to Finucane's Undertakers, Moyvane.
Kitty Fitzgerald (Nee McKenna), Meenahorna, Duagh & formerly of Knockanebrack, Lyreacrompane.
Reposing at Harnett's Funeral Home, The Square, Abbeyfeale on Monday (Dec 26th) from 5pm followed by removal @ 7pm to St. Brigid's Church, Duagh. ...
Undertaker Login
Man charged with over 100 offences involving five children is sent forward for trial...
radiokerrynews - 23rd January 2020
A man charged with over 100 offences involving five children, including rape, has been sent forward for trial in the Central Criminal Court.He appeared...
Woman who died following Kilcummin crash to be laid to rest today
The woman who died following a crash in Kilcummin earlier this month is to be laid to rest today.Mary Kelliher, who was in her...
Kerry Fire Service called to combat fire in Tralee last evening
The Kerry Fire Service was called to combat a fire in Tralee last evening.Shortly after 9:30, two fire units and a water tanker were...
No winner of last night's lotto
There was no winner of last night's lotto jackpot, which was worth over €6.8 million.The winning numbers were 10, 15, 19, 21, 24, 37...
A Problem Shared – January 22nd, 2020
Admin - 22nd January 2020
A mother is giving gifts for the house but while the family appreciates the gesture, they don't like the presents. Val and Tony offered...
Wednesday Evening Sports Update
radiokerrysport - 22nd January 2020
RUGBYJonathan Sexton is fully confident of playing in Ireland's Six Nations opener with Scotland.The out-half has spent the past few weeks on the sidelines...
Ireland Women's Rugby Captain Targeting Three Home Wins In Six Nations
Ireland captain Ciara Griffin says the squad are looking forward to improving on last year's performances in the Women's Six Nations after a disappointing...
Kerry Hurlers Without Na Gaeil Duo For League Opener
The Kerry hurlers will be without two Na Gaeil players for the county's opening Allianz Hurling League Division 2 game against Mayo.Dan Goggin and...
Radio Kerry your voice in the kingdom 96-98fm
We use cookies to give you the most relevant experience by remembering your preferences and repeat visits. By clicking "Accept", you consent to the use of ALL the cookies. However you may visit Cookie Settings to provide a controlled consent.
__cfduid 1 11 months The cookie is set by CloudFare. The cookie is used to identify individual clients behind a shared IP address d apply security settings on a per-client basis. It doesnot correspond to any user ID in the web application and does not store any personally identifiable information.
DSID 1 1 hour To note specific user identity. Contains hashed/encrypted unique ID.
advanced_ads_browser_width 0 4 weeks
__utma 0 2 years This cookie is set by Google Analytics and is used to distinguish users and sessions. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.
__utmb 0 30 minutes The cookie is set by Google Analytics. The cookie is used to determine new sessions/visits. The cookie is created when the JavaScript library executes and there are no existing __utma cookies. The cookie is updated every time data is sent to Google Analytics.
__utmc 0 The cookie is set by Google Analytics and is deleted when the user closes the browser. The cookie is not used by ga.js. The cookie is used to enable interoperability with urchin.js which is an older version of Google analytics and used in conjunction with the __utmb cookie to determine new sessions/visits.
__utmt 0 10 minutes The cookie is set by Google Analytics and is used to throttle the request rate.
__utmz 0 5 months This cookie is set by Google analytics and is used to store the traffic source or campaign through which the visitor reached your site.
NID 1 6 months This cookie is used to a profile based on user's interest and display personalized ads to the users.

redpajama_set_name: RedPajamaCommonCrawl


text:
Making My First Feature… The true story of LondonSWF delegates who made their movie!
Room: Tuke Cinema
040 - (T)
with Roland Moore and Andromeda Godfrey & Diana Juhr DeBenedetti
They met at LondonSWF events, made Create50 films and committed to making a world class feature together…
Their movie, 2:Hrs, is released on July 29th. How did they do it? How can you too?
Meet Diana, Andromeda and Roland. Together with director D. James Newton, they made their debut feature film 2:hrs, a children's adventure romp. They did it with little more than attitude, hustle, less cash than they needed but enough passion to keep on going.
Warning! They may appear a little drunk in this session, but that's just a combination of exhaustion and giddy excitement that they actually did it.
Warning! You may be inspired and informed in such a way as to follow in their footsteps!
In this session they will take you on their journey, illuminating the pitfalls that many often fall into (and some they also fell into), and will reveal their strategies and tactics that led them to getting to set, to international sales and beyond…
Fundamentally, their story is one of taking action. Starting with a short they made for our very own Create50 project 50 Kisses, and moving onto 2:Hrs, their stunning children's fantasy that is receiving rave reviews.
Their story is one of 'one step after another'.
Their message? You can do the same.
Stop waiting to be discovered and start now by creating your own destiny through inspired and informed action.
Buckle up for a bumpy, entertaining and inspiring session.
Andromeda Godfrey & Diana Juhr DeBenedetti – Actress & Screenwriter
Credits include: 2:Hrs, Dream Date
Actress Andromeda and Screenwriter Diana founded Makelight Productions in 2010 to develop and produce entertaining independent films.
Roland Moore – Screenwriter
Credits include: Land Girls, Rastamouse, Smack the Pony
Roland created the award-winning returning drama series Land Girls for BBC1 and has written for a range of television series.
It was my first LondonSWF but it won't be my last. Friendly, informative and fun. See you next year!
Louise Walters, Screenwriter


The first important thing to do after adopting or acquiring a canine member into your family is to choose a suitable name for him if it's a male dog. Since there are multiple choices for boy dogs' name, you have to look for the ideal one that suits your dog. To choose the name of your puppy can be a quite huge task since you have lots of things to consider like breed, color and some traits. Here are some helpful tips to assist in selecting the right name your boy dog.
The character of a dog can also reflect kind of name should choose for it. For instance, your dog like sleeping, give him a name like a dozer, bang and buster. Likewise, if your dog has a large appetite, the names that use him is such as Amazon, Dragon, Cruncher, Glutton or cracker.
There also have perfect name to choose for your boy dog names. For example, Galahad does not sound that big, or even names such as Apollo, King Napoleon, or Czar can sound majestic. Also, the names that rank higher include Chip, Gruff, Grouch or Gusto. They are considered among the perfect names. Names such as Bullet, Achilles and Bang are also an ideal name that suits a male dog.
I prefer something unique that is unmatched and exclusive then names such as Goliath, Fur Face, Jig Saw, Big tail or Hermit. You can as well come up with expressive names such incredible, Energizer, Dazzler or Flipper.
The initials such as JD can be unique. The exceptional male dogs can also be a great hit among your family and friends. Whichever name you choose from dog make it's unique and easy to pronounce. Also, you can consult your family about the name you want to give to your dog. Give them chance to approve whether good or bad since you choose a name your dog is for a lifetime.
There are countless perfect names you can choose for you male dog all you need to is spend much quality with your dog to study his character.
When it comes to naming a dog, always try to give a name that totally differs from other pets in the family or in the neighbourhood.so as to avoid any sort of confusion. Some names listed in this article are common but you try to use techniques such as breed, or character to name your dog.


"Turbo Download Manager (v2)" is a multi-threading download manager with built-in support for downloading media and image files. The extension is capable of getting remote files in parallel segments to increase the downloading speed. Also, there is an internal interruption control to resume downloads on slow or bad-quality networks. This extension offers a complete download manager user interface and well as download observer and multi-threading supports all in a single package. For the extension to support multi-threading, your browser needs to support FileSystem APIs. If these APIs are not yet supported, the extension redirects your downloading jobs to the internal download manager.
What is the "Turbo Download Manager (v2)" extension?
This extension has four main modules: 1. Download manager popup (to monitor downloading jobs initiated either from this extension or from the built-in download manager). 2. Download dialog window (to add multiple links and to collect images from the opener tab), 3. Multi-threading internal module (downloads.js) to handle multi-threading jobs. 4. Download observer module to monitor video and audio sources that are requested by each page.
To monitor ongoing downloading jobs from either this extension or the built-in download manager click on the toolbar button to open the popup. From this popup, you can pause and resume all downloads and see the number of segments for multi-threading jobs. Note that this is the only place you can monitor and stop multi-threading jobs. No multi-threading job appears in the default download manager of your browser until it is fully fetched.
To request new downloading jobs, you need to open the "Download dialog window" which can be accessed either from the "Download manager popup" or from the right-click menu item over the toolbar button. This window can be used to initiate multiple downloads simultaneously with a custom number of threads. Note that if a single thread is selected for a download, this extension redirects the request to the internal download manager (there is no advantage of getting a single-threaded file with the internal "downloads.js" module). So to initiate a file with the built-in download manager simply reduce the number of threads to one. You can define the default thread sizes for image, audio and video formats from the options page.
How does the multi-threading work?
When a new job is requested, the extension inspects the server and ensures the multi-threading is supported by the server. If the multi-threading is not supported, your download will be handled by browser's built-in download manager. Otherwise, the total size of the file is calculated and then the file is split into different segments. Next, the extension asks your browser to allocate the same disk space locally. If this space is available on your system, then all segments are initiated one by one. When all segments are completed successfully the file is moved from the internal storage to the user's defined Downloads directory. This step is handled by your browser built-in download manager (copy the file from internal storage to user's Downloads directory). This helps the browser to track the downloads that are handled by this extension so there is no internal module to handle user's history! Also, other extensions can access the downloads history like normal downloads. Note that in oppose to the built-in download manager, the temporary file is not being created in the Downloads directory since the extension has no access to this directory. Instead, the internal storage is used.
Note to the Firefox users: at the time of writing this documentation, Firefox browser still does not support "FileSystem" APIs that are required to handle file creation and segmented writing. This extension has a wrapper module to use "IndexedDB" APIs but this method is not efficient so it is disabled. As soon as the "FileSystem" APIs get supported by Firefox, the extension automatically uses them and multi-threading will work.
How can I add a new downloading job?
You can add a new job from either right-click context menu over a link, video, audio or image element or by adding the actual downloadable links to the "Download dialog window" which can be accessed from the right-click context menu over the toolbar button. When a download is added using "Download dialog window", you can customize the number of threads for each job individually, however, when a download is initiated with the context menu items, the default threading number is used based on the mime type of the link.
How can I download all images on a page?
First, switch to the tab that you want to grab images from. Now open the "Download dialog window" from the right-click context menu over the toolbar button. There is a button in the UI to search for images loaded in the opener tab. Click and wait until all images are grabbed. You will see the list of all images in the UI. Select those images you want to download and press the "Download" button.
How can I download all media (video and audio) files from a single page?
This extension has an internal module to track all media sources in a page. You can see the number of detected sources in the badge area of the toolbar button (for the current tab). If the "Download dialog window" is opened from a page with the badge number, all the detected sources will be listed in the window. If a page has multiple sources and you are confused how to get the last media link, first right-click on the toolbar button and press the "Reset" item. Now start the track that you want to have access to its source. when the toolbar button badge text is updated, open the "Download dialog window".
What is the meaning the toolbar badge text?
How can I monitor my downloading jobs?
You can left-click on the toolbar button to open the "Download manager popup" view. In this view, all the downloaded files or in-progress ones are listed. This is the only place you can monitor multi-threading jobs as well are the built-in ones.
What is the recommended number of threads?
If the file is not too big, use two threads and if it is big use three threads.


text:
My Wanderings in Retirement
2. Movano Structure
3. Water System
4. Electrical System
5. LPG system
Skipton Castle
Posted on Monday, 27 August, 2012 by Robin Goldsmith
Something less than 1000 years ago, a Norman baron built a castle on a rocky outcrop to protect the neighbourhood from marauding Scots. Seven hundred years ago King Edward II granted the castle to a Robert Clifford who he appointed Lord Clifton of Skipton. The first Lord got busy improving and strengthening the castle but sadly, four years later was killed at the Battle of Bannockburn (1314) fighting the Scots. For the next three hundred and thirty years, the Lords Clifford prospered and the castle grew in strength and importance. During the English Civil war, the castle was the only royal stronghold in the north of England and in 1642, the Parliamentarians laid siege to the castle. It withstood the siege for 3 years until in 1645, an honourable surrender was negotiated and the garrison of 300 was allowed to leave with drums beating and flags flying. They paraded down the main street and dispersed.
Cromwell was not one to leave such a stronghold standing and ordered its destruction which was done, apparently, in an unenthusiastic way by local people. In 1605, George Clifford, 3rd Earl of Cumberland had died. He willed the estate to his brother, rather than leave it to his daughter, the redoubtable Lady Anne Clifford, who was only 15 at the time. As a child she had been a favourite of Queen Elizabeth I and had known Queen Anne of Denmark, the queen of King James I of England. She was having none of that and entered into a long and complex legal challenge. It was not until the 4th Earl's son died in 1643 that she managed to secure the family estates and in 1649 she was able to take possession. Almost immediately, she set to work to restore the castle at Skipton.
She was granted permission to do so on the understanding that the walls would not be as thick as before, and the roofs would not be flat and able to bear cannon. The repairs were carried out and to mark their completion, she planted a yew tree in the Conduit Court in 1659.
She worked tirelessly on her castles in the North despite her two marriages to other noblemen. In 1609 she had married the Earl of Dorset and, six years after he died in 1624, she married the Earl of Pembroke and Montgomery.
This castle is a wonderful synthesis of styles. The entrance tower is full of Norman touches such as the entrance, the approach to which used to be a drawbridge.
Inside the Court was a secure area and so all of the formal rooms face inwards. Though small, this gives the courtyard a cosy, almost homely feel. The Great Hall would have been used for all meals, usually taken around mid-day. The Lord, his Lady and all the main people of the Garrison would gather and be served by Retainers from the adjacent kitchen. For those caught short, it was not far to go to relieve oneself. The garderobe or privy was just the other side of the kitchen it delivered its gifts into the river below, as did the garbage from the kitchen.
The first picture shows the Great Hall. Both chutes can be seen in the picture on the right.
The entrance to the castle is an imposing mix of medieval fortress and later-day house. Inside the gateway, on the right, there is a Renaissance Grotto; one of only two preserved in the country. Created between 1626 and 1629 for Henry, Lord Clifford, it is made from coral and oyster shells and represents the four elements, fire, wind, air and water.
Lady Anne Clifford died in 1676 at the ripe old age of 86. When she died she was Baroness Clifford, Dowager Countess of Dorset and Dowager Countess of Pembroke and Montgomery. Her daughter died the same year, at which point it ceased to be lived in. In the 1970's the castle was bought by the Fattorini family. They had arrived early in the nineteenth century and in 1831 opened a jewelery shop in Harrogate. Twenty years later they opened another in Bradford. They specialised in sports trophies and medals making, amongst others, the FA Cup and the Rugby League Challenge Cup. They have done much to restore the castle and to open it to the public. The family live in a wing of the castle.
ga('create', 'UA-44971011-1', 'free-at-last.co.uk'); ga('send', 'pageview');
'Like' on Facebook to get all the updates
This entry was posted in England by Robin Goldsmith. Bookmark the permalink.
'Like' me on Facebook


Road Runner provides a wide selection of blunt wraps in a variety of flavors and strengths. We have everything from grape flavored full strength cigars to our double platinum mango range. Whether you prefer the exquisite taste of our Golden Vally cigars, or the fruity zest of our blunt wrap varieties, we are the one stop cigar shop for all of your cigar related needs. Road Runner is one of the top wholesale cigar distributors in the nation. We are able to offer prices that undercut most other companies. In addition to our rock bottom prices, we also offer free shipping on selected items, and on all orders that exceed $150.00. When you buy blunt wraps online, we are the go to trusted source for great tasting and authentic products.
Since we are a wholesale dealer, we have access to the cheapest deals around. Don't toss your money away by wasting it on cheaply made cigars that leave nothing but a bad taste in your mouth, when you can have premium quality cigars from Road Runner. Our branded luxury cigars all have great flavor and a distinct taste that is enough to charm even the most selective of smokers.
Contact us today to place an order for your premium blunts and other smoking related products. We are happy to assist you in any way that we can. Whether you need shipping calculations, product information, or general help in getting your questions answered, we will be pleased to help. Don't settle for less than the best. Call Road Runner today! Let us show you why customers choose Road Runner time after time for their cigar purchases.


Yakima, Washington – March 26, 2018: Light aircraft manufacturer, CubCrafters, has announced FAA certification for the installation of Wipline 2100 floats on the company's XCub. Both amphibious and seaplane versions of the floats, built by Wipaire, Inc., have been approved and are now part of the XCub's Type Certificate.
January 10, 2018 (PRIEST RIVER, ID) - World-leading aerospace design, product development and manufacturing company Aerocet® announced today that it has received European Union-EASA approval on the 3400 amphibious twin aircraft composite floats for the Cessna 206.


Israel's political picture was brought a little more into focus Monday, at least for the moment, as two of the main candidates for prime minister firmed up their positions and came out swinging. Benjamin Netanyahu, the incumbent, easily won a primary within his Likud Party and formally emerged as its candidate for prime minister, television projections indicated. In so doing, the embattled leader, whose government fell Dec.
JERUSALEM - There was no white smoke and little pomp, but a collection of religious and political leaders emerged Wednesday from a much-anticipated private meeting in a Jerusalem hotel to announce the election of Israel's next two chief rabbis, who will take their place among the nation's most influential religious figures. It was a bruising, sometimes ugly campaign in which many of the candidates were tainted by allegations of corruption, nepotism or racism, underscoring why the government-funded rabbinate has fallen so low in public opinion in recent years.
HISTORY: Israel was founded in 1948 after the Holocaust as a haven for Jews on part of what was their biblical homeland. At the time, the area was British-ruled Palestine and inhabited largely by Arabs, many of whom opposed a Jewish state. The Palestinians hope to establish their own state in the West Bank and Gaza as a result of Arab-Israeli peacemaking. LAND DISPUTE: In the 1967 Middle East war, Israel captured the West Bank, the Gaza Strip and the Golan Heights.
The weather at Razi Elementary School was depressing -- a driving winter rain mixed with occasional hail that soaked prospective voters as soon as they stepped out of their cars. Inside the school, local politicians fretted about the possibility of a record low turnout here in Israel's second-largest Arab city, and among Arab voters nationwide. On the streets, a protest against a right-wing Israeli politician serving as an election supervisor here briefly turned violent.
Mahmoud Abbas, president-elect of the Palestinian Authority, didn't wait for election officials to formally announce his victory. Instead, he was on the job first thing Monday morning -- perhaps because he knows he has his work cut out for him. Abbas, the 69-year-old successor to the late Yasser Arafat, received a daylong procession of dignitaries offering congratulations for his win in Sunday's landmark elections in the West Bank, Gaza Strip and East Jerusalem.
Mayor Shlomo Lahat of Tel Aviv, a member of the rightist Likud Bloc, came out publicly the other day in favor of giving the Israeli-occupied West Bank and Gaza Strip to Jordan's King Hussein, a position shared by the most dovish wing of the rival Labor Alignment.
Peres to Continue Process of Peace, Won't Call Election : Israel: Acting prime minister will push for expansion of Palestinian self-rule. Rabin's widow condemns right-wing rhetoric, lashes out at opposition leader.
Acting Israeli Prime Minister Shimon Peres, in mourning over his slain predecessor, took up the reins of government Tuesday, restating his commitment to peace and pushing ahead with the expansion of Palestinian self-rule in the occupied West Bank. In meetings with world leaders and in public statements, Peres said he will not seek early elections. He said he is determined to press forward with the 1993 Israeli-Palestinian peace accord in the remaining year of the Labor-led government's term.
It seemed like a clear-cut choice for the 15,000 Israeli residents of the strategic Golan Heights: Labor Party candidate Shimon Peres talked of returning the Golan to Syria in exchange for a permanent peace treaty, while Likud Party hopeful Benjamin Netanyahu labeled any return of the green and wind-swept highlands "impossible." But to hear residents tell it Wednesday, it was a false choice. For them, the handwriting is already on the wall.
PLO Blames Israelis for Delay in Elections : Mideast: Israel says the holdup is due to proposal for a Palestinian Parliament, which deviates from pact.
Israel and the Palestine Liberation Organization are trading angry accusations about who is to blame for the delay in holding West Bank and Gaza Strip elections called for in their peace accord. Both sides agree on one thing: It is growing increasingly unlikely that Palestinian elections will be held before the end of this year.
Israeli Prime Minister Ehud Barak, looking haggard and grim, tossed his second bombshell in 12 days into the political arena Saturday night, announcing at a hastily called news conference that he will resign today and face a new election in 60 days.
Duki Dror had already cast his ballot, marking his choices on the yellow and blue forms at the Aharon Katzir High School polling place in this breezy seaside city. But he lingered Wednesday in the morning sunlight, agonizing about issues of peace and security, about the hope and fear that loomed, as always, over the Israeli elections. "Here in this place, in the Middle East, we cannot take a chance and not succeed in it," said Dror, a documentary filmmaker.


text:
Q: For rings $R \subseteq S, \alpha \in S,$ how is $R(\alpha)$ defined if $\alpha$ has no inverse? I understand that $R(\alpha)$ is defined to be the smallest subfield of $S$ that contains $R$ and $\alpha.$ But how is it defined if $\alpha$ has no inverse for example?

A: It's not defined.  The notation $R(\alpha)$ is normally used only when $R$ and $S$ are fields.  It may also be used sometimes where $S$ is only an integral domain, in which case it refers to the smallest subfield of $K$ containing $R$ and $\alpha$, where $K$ is the field of fractions of $S$.

redpajama_set_name: RedPajamaStackExchange


As well as giving you an understanding of the main elements of computing, studying Computer Science A-level at Bilborough College will build your awareness of the social, economic and legal implications of using computer-based systems in a variety of situations. Please note that studying for this academic course is very different from the day-to-day use of PCs as a hobby or as a tool for other subjects. It is not about mastering the latest system available in the IT or games market (although admittedly that is fun). Programming will be an essential part of your studies and you'll be expected to put a considerable amount of work into mastering basic programming skills using VB.NET.
Here in Bilborough College's Computer Science department we are building links with Loughborough University, Sheffield Hallam University and the University of Coventry, so you can visit them and take part in special lectures focusing on Computing and project management. To give you further insight into possible degree paths, former Bilborough College students who are doing Computer Science related courses at university will come in to share their experiences with you.
Like other A-Level's, Computer Science is a two-year course. Over the course of the two years you will complete three components. Component one is all about 'Programming & Systems Development' There are nine topics to study in this component. This component will be assessed at the end of the second year by a written examination lasting for two hours and forty-five minutes worth 40% towards your final grade.
Component two is all about 'Computer Architecture, Data Communication and Applications' There are eight topics to study in this component and like component one will be assessed at the end of the second year by a written examination lasting for two hours and forty-five minutes worth 40% towards your final grade.
Component three is a practical component (coursework). In this component you will be putting into practice all the programming and systems development skills you learned during the first year of the course. This component will start at the beginning of the second year and be completed by Easter.


There are total 1 job opportunities in Innovative Islamia School till today 20 April 2019. Innovative Islamia School few new vacancies are in city Abbottabad, Pakistan. You can view Innovative Islamia School April 2019 all latest job opportunities online for Punjab, Sindh, KPK, Balochistan, AJK from all major cities including Lahore, Karachi, Islamabad, Rawalpindi, Faisalabad, Multan, Hyderabad, Quetta, Faisalabad, Gujranwala, Gujrat etc.


Victoria Alonso and Robert Downey Jr. on the set of Iron Man 3 (2012).
In February, acclaimed producer and Marvel Studios Executive Vice President of Physical Production Victoria Alonso was bestowed with the prestigious VES Visionary Award. And with that much-deserved honor for her stellar creative achievements, she broke new ground as the first woman to receive it. A staunch advocate for the advancement of women in VFX, Alonso wears the responsibility and opportunity of being a trailblazer with grace, conviction and hope for all that lies ahead for those following her path.
In conferring Alonso with this distinction, the VES recognized her impact in elevating visual effects as an integral element of the art and business of moviemaking and cited her leadership in redefining the profile of visual effects on a global scale.
An amazing creative force and powerhouse producer, Alonso's unique vision has delivered some of the most popular movie franchises of all time. She is currently executive producing Guardians of the Galaxy Vol. 2 and Thor: Ragnarok. In her executive role, she oversees post-production and visual effects for the studio slate. She executive produced Doctor Strange, Captain America: Civil War, Ant-Man, Avengers: Age of Ultron, Guardians of the Galaxy, Captain America: The Winter Soldier, Thor: The Dark World, Iron Man 3, as well as Marvel's The Avengers. She co-produced Iron Man and Iron Man 2, Thor and Captain America: The First Avenger.
From left: Craig Kyle, Executive Producer; Victoria Alonso, Executive Producer; Kevin Feige, Executive Producer; and Director Kenneth Branagh on the set of Thor (2010).
Alonso defines herself as a fiercely inquisitive person, a trait which has served her well. "I started asking a lot of questions and they seemed to think: this girl seems to be smart enough, friendly enough, cares enough. So would you like to do a job for us? Ed Ulbrich's assistant had broken her leg and so I helped for two weeks…and stayed for four years.
"One of the things I always love about visual effects is that it's ever-changing. Just when you think you know it, you don't. There is always something new, faster, better, more radical out there in the market and so you're consistently evolving and learning. I'm that person who needs to be consistently stimulated, so I'm a good fit…and I love geeks and some of them need someone like me to help them communicate. So it was a match made in heaven.
"It was completely on the job training. I figured if I could understand how they did it, I could help them maximize the efficiencies of how we get the image where it needs to go so that we have happy clients. My entire process was in breaking down how to get things done in hours it didn't appear we had, and reassembling the workflow to get ahead of the curve.
Victoria Alonso and Gwyneth Paltrow on the set of Iron Man 3 (2012).
On the subject of diversity and women in VFX, Alonso does not mince words. "We are consistently failing in terms of diversity and that is a hard statement to make. We have a lot of work to do and it's not moving fast enough. It's one thing to see more women in the audience at things like SIGGRAPH, but we need to see more women at the helm of these panels. I won't stop until we have parity, which will make me very happy.
"When I went to this year's [Academy] bake-off, I saw 10 wonderful presentations by incredibly talented white men. There was not one woman, and no diversity on those teams. Even though those men are so talented, I would be remiss in not mentioning that we are not represented at all – and that includes my own teams.
From left: Stephen Broussard, Executive Producer; Victoria Alonso, Executive Producer; and Charles Newirth, Line Producer, on set of Thor (2010).
To women looking to enter and rise up in VFX: "For women to invest and achieve in this field, they need to have a family-friendly place they can work and grow, like Facebook and Google. There has to be room for flexibility. The other reality is that it has evolved into a gypsy life, and we can't change that, and it may be more difficult to embrace that kind of career with a family. Big jobs require a lot of time. Even while we create new technologies that allow greater efficiencies, we somehow shorten ourselves in the schedule in the relentless pursuit of a good story.
"When it comes to emerging technologies, I think there is room for everybody. Audiences are thirsty for what's new, but also dedicated to more traditional ways of experiencing a good story – like your most memorable experiences sharing stories around a campfire. It's not about one part of the industry eating up another – it's great that the industry is so flexible with mediums that can open up your mind and allow you to think of things differently one day and see something in 2D or 3D of hear it in Atmos and be delighted in your choices. So not new dog beating old dog or big dog conquering small dog – it's more like, let's all go play!
"My favorite thing: to wake up in the morning and think of the privilege of making something that kids and those with the kid-inside are expecting to see and making sure that we can be faithful to what that dream was and either equalize that feeling or experience or make it better. If I continue to do that for the rest of my life, I would consider myself beyond lucky. If I can share with the girls that it can happen to you, that it doesn't matter how different you are then I would have left a legacy for my daughter, which I think is incredibly important to know that ' if you can see it, you can be it'!


text:
US ethanol producers experience another record-breaking week
The U.S. ethanol industry set another new record for production the week ending Jan. 27, with production averaging 1.061 million barrels per day. The industry set new records three out of four weeks in January.
Valero reports improved operating income for ethanol segment
Valero Energy Corp. has released financial results for the fourth quarter of 2016, reporting the company's ethanol segment achieved $126 million in operating income for the quarter, compared to a loss of $13 million for the same period of 2015.
UNICA reports ethanol sales, production for first half of January
UNICA, the Brazilian sugarcane industry association, has announced plants in the south-central region of the country processed 1.18 million tons of sugarcane during the first half of January, producing 67.81 million liters of ethanol.
BETO publishes new strategic plan
In January, the U.S. Department of Energy's Bioenergy Technologies Office released a new strategic plan that serves as a blueprint on how it will tackle the challenges and opportunities that lie ahead in building a sustainable bioeconomy in the U.S.
Sensenbrenner introduces bills that aim to reform the RFS
On Jan. 31, Rep. Jim Sensenbrenner, R-Wis., introduced two bills that aim to alter the renewable fuel standard (RFS). One focuses on mid-level ethanol blends, while the other focuses on cellulosic biofuels.
RFA, Transcaer to host 4 ethanol safety webinars
The Renewable Fuels Association and Transcaer are hosting four free ethanol safety webinars this year for ethanol emergency response teams. The first webinar will be held on Feb. 7, followed by webinars on April 11, June 14 and Aug. 2.
Nebraska Ethanol Board elects executive committee
During the Nebraska Ethanol Board meeting Jan. 24, board members elected new executive committee officers for 2017. Members of the Nebraska Ethanol Board are appointed by the governor to serve four year terms.
Clean Cooking Madagascar issues RFP for ethanol micro distillery
Clean Cooking Madagascar, and NGO based in Antananarivo, Madagascar, is seeking proposals for an ethanol micro distillery for its national Ethanol Stove Program in Madagascar. CMM plans to create a market for 100,000 ethanol cookstoves.
2 Purdue University students awarded NEC scholarships
The Renewable Fuels Association and the Renewable Fuels Foundation have announced Daehwan Kim and David Orrego as winners of student scholarships that will send them to RFA's 22nd annual National Ethanol Conference.
UAI calls on EPA to withdraw erroneous vehicle emissions model
Stakeholders, including the states of Kansas and Nebraska, joined the Urban Air Initiative and the Energy Future Coalition as they petitioned the U.S. EPA to correct the agency's flawed models that limit the use of higher blends of ethanol.
Taurus Energy: Favorable opinion over XyloFerm, distillers grains
The expert panel convened by Taurus Energy has concluded that the genetic modifications made to create the XyloFerm strains do not alter the safety assessment or risk profile of distillers grains produced using non-modified, wild type yeast.
Syngenta announces $340,000 donation to Prime the Pump fund
Syngenta recently announced a donation of $340,000 to the Prime the Pump Fund to help make higher ethanol blends more widely available and help grow demand for American ethanol.
EPA approves Little Sioux Corn Processors for cellulosic ethanol
Edeniq Inc. and Archer Daniels Midland Co. recently announced that the U.S. EPA has approved Little Sioux Corn Processors' registration of its 150 million gallon per year Marcus, Iowa, ethanol plant for cellulosic ethanol production.
Decoded microbial metabolism explains biofuel yield
To unravel how intricate waste biomass converts to biofuels, a Cornell professor studied Clostridium acetobutylicum to decipher its metabolism. Understanding the bacterium's sugar-processing complexities may lead to improved biofuel yields.


Is not a long time that Ubuntu 11.10 has been released and users are very happy with this upgrade! The main upgrade or feature is the Unity, that finally mature. We have the new Gnome 3.2 also Firefox 7, LibreOffice 3.4.2, Gwibber and Ubuntu Software Center get updated. Ubuntu 11.10 is running on Linux 3.0 kernel. It's a new login on the system and a new font Ubuntu monotype.
For more features or a specific argument visit the Releases Notes or to have a free copy of Ubuntu, visit the Download Page.
What I want to share today is this nice theme for Ubuntu, called "Genoid". A theme for Gnome Environment, Ubuntu Linux inheriting Android Theme with some modification and improvement. Genoid has GTK, GDM and icon themes to have a complete android experience.
You can download Genoid theme here.
In the lasted version of Compiz Fusion 0.7.6, to many new plugins and eye candys have been added. One of this new features is that you can now rotate your desktop as cylinder or sphere.
Lady's and Gentlemen, I'm glad to release today "Ubuntu WP" a Free WordPress Theme.
Ubuntu is a computer operating system. It is often rated as the most popular of the many Linux distributions.
Here we are with this nice theme will a lot of features and support also WordPress 2.6+.
The theme load time is very fast and this is most important. I have used just a few images and no other scripts.