text:
In-Form Harris To Lead South Africa Davis Cup Team Against Venezuela
The tie will be held in New York on 18 and 19 September.
Lloyd Harris (photo: Peter Heeger)
Tennis South Africa Press Release, September 7, 2021
Rising international star Lloyd Harris will spearhead a strong South African team packed with a mix of youth and experience in their Davis Cup by Rakuten World Group II tie against Venezuela next week.
A five-member national squad was named by SA captain Christo van Rensburg on Tuesday for the clash, to be played at a neutral venue (West Side Tennis Club) in New York on 18 and 19 September, a week after the conclusion of the US Open.
"Lloyd Harris is playing some of his best tennis, which is exciting, and this team has experience, youth, speed and power," said Van Rensburg, who will lead the SA team for the first time.
Harris, the SA No 1, has displayed superb form this season, capped by his breakthrough at the US Open where he faces Alexander Zverev of Germany in the quarter-finals on Wednesday.
And he will be well backed in the clash against Venezuela, with the squad also featuring Davis Cup veterans Ruan Roelofse and doubles specialist Raven Klaasen.
They will be joined by 20-year-old Philip Henning, and 21-year-old players Sipho Montsi, who are both based in the United States where they compete on the collegiate circuit.
"The depth of this team consists of the up-and-coming college stars, and being in New York makes it easier for them to get off from college duties," Van Rensburg said.
"Coming together earlier than usual will give me more time with every player and to prepare them for the matches."
The hard-court tie will be played at a neutral venue due to security concerns in Venezuela.
The official draw for the time will be held on Friday, 17 September.
"I look forward to the energy of brainstorming with the younger generation and also hearing from the more experienced players in this team," Van Rensburg said.
"We will all learn from each other and I'm hoping we can play some amazing tennis."
SA team (singles / doubles world ranking):
Lloyd Harris (46/274)
Raven Klaasen (NA/23)
Ruan Roelofse (783/229)
Sipho Montsi (1 358/1 231)
Philip Henning (NA/NA)
Captain – Christo van Rensburg
Venezuela Team:
Ricardo Rodriguez (592/619)
Luis David Martinez (NA/99)
Brandon Perez (1 935/1 503)
Dimitri Badra (NA/NA)
Captain – Maurice Ruah
This entry was posted in Davis Cup, Davis Cup / Fed Cup and tagged Davis Cup, Lloyd Harris, South Africa.


text:
\section{Conclusion and Future Work}

In this paper, we develop a content-free scheme (OVCP) to detect clickbait videos on online video sharing platforms. Our scheme leverages the comment and interaction between users who watched the video, and learns latent features from their unstructured and complex comments. We evaluate our scheme using the real-world data collected from YouTube. The results demonstrate that our scheme is more accurate than both state-of-the-art clickbait detection tools and human annotators in identifying online clickbait videos.

Finally, we also outline a few future research directions that can be built on the work from this paper. First, clickbait videos on YouTube are often customized by experienced content creators who know how to game with the YouTube recommendation system. To make the OVCP scheme more robust and generalizable across different video platforms, we plan to extend OVCP's compatibility with other video sharing platforms using different commentary structures. Examples of such platforms include both video-centric platforms (e.g., Vimeo) and social-based platforms (e.g., Twitter).
Second, there exist many marketing vendors from which content creators can buy comments and high-retention views. In future work, we will further study how to identify and filter such fake or machine-generated comments. A possible solution is to leverage the social media bot detection techniques \cite{ferrara2016rise} and train a classifier that can discriminate comments generated by humans and bots. Meanwhile, we can also adopt the truth discovery \cite{zhang2018towards} and fact-checking \cite{wang2019age} approaches to verify the truthfulness of the user comments. The authors believe the above extensions will further improve the effectiveness and robustness of the OVCP scheme in detecting online clickbait videos.

\section{Evaluation} \label{sec:eval}
In this section, we first describe the dataset we collected from YouTube. We then evaluate the performance of the OVCP scheme in comparison with state-of-the-art baselines on the collected dataset. The results show that OVCP significantly outperforms both the compared baseline methods and human annotators in terms of accurately detecting online clickbait videos.

\subsection{Dataset}
\label{subsec:dataset}
YouTube is visited by over 1.9 billion logged-in users each month and over a billion hours of video are watched daily\footnote{\url{https://www.youtube.com/intl/en-US/yt/about/press/}}. In view of the diversity and popularity of \lanyu{YouTube videos}, we take YouTube as our data source to collect video information.

For the training dataset, considering the imbalanced nature of clickbait videos on YouTube, we leverage YouTube's recommendation system to efficiently collect clickbait videos being recommended to real users and to minimize human bias in the data collection process. First, we randomly assigned a set of trending videos recommended by YouTube to three independent human annotators who collectively identified a set of ``seed'' clickbait videos by manually watching them (we set the size of the seed set to be 40 in our experiment due to the high labeling cost). We then created a dummy account on YouTube and let YouTube recommend relevant videos based on the watching history of the ``seed'' videos. Using such a method, we collected a training dataset of 500 videos which is a reasonable sample size  to study the online video detection problem on YouTube \cite{bajaj2016disinformation}.
Note that, in the training set, we intentionally collect more clickbait videos so that the training set is relatively balanced. This allows us to train the model more effectively \cite{liu2008exploratory}. To ensure fairness, we use the same training data for all baselines. In order to evaluate the performance in a real-world scenario, we randomly collected 125 videos from the ``trending video" section \lanyu{on} YouTube's homepage. This strategy allows the collected videos to have a similar clickbait video percentage as that on YouTube due to its nature of random sampling. In particular, following the common practice in supervised machine learning  \cite{domingos2012few} and the Pareto principle (i.e., 80/20 rule)\footnote{\url{https://en.wikipedia.org/wiki/Pareto\_principle}} of training/testing data split, we selected 500 videos as the training set and 125 videos as the testing set, and performed 5-fold cross-validation in our evaluation.

For each of these videos, we recorded its unique \texttt{videoID} and collected the ground-truth label (i.e., whether the video is a clickbait or not) based on the majority voting of human annotations. We also collected the title, description,  thumbnail,  comments  and commentThreads~\footnote{An attribute that records the structure of each comment thread in the comment section}, and the content of each video through the YouTube Data API\footnote{\url{https://developers.google.com/youtube/v3/}}.
An overview of the collected dataset is summarized in Table \ref{table:datatrace}. We observe that the comments in the collected videos follow a ``long tail'' distribution. As shown in Figure \ref{fig:comment_count}, most comments in a thread have no replies (i.e., number of comments per thread is 1) and only a small portion of the threads have multiple comments.

\begin{table}[htb!]
    \caption{Data Trace Statistics}
    \label{table:datatrace}
    \small
    \centering
    \scalebox{0.88}{
    \begin{tabular}{l c c c c}
        \toprule
        \midrule
        \textbf{Dataset} &  \multicolumn{2}{c}{\textbf{Train}} & \multicolumn{2}{c}{\textbf{Test}}  \\
        \toprule
        \textbf{Video Label} &  \textbf{Clickbait} & \textbf{Non-clickbait} & \textbf{Clickbait} & \textbf{Non-clickbait}  \\
        \toprule
        Videos & 128 & 372 & 13 & 112\\
        \hline
        Comment Threads & 388,337 & 2,050,438 & 50,139 & 243,213 \\
        \hline
        Comments & 506,407 & 2,771,672 & 59,523 & 287,045\\
        \hline
        Comments per Thread & 1.30 & 1.35 & 1.19 & 1.18\\
        \hline
        Distinct Users & 419,392 & 2,072,927  & 51,998 & 246,481\\
        \hline
        Unique Words in Comments & 156,201 & 551,552 & 37,330 & 119,270 \\
        \midrule
        \toprule
    \end{tabular}
    }
\end{table}

\begin{figure}[!htb]
    \centering
    \includegraphics[width=0.8\linewidth]{histogram.pdf}
    \caption{Distribution of Comments Count per Thread}
    \label{fig:comment_count}
\end{figure}

\subsection{Baselines}
We integrate the following state-of-the-art supervised classifiers \cite{alpaydin2014introduction} with our scheme as discussed in Section~\ref{sec:classifier}: \textit{Logistic Regression (LR)}, \textit{Linear Support Vector Machine (SVM)}, \textit{AdaBoost}, \textit{Random Forest (RF)}, and \textit{Multi-layer Perceptron (MLP)}. We select the best-performed one to be the one used in our OVCP scheme.  We compare the performance of our scheme with the following representative baselines in clickbait detection \footnote{The online video clickbait detection problem is a largely unsolved research problem. Therefore, we only have a limited number of baselines from the literature to compare against.}.

\begin{itemize}
\item \textbf{Image-based Clickbait Detection (VGG-16):} A convolutional neural network approach (pre-trained on ImageNet) that detects the clickbait video using only the image content of the thumbnail \cite{zhang2015accelerating}.

\item \textbf{Stop Clickbait (ASONAM16):}  A linguistic-based clickbait classification approach that detects the clickbait video based on the title content of the video \cite{chakraborty2016stop}.

\item \textbf{Clickbait Detection using Deep Learning (NGCT16):} A deep neural network approach that identifies clickbait videos using a compiled clickbait corpus from social media posts\cite{agrawal2016clickbait}.

\item \textbf{Clickbait Video Detection (SPW18):} A deep learning based approach that detects clickbait video on YouTube using features from the headline, thumbnail, comments and video statistics \cite{zannettou2018good}.
\end{itemize}

\subsection{Detection Accuracy}
In the first set of experiments, we evaluate the performance of our scheme when it is coupled with a set of classifiers and identify the best-performed one as the OVCP scheme. The evaluation metrics involved in measuring the effectiveness of clickbait video detection include \textit{Accuracy}, \textit{Precision}, \textit{Recall}, and \textit{F1-Score}, which are commonly used in \lanyu{evaluating} binary classification tasks. In addition,  we include two imbalanced metrics, \textit{Cohen's Kappa (Kappa)} and \textit{Matthews Correlation Coefficient (MCC)}, because our dataset is not perfectly balanced (see Table~\ref{table:datatrace}). For all classification methods and baselines, we perform 5-fold cross-validation in the parameter tuning process.

In the  OVCP scheme, we set the parameters in each component primarily based on our empirical observation as follows: i) \textit{random walk}: we set $M=100$ and $K=5$;  ii) \textit{stacked autoencoder (network)}: we set the size of encoding layers to be 256, 64 and 16, and the size of decoding layers to be 64, 256 and 500; iii) \textit{Doc2vec}: the length of the comment embedding is set to be 256; and iv) \textit{stacked autoencoder (linguistic)}: we set the size of encoding layers to be 128, 64, 32 and 16, and the size of decoding layers to be 32, 64, 128 and 256.

The performance of all compared schemes is summarized in Table \ref{tab:classification}. We observe that AdaBoost performs the best among all classifiers and thus is selected to be the default classifier in the OVCP scheme. We also observe that our scheme outperforms all baseline approaches \lanyu{under} all evaluation metrics. In particular, comparing to the \textit{VGG-16}, \textit{ASONAM16}, \textit{NGCT16} and \textit{SPW18} baselines, our scheme achieves a performance gain of 0.3262, 0.2219, 0.1943 and 0.1229 on the F1-score respectively. Although the \textit{SPW18} approach considers various content-based features (e.g., headline, thumbnail, video statistics) in its model, it is found to be less effective than our scheme in detecting clickbait videos.  The reason is that the \textit{SPW18} method has a high-dimensional input vector size of 855 that requires a vast amount of training samples to achieve a reasonable performance. The \textit{SPW18} approach also relies on visual features (i.e., thumbnail) which can be misleading in detecting the clickbait videos.

\begin{table}[htb!]
  \small
  \centering
  \caption{Clickbait Classification Performance for All Methods}
  \scalebox{0.88}{

    \begin{tabular}{l c c c c c c}
    \toprule
    \midrule
    \textbf{Algorithms}  &\textbf{Accuracy}&\textbf{Precision}&\textbf{Recall}&\textbf{F1-Score}&\textbf{Kappa} & \textbf{MCC}\\
    \toprule
    AdaBoost (\textbf{OVCP}) & \textbf{0.8960 } & \textbf{0.5000} & \textbf{0.4615} & \textbf{0.4800} & \textbf{0.4223} &\textbf{ 0.4227} \\
    \cmidrule(l){1-7}
    LR & 0.8800 & 0.4286  & 0.4615 & 0.4444 & 0.3773  & 0.3776\\
    \cmidrule(l){1-7}
    SVM & 0.8880  & 0.4545 & 0.3846 & 0.4167 & 0.3552 & 0.3567 \\
    \cmidrule(l){1-7}
    RF  & 0.8640 & 0.3889 & 0.5385 & 0.4516 & 0.3763 & 0.3828 \\
    \cmidrule(l){1-7}
    MLP        & 0.8480 & 0.2857 & 0.3077 & 0.2963  & 0.2112 & 0.2114\\
    \cmidrule(l){1-7}
    \textbf{VGG-16} & 0.8240 & 0.1538 & 0.1538 & 0.1538 & 0.0556 & 0.0556 \\
    \cmidrule(l){1-7}
    \textbf{ASONAM16} & 0.8160 & 0.2222 & 0.3077 & 0.2581 & 0.1561 & 0.1588\\
    \cmidrule(l){1-7}
    \textbf{NGCT16} & 0.8400 & 0.2667 & 0.3077 & 0.2857 & 0.1961  & 0.1968\\
    \cmidrule(l){1-7}
    \textbf{SPW18} & 0.8560 & 0.3333 & 0.3846 & 0.3571 & 0.2765  & 0.2774\\
    \midrule
    \toprule
    \end{tabular}
    }
\label{tab:classification}
\end{table}

Additionally, we plot the Receiver Operating Characteristic (ROC) curve of all schemes to evaluate the robustness of their performance with respect to the classification threshold. As shown in Figure \ref{fig:roc}, our scheme consistently outperforms all baselines and achieves an increase of 0.09 on the AUC score compared to the baseline of best performance (i.e., NGCT16).

\begin{figure}[!htb]
    \centering
    \includegraphics[width=0.8\linewidth]{roc_all.pdf}
    \caption{ROC Curve of All Schemes}
    \label{fig:roc}
\end{figure}

\subsection{Feature Analysis}
In the second experiment, we study the importance of features in each category (i.e., network, linguistic, metadata) and their combinations. The results are shown in Table \ref{tab:classification_feature_set}. We observe that the combination of all features achieve the best results on all evaluation metrics which confirms their necessity in our model. We also observe that linguistic features achieve the best performance among all single feature categories, and the combination of linguistic and metadata features achieve the best performance among the combination of any two feature categories.

\begin{table}[htb!]

  \small
  \centering

  \caption{Classification Performance for All Feature Sets}
  \vspace{-0.1in}
  \scalebox{0.88}{

    \begin{tabular}{l c c c c }
    \toprule
    \midrule
    \textbf{Feature Set}  &\textbf{Accuracy}&\textbf{Precision}&\textbf{Recall}&\textbf{F1-Score} \\
    \toprule
    \textbf{All Features}& \textbf{0.8960 } & \textbf{0.5000} & \textbf{0.4615} & \textbf{0.4800} \\
    \cmidrule(l){1-5}
    \textbf{Metadata}  &0.7680 &0.1364 &0.2308 &0.1714  \\
    \cmidrule(l){1-5}
    \textbf{Network} &0.8240 &0.1538 &0.1538 &0.1538 \\
    \cmidrule(l){1-5}
    \textbf{Linguistic} &0.8480 &0.2000 &0.1538 &0.1739 \\
    \cmidrule(l){1-5}
    \textbf{Network \& Metadata} &0.8720 &0.2857 &0.1538 &0.2000 \\
     \cmidrule(l){1-5}
    \textbf{Linguistic \& Metadata } &0.8400 &0.2941 &0.3846 &0.3333\\
    \cmidrule(l){1-5}
    \textbf{Network \&Linguistic}  &0.8560 &0.2727 &0.2308 &0.2500 \\
    \midrule
    \toprule
    \end{tabular}
    }
\label{tab:classification_feature_set}
\end{table}

\subsection{Influence of Training Size}

In the third experiment, we further study the robustness of our scheme against the size of the training set. In particular, we evaluate the performance of the OVCP scheme and all other baselines by increasing the size of the training set from 30\% to 100\% of the entire \lanyu{training} set. The F1-score results are reported in Figure \ref{fig:trainsize}.  We observe that the performance of our scheme generally improves as the size of the training set increases and continues to outperform other baselines.

\begin{figure}[!htb]
    \centering
    \includegraphics[width=0.8\linewidth]{trainsize.pdf}
    \caption{Size of Training Set vs. Performance (F1 Score)}
    \label{fig:trainsize}
\end{figure}

\subsection{Detection Time}

In the fourth experiment, we measure the detection performance of the OVCP scheme against the time frame after the video is posted. Specifically, we limit the comment data in our scheme to be within a time frame from the corresponding video's publishing time, and increase the time frame from 10 minutes to 24 hours. The classification results are summarized in Table \ref{tab:classification_time}. We observe that the OVCP scheme using \lanyu{comments within} the first 10 minutes after the video's publication has already outperformed all content-based baselines with a non-trivial performance gain. Moreover, the OVCP scheme is observed to reach its optimal performance within 24 hours.

\begin{table}[htb!]
  \small
  \centering

  \caption{Classification Performance v.s. Detecting Time Frame}
  \vspace{-0.1in}
  \scalebox{0.88}{

    \begin{tabular}{l c c c c }
    \toprule
    \midrule
    \textbf{Time Frame}  &\textbf{Accuracy}&\textbf{Precision}&\textbf{Recall}&\textbf{F1-Score} \\
    \toprule
    \textbf{OVCP (Within 10 Minutes)}  & 0.8640 & 0.3571 & 0.3846 & 0.3704 \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (Within 30 Minutes)}  & 0.8560 & 0.3684 & 0.5385 & 0.4375  \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (Within 1 Hour)}  & 0.8640 & 0.3889 & 0.5385 & 0.4516 \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (Within 6 Hours)} & 0.8720 & 0.4118 & 0.5385 & 0.4667 \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (Within 12 Hours)} & 0.8880 & 0.4615 & 0.4615 & 0.4615 \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (Within 24 Hours)} & 0.8960 & 0.5000 & 0.4615 & 0.4800 \\
    \cmidrule(lr){1-5}
    \textbf{OVCP (All Comments)}  &\textbf{0.8960 } & \textbf{0.5000} & \textbf{0.4615} & \textbf{0.4800} \\
    \cmidrule(lr){1-5}
    \textbf{VGG-16} & 0.8240 & 0.1538 & 0.1538 & 0.1538  \\
    \cmidrule(lr){1-5}
    \textbf{ASONAM16} & 0.8160 & 0.2222 & 0.3077 & 0.2581 \\
    \cmidrule(lr){1-5}
    \textbf{NGCT16} & 0.8400 & 0.2667 & 0.3077 & 0.2857 \\
    \cmidrule(lr){1-5}
    \textbf{SPW18} & 0.8560 & 0.3333 & 0.3846 &0.3571\\
    \midrule
    \toprule
    \end{tabular}
    }
\label{tab:classification_time}
\end{table}

\subsection{Detection Efficiency}

In the fifth experiment, we empirically measure the efficiency (i.e., detection time per video) of all schemes. The results are shown in Figure~\ref{fig:detectiontime}. We observe that the OVCP scheme achieves the best detection performance (F1 score) with a reasonable detection time compared to other baselines. We also observe that the SPW18 approach is the slowest among all compared schemes. This is because SPW18 requires features to be extracted from multiple data modalities, including image (i.e., thumbnail), texts (i.e., video title and description) and metadata. Such a large feature space significantly reduces the detection efficiency. The NGCT16 has the least detection time since it simply relies on pre-trained word embeddings to predict clickbait videos via a deep learning framework, which in turn limits its detection accuracy.

\begin{figure}[!htb]
    \centering
    \includegraphics[width=0.78\linewidth]{detection_time.pdf}
    \caption{Performance (F1 Score) v.s. Average Detection Time Cost (per Video)}
    \label{fig:detectiontime}
\end{figure}

Finally, we study the computational complexity/scalability of our OVCP scheme. In particular, we perform an empirical study of different modules of the OVCP scheme for a detailed analysis. The results are shown in Figure~\ref{fig:computingtime}. We observe that the linguistic and network feature extraction modules are the two dominant ones that consume most of the execution time of the OVCP scheme. The reason is that these dominant modules include neural network solutions (i.e., doc2vec and autoencoder) for latent feature extraction, which often involve a non-trivial amount of matrix operations in both learning and inference phases of the process \cite{hertz2018introduction}. However, we observe the computation time of those two dominant modules increase ~\emph{linearly} as the size of the input data increases, which demonstrates the good scalability of our scheme with larger datasets.

We also note that, in our current implementation, the videos are detected in a sequential manner, i.e., one video is detected after another. This leads to the linear growth of the execution time as the number of videos becomes large. We observe that our OVCP scheme can be easily extended to further improve its speed by leveraging the parallel GPU programming frameworks such as CUDA\footnote{\url{https://developer.nvidia.com/cuda-zone}} to execute many video streams in parallel on thousands of GPU cores. Moreover, elastic distributed computing systems such as AWS Kubernetes\footnote{\url{https://kubernetes.io/}} can also allow new OVCP instances to spin up on virtual machines when the system is overloaded (e.g., when many new videos are uploaded). Considering this line of effort is beyond the scope of this paper, we plan to implement it in our future work.
\begin{figure}[!htb]
    \centering
    \includegraphics[width=0.8\linewidth]{computing_time.pdf}
    \caption{Computation Time for All Modules of OVCP}
    \label{fig:computingtime}
\end{figure}

\subsection{Human Performance Comparison}

In the last experiment, we compare the performance of OVCP with human annotators\footnote{The difference between the human annotators in this experiment and the human annotators we used in collecting the ground truth labels is that we did not allow the human annotators in this experiment to watch the actual videos before they generate the annotations.}. In particular, we invited three independent annotators (different from the ones who were invited to generate the ground truth labels) to identify clickbait videos from the same testing dataset used for the OVCP scheme. First, we asked these annotators to annotate videos by only showing them the title, thumbnail and view count of each video which \lanyu{are} exactly the same information a YouTube user receives \textit{before} clicking a video. Next, we asked these annotators to annotate the videos by also giving them access to the comment section of the videos (i.e., metadata and comment features).
The performance of each individual annotator and the aggregated results using the majority voting (i.e., ``overall'' and ``overall+comment'') are shown in Table \ref{tab:human}. We also report false positive rate (FPR), and false negative rate (FNR) in addition to accuracy and F1 score metrics to study what kinds of mistakes the human annators made. We observe that the OVCP scheme consistently outperforms human annotators in detecting clickbait videos, demonstrating the necessity of developing such a tool. We also observe the human annotators often perform worse when they are not allowed to access the comments of the video, especially in terms of false negative rate (\lanyu{i.e.,} missing many clickbait videos).

\begin{table}[htb!]
  \small
  \centering
  \caption{Comparison between OVCP and Human Performance}
  \vspace{-0.1in}
  \scalebox{1}{

    \begin{tabular}{l c c c c }
    \toprule
    \midrule
     &\textbf{Accuracy}&\textbf{F1-Score}&\textbf{FPR}&\textbf{FNR}\\
    \toprule
    \textbf{OVCP} & \textbf{0.8960} & \textbf{0.4800} & \textbf{0.0536} & \textbf{0.5385}  \\
     \cmidrule(l){1-5}
     Annotator 1 (without comments) & 0.7040 & 0.0976 & 0.2321 & 0.8462 \\
     \cmidrule(l){1-5}
     Annotator 1 (with comments)    & 0.7440 & 0.1579 & 0.1964 & 0.7692 \\
     \cmidrule(l){1-5}
     Annotator 2 (without comments) & 0.6880 & 0.1333 & 0.2589 & 0.7692 \\
     \cmidrule(l){1-5}
     Annotator 2 (with comments)  & 0.7360 & 0.1951 & 0.2143 & 0.6923   \\
     \cmidrule(l){1-5}
     Annotator 3 (without comments) & 0.7520 & 0.1143 & 0.1786 & 0.8462  \\
     \cmidrule(l){1-5}
     Annotator 3 (with comments)  & 0.7920 & 0.1875 & 0.1429 & 0.7692  \\
     \cmidrule(l){1-5}
     Overall (without comments) & 0.7280  & 0.1053  & 0.2054  & 0.8462  \\
     \cmidrule(l){1-5}
     Overall (with comments) & 0.7680  & 0.1714  & 0.1696 & 0.7692  \\
    \midrule
    \toprule
    \end{tabular}
    }
\label{tab:human}
\end{table}

\section{Introduction}
\label{sec:intro}

In the age of instant gratification, people are increasingly consuming more video contents from the Internet (e.g., YouTube\footnote{https://www.youtube.com}, Vimeo\footnote{https://vimeo.com/watch}) than cable networks~\cite{groupm2018}. The time people spend on online media is expected to surpass the time they spend on traditional TV worldwide in 2019 \cite{recode2019}. For example, YouTube has over a billion users covering almost one-third of the Internet population and reaches billions of views per day \footnote{https://www.youtube.com/yt/about/press/}.
An online video usually includes \lanyu{the} title, \lanyu{the} thumbnail, and the video content. The title and thumbnail are visible to the viewers ~\emph{before} they click the video and are found to be the crucial factors that may attract users to click and watch a video.
\lanyu{Clickbait} video, whose content clearly deviates from its title/thumbnail, is generated to entice viewers to click \lanyu{the} video and boost the viewership of the video consequently \cite{bartl2018youtube}.
However, the spread of clickbait videos not only wastes the time of viewers but also decreases the trustworthiness of video-sharing platforms.
In this paper, we focus on the problem of reliably detecting clickbait videos \lanyu{on} online video-sharing platforms.

The detection of clickbait videos requires a careful investigation of the relationship between the title/thumbnail and the video content. This problem cannot be fully addressed by current content-based solutions which only focus on the text of the title \cite{rony2017diving, wang2015social}, the image of the thumbnail \cite{zeiler2014visualizing, zhang2018opinion}, or the content of the video \cite{papadopoulou2017web, zhang2018streamguard}.
For example, several text-based clickbait detection techniques have been developed to identify clickbait from social media posts (e.g., the clickbait news headline detection using word embeddings \cite{rony2017diving}, \lanyu{the} clickbait tweets detection using the linguistic feature analysis \cite{potthast2016clickbait}).
However, those solutions cannot be adopted to address the video clickbait detection problem because the content of the title may not be a reliable indicator to identify a clickbait video. For example, both videos shown in Figure \ref{fig:example_title} share the same title. The video shown in Figure \ref{fig:example_title_clickbait} is a clickbait because it does not deliver the content the viewers expect to see (e.g., a plane with 13 engines on each wing as shown in its thumbnail). In contrast, Figure \ref{fig:example_title_nonclickbait} is a non-clickbait video and the video does present the ten largest airplanes in the world including the one shown in the thumbnail.

\begin{figure}[!htb]
    \centering
    \subfigure[][Clickbait]{
        \centering
        \includegraphics[width=0.4\linewidth]{example_clickbait_big_plane.pdf}
        \label{fig:example_title_clickbait}
    }
    \hspace{0.1in}
    \subfigure[][Non-clickbait]{
        \centering
        \includegraphics[width=0.4\linewidth]{example_nonclickbait_big_plane.pdf}
        \label{fig:example_title_nonclickbait}
     }
    \caption{Examples of Clickbait and Non-clickbait Video with Similar Titles}
    \label{fig:example_title}
\end{figure}

Similarly, image-based approaches that only focus on thumbnail features (e.g., convolutional method \cite{zeiler2014visualizing}, self-consistency \cite{huh2018fighting}) cannot solve the video clickbait detection problem.
For example, Figure \ref{fig:example_thumbnail} shows two videos with very similar thumbnails. The video \ref{fig:example_thumbnail_clickbait} in Figure \ref{fig:example_thumbnail} is a clickbait because the video only presents some irrelevant tricks, such as how to plot letters ``L'', ``O'', ``V'', ``E'' with math functions, and never explains the equation $2+2=5$.
In contrast, the video shown in Figure \ref{fig:example_thumbnail_nonclickbait} is a non-clickbait as its content explicitly discusses some tricky methods to derive the equation $2+2=5$ (e.g., $2\times 0+2 \times 0=5\times 0$).

Moreover, video authentication and verification solutions have been developed to identify fake online videos \cite{papadopoulou2017web} and AI-generated videos \cite{li2018ictu}. However, these solutions only focus on the video content itself and ignore the information exposed to viewers before the video is clicked (e.g., title \lanyu{and} thumbnail). For example, an old movie can be uploaded under a title/thumbnail \lanyu{that is} identical or similar to a recently released one. Such a clickbait video can easily bypass the video-based detection systems as the video content is real \cite{fagan2018}. Therefore, a reliable online clickbait video detection tool that explicitly considers the relationship between the title/thumbnail and the video content has yet to be developed.

\begin{figure}[!htb]
    \centering
    \subfigure[][Clickbait]{
        \centering
        \includegraphics[width=0.4\linewidth]{example_clickbait.pdf}
        \label{fig:example_thumbnail_clickbait}
    }
    \hspace{0.1in}
    \subfigure[][Non-clickbait]{
        \centering
        \includegraphics[width=0.4\linewidth]{example_non_clickbait.pdf}
        \label{fig:example_thumbnail_nonclickbait}
    }
    \caption{Examples of Clickbait and Non-clickbait Video with Similar Thumbnails}
    \label{fig:example_thumbnail}
\end{figure}

In this paper, we develop a novel content-agnostic scheme, \textit{Online Video Clickbait Protector (OVCP)}, to effectively detect clickbait videos whose titles/thumbnails are deviating from the video content. OVCP is content-agnostic in the sense that it does not analyze the content information of a video (i.e., title, thumbnail, video clips). Instead, OVCP investigates the comments and discussions from online users who \lanyu{have} watched the video. Our approach is motivated by the observation that users usually make complaints or sarcastic comments when they watch clickbait videos (e.g., ``My body is crying for help watching this video'' in a clickbait video titled ``28 SIGNS YOUR BODY IS CRYING FOR HELP'').
Additionally, users are observed to be less interactive in the discussion of clickbait videos than non-clickbait ones given the fact that the content of clickbait videos often appears to be tedious to the users~\cite{marketing2018}.  The OVCP scheme explores the topological and semantic structure of the user comment networks and the latent linguistic features of user comments to identify the unique characteristics of clickbait videos. OVCP is then integrated with a set of state-of-the-art supervised classifiers to detect clickbait videos.

To the best of our knowledge, OVCP is the first content-agnostic approach to address the online clickbait video detection problem.
OVCP is robust against \lanyu{sophisticated content creators} who often generate clickbait videos that can bypass the current clickbait detectors. The evaluation results on a real-world dataset collected from YouTube show that our OVCP scheme accurately detects clickbait videos and significantly outperforms state-of-the-art baselines and human annotators.

\section{Problem Definition}
\label{sec:problem}
In this section, we present the online clickbait video detection problem. First, we define a few key terms that will be used in the problem formulation.

\begin{Dfn}
\textbf{Video ($V_i$):} it is a publicly available video instance uploaded to the online sharing platforms (Figure~\ref{fig:example1}). Each video, denoted as $V_i$, contains five elements: title ($T_i$), thumbnail ($M_i$), description ($D_i$), comments ($C_i$), and the ground truth label ($z_i$) of the video. Formally, $V_i$ is denoted as a quintuple $V_i = (T_i, M_i, D_i, C_i, z_i)$. Additionally, we define a video set of $N$ videos as $\mathcal{V} = \{ V_1, V_2, \cdots, V_N\}$.
\end{Dfn}

\begin{figure*}[!htb]
 \centering
    \subfigure[][Title and Thumbnail]{
         \centering
         \includegraphics[width=0.35\textwidth]{example2_thumbnail0.pdf}
         \label{fig:example-title-thumbnail}
     }
     \hspace{0.1in}
     \subfigure[][Description]{
         \centering
         \includegraphics[width=0.57\textwidth]{example2_title.pdf}
        \label{fig:example-description}
     }
     \subfigure[][Sample Comments]{
         \centering
         \includegraphics[width=0.8\textwidth]{example2_comment.pdf}
        \label{fig:example-comments}
     }
     \caption{Example of an Online Video and Its Components}
     \label{fig:example1}

 \end{figure*}

\begin{Dfn}
\textbf{Title ($T_i$)}: it is a piece of brief textual information provided by the video uploader to describe the video content. An example of a video title is shown in Figure \ref{fig:example-title-thumbnail}.
\end{Dfn}

\begin{Dfn}
\textbf{Thumbnail ($M_i$):} it is an image that provides a visual description of the video content. YouTube video creators are allowed to choose a thumbnail generated automatically by the YouTube algorithm, which is typically a frame from the video, or to upload a customized image. An example of a video thumbnail is shown in Figure \ref{fig:example-title-thumbnail}.
\end{Dfn}

\begin{Dfn}

\textbf{Description ($D_i$):} it includes the description provided by the video creator, and the video's meta information (e.g., number of views, number of likes). An example of a video description is shown in Figure \ref{fig:example-description}.
\end{Dfn}

\begin{Dfn}
\textbf{Comments ($C_i$):} they include all the comments and reviews of a video from its viewers. An example of the comment section of a video is shown in Figure \ref{fig:example-comments}.
\end{Dfn}

\begin{Dfn}
\textbf{Clickbait Video (labeled as ``true"):} a video is defined as \textit{clickbait} if its title and/or thumbnail is intentionally crafted to attract viewers' attention, and entice them to click the accompanied video whose content clearly deviates from the title and/or thumbnail.
\end{Dfn}

\begin{Dfn}

\textbf{Non-clickbait Video (labeled as ``false"):} any video that is not in the clickbait video category.

\end{Dfn}

Based on the above definitions, the clickbait video detection is a \emph{binary classification problem} that targets at classifying each video into two categories, i.e., clickbait or non-clickbait. The problem is formulated as:
\begin{equation}
    \arg \max_{\Tilde{z_i}} Pr(\Tilde{z_i} = z_i|V_i) ,~  \forall 1 \le i \le N
\end{equation}
\noindent
where $\Tilde{z_n}$ denotes the estimated label for video $V_n$.

\section{Related Work}

\subsection{Clickbait Detection}

Our work bears some resemblance with literature on clickbait detection on social media platforms. The term clickbait was coined as ``exaggerated headlines whose main motive is to mislead the reader to click on them" \cite{agrawal2016clickbait}. The problem of clickbait has been widely studied and many solutions were provided~\cite{potthast2016clickbait,potthast2018crowdsourcing,anand2017we}. The first automatic clickbait detector was proposed in \cite{potthast2016clickbait}, where a set of handcrafted features (e.g., bag-of-words, n-grams and number of hashtags) have been selected to train a clickbait classifier.  The follow-up solutions improve this feature engineering approach by developing deep neural network based approaches to automatically detect clickbaits without cherry picking the features \cite{anand2017we,thomas2017clickbait}. Unfortunately, these schemes rely on the analysis of the textual content (i.e., titles and descriptors) to detect clickbaits and cannot effectively address our problem because both clickbait videos and non-clickbait videos can share very similar titles and descriptors as shown in Figure \ref{fig:example_title}.

We found there exist very few clickbait detection solutions that focus on video-based clickbaits. In a very recent work,   Qu \emph{et al.} developed a crowdsourcing-based approach where human annotators are invited to label whether the thumbnail of a video is clickbait or not \cite{qutowards}. This work requires a significant amount of human labor and can be time-consuming if the labeled dataset is large. The most relevant work was proposed by Zannettou \textit{et al.}, who developed a deep neural network model  to combine the features of the thumbnails and the statistical features of users' comments \cite{zannettou2018good}. However, this approach relies on the direct analysis of the thumbnails which has been shown to be an unreliable indicator of clickbait videos (Figure \ref{fig:example_thumbnail}).
Moreover, the scheme assumes all the videos posted by the same user were clickbaits which is an oversimplified assumption for the real-world scenarios where sophisticated users exist. In contrast, the proposed OVCP scheme is the first \emph{content-agnostic} approach to address the clickbait video detection problem on video sharing platforms.

\subsection{Misinformation Detection}
The spread of misinformation on online social media has received a significant amount of attention in recent years \cite{zhang2018towards,vo2018rise,R33,wang2014using,wang2013recursive,zhang2018scalable}.  There exist two categories of misinformation detection problems that are related to ours. The first category is called \emph{fake news detection} where \emph{textual content} (e.g., news articles and social media posts) is analyzed to verify whether the news statement is truthful or not.  For example, Vo \textit{et al.} developed a fake news detection scheme that can identify a group of users who actively debunk fake information on social media and recommend fact-checking URLs posted from these users \cite{vo2018rise}. Wang \emph{et al.} developed an estimation theoretical scheme that identifies truthful online social media posts by explicitly considering the reliability of data sources and source dependency~\cite{R1}. The second category is commonly referred to as ``image forgery detection" schemes. For example, Zhang \emph{et al.} developed a novel fauxtography detector that can effectively track down misleading images on social media (e.g., Twitter, Reddit)  \cite{zhang2018fauxbuster}. Huynh-Kha \emph{et al.} developed an image forgery detection scheme that can detect whether an image is manually edited by copy-move, splicing or both in the same image \cite{huynh2016robust}.

However, the above solutions are insufficient to address the clickbait video detection problem in this paper because sophisticated uploaders can often create clickbait videos by using credible titles and non-edited images to bypass the detection systems~\cite{zannettou2018good}. In this work, we propose a more robust content-agnostic approach that leverages the audience's comments to detect clickbait videos rather than \lanyu{relies} on the analysis of textual descriptors or thumbnails.

\subsection{Network Embedding for Comment Feature Extraction}

Extracting topological and semantic information from the comment network is a key technique used in the proposed OVCP scheme. Various relevant network embedding techniques have been proposed \cite{wang2018shine,grover2016node2vec,perozzi2014deepwalk,huang2017label,zhang2018risksens}. For example, DeepWalk uses short random walks to learn the latent representations to describe the topological structure of a network  \cite{perozzi2014deepwalk}.  Node2vec designs a biased random walk procedure to learn the representation of a network that maximizes the likelihood of preserving network neighborhoods of a node \cite{grover2016node2vec}. These classical methods focus on capturing the topological structure of the nodes while ignoring the attributes of the edge. In a more recent study, Wang \emph{et al.} developed SHINE, a network embedding technique that can capture the positive/negative emotions of the edge in a network \cite{wang2018shine}. Huang \emph{et al.} further extended the effort by proposing LANE, a general network embedding technique designed for attributed networks \cite{huang2017label}. Compared to SHINE, LANE is able to capture general categorical attributes on the edges of a network. Different from the above methods, our work encodes the unique sentiment and endorsement features of the user comment network for the purpose of detecting clickbait videos.

\section{Solution}
\label{sec:solution}
In this section, we present the \textit{Online Video Clickbait Protector (OVCP)} to address the problem formulated in the previous section. Our scheme consists of four major components as shown in Figure \ref{fig:architecture}. This first component is a \textit{Network Feature Extraction} module that extracts topological and semantic features from the audience interactive discussions and comments of a video.  The second component is a \textit{Linguistic Feature Extraction} module that learns the vector representations of user comments and extracts linguistic features of the comments (e.g., document embeddings). The third component is a \textit{Metadata Feature Extraction} module that extracts auxiliary metadata features of the video (e.g., number of views, number of likes).  The last component is a \textit{Supervised Classification} module that identifies the clickbait videos using the features extracted from the first three components.

\begin{figure}[!htb]
    \centering
    \includegraphics[width=\linewidth]{architecture.pdf}
    \caption{An Overview of OVCP}
    \label{fig:architecture}
\end{figure}

\subsection{Network Feature Extraction} \label{sec:network}

The network feature extraction component in OVCP is designed to capture characteristics from the audience's comments of an online video. We observe that clickbait and non-clickbait videos are different in terms of topological features (e.g., structure of comment threads) and semantic features (e.g., sentiments, endorsements) of audience's comments. To effectively capture both topological and semantic features, the network feature extraction component constructs a comment network, records the network attributes by a Random Walk scheme, and learns the vector representation via an autoencoding approach.

\subsubsection{Comment Network Construction for Individual Video}

We first construct a network of user comments that represents the topological structure and semantic attributes/features of the user comments. A \emph{comment network $\mathbf{G}$} for each video is defined as a directed graph $\mathbf{G} = (\mathbf{V}, \mathbf{E})$, where $\mathbf{V}$ is the set of nodes and $\mathbf{E}$ is the set of directed edges between nodes. We define a source node $s \in \mathbf{V}$ to be the description of the video, and other nodes (i.e., $v \in \mathbf{V}, v \neq s$) to be all comments of the video. Each edge $(v,v') \in \mathbf{E}$ denotes a reply from comment $v$ to comment $v'$.

Different from other trending social media platforms (e.g., Reddit) that have a multi-level comment structure, YouTube only allows a two-level commentary mechanism, namely a top-level comment and its replies (i.e., second-level comments to the top-level comment). In other words, each comment thread is recorded as the top-level comment followed by all of its replies in a flat structure.  To retrieve the comment network structure, in each comment thread, we connect the top-level comment node to the source node $s$ and direct all second-level comment nodes to their top-level comment node. When a specific user (e.g., a username following a ``+'' or ``@'' ) is mentioned in a second-level comment, the comment node is redirected to the latest comment node of the mentioned user in the same thread. Note that a comment can receive any number of replies but can only reply to one comment. In other words, each node $v$ can have any number of incoming edges but only one outgoing edge.

Figure \ref{fig:network} demonstrates the networks constructed from the comments of a clickbait video and a non-clickbait video. We observe that the comment network of a clickbait video has a few ``hub" comment nodes that receive more replies than normal comment nodes. For example, a comment like ``Who else come for the thumbnail?'' receives a large number of replies from other viewers who suffer from the same clickbait video. We also observe that non-clickbait videos contain longer comment threads since users are more likely to discuss the topic presented in the video through interactive replies if the video is not a clickbait.

\begin{figure*}[!htb]
\centering
     \subfigure[][Clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_clickbait.pdf}
         \label{fig:network-clickbait}
     }
     \subfigure[][Non-clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_nonclickbait.pdf}
        \label{fig:network-nonclickbait}
     }
     \caption{Examples of the Comment Network Structure  for Clickbait and Non-clickbait Videos  Note: for better visualization, we only keep threads with more than one comments in the plot.}
     \label{fig:network}`
 \end{figure*}

\subsubsection{Semantic Feature Extraction with Random Walks}

After the comment network $\mathbf{G}$ of a video is constructed, we further extract the semantic features (i.e., sentiment and endorsement) from the comment network. We observe that such semantic features of a comment can sometimes reveal the user's attitude and behavior towards a possible clickbait video. Therefore, it makes sense to incorporate them into our OVCP scheme.  In particular, we adopt a Random Walk (RW) algorithm \cite{grover2016node2vec} in the constructed network to capture these two semantic features.

A random walk $RW(M, K)$ scheme often traverses a graph $M$ times while limiting the number of steps in each traverse path to be at most $K$ \cite{perozzi2014deepwalk}. In the OVCP scheme, we trace not only the reply direction of a comment node but also the attributes of the comment node. Intuitively, in each traversal of the comment network $\mathbf{G}$, we randomly select a comment node and record its path until it reaches the source node or the length of the path reaches $K$.
We define the two attitude paths below to track the semantic features (i.e., sentiment and endorsement) of each comment node in the RW process.  In particular, we define two semantic features as attributes of each comment node $v$: i) \emph{sentiment attribute $\alpha_{s}(v)$} is defined as the \emph{polarity} score extracted by a sentiment analysis tool TextBlob\footnote{https://textblob.readthedocs.io/}, and ii) \emph{endorsement attribute $\alpha_{e}(v)$} is defined as the number of likes a comment received from other users.

\begin{Dfn}
\textbf{Sentiment Path ($RW_{s}$):} is the random walk process that traverses the graph $\mathbf{G}$ from a randomly selected comment node $v_0$ and records the \textit{sentiment} attribute $\alpha_{s}$ of each comment node on the path. In each step, the random walk process follows the direction to the next comment node that the current comment node replies to. Formally, for the $m^{th}$ walk in the process, $RW_{s}(m) = \{RW_{s}(m, 0), RW_{s}(m, 1), \cdots, RW_{s}(m, K-1)\}$ where $RW_{s}(m, k) = \alpha_{s}(v_k)$ represents the sentiment attribute of the $k^{th}$ node $v_k$ on the path.
\end{Dfn}

The Sentiment Path $RW_{s}$ captures the sentiment feature of the comment network. Figure \ref{fig:network-color} demonstrates an example of the sentiment attribute in the comment network.  We observe that the comment sentiments from non-clickbait videos are more diverse than clickbait videos. This is because the comments from viewers of non-clickbait videos primarily focus on the content of the video and usually reflect the diversified attitudes (e.g., like vs. dislike) towards the video. Another reason is that viewers often leave a clickbait video immediately without making any comment. Such rational user behavior reduces not only the number of comments a clickbait video receives but also its likelihood of getting diversified comments.

\begin{figure*}[!htb]
\centering
     \subfigure[][Clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_clickbait_color.pdf}
         \label{fig:network-clickbait-color}
     }
     \subfigure[][Non-clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_nonclickbait_color.pdf}
        \label{fig:network-nonclickbait-color}
     }
     \caption{Sentiment Feature Path. The color of each node represents the sentiment attribute of the corresponding comment, i.e., \textit{red} - positive, \textit{blue} - negative, \textit{grey} - neutral.}
     \label{fig:network-color}
 \end{figure*}

\begin{Dfn}
\textbf{Endorsement Path ($RW_{e}$):} is the random walk process that traverses the graph $\mathbf{G}$ from a randomly selected comment node $v_0$ and records the \textit{endorsement} attribute $\alpha_{e}$ of each comment node on the path. In each step, the random walk process follows the direction to the next comment node that the current comment node replies to. Formally, for the $m^{th}$ walk in the process, $RW_{e}(m) = \{RW_{e}(m, 0), RW_{e}(m, 1), \cdots, RW_{e}(m, K-1)\}$ where $RW_{e}(m, k) = \alpha_{e}(v_k)$ represents the endorsement attribute of the $k^{th}$ node $v_k$ on the path.
\end{Dfn}

The Endorsement Path $RW_{e}$ captures the endorsement a comment receives in the comment network. It reflects the agreement from other users on a particular comment. Figure \ref{fig:network-size} demonstrates an example of the endorsement attribute in the comment network. We observe that only a few comments in clickbait videos receive a large amount of endorsements from other users. We find these comments are often the ones that point out the video is a clickbait, which are appreciated/endorsed by other users. The degree of endorsements in non-clickbait videos is much more diversified since users are more engaged in the discussion of the video content instead of endorsing or disputing it.

\begin{figure*}[!htb]
\centering
     \subfigure[][Clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_clickbait_size.pdf}
         \label{fig:network-clickbait-size}
     }
     \subfigure[][Non-clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{network_nonclickbait_size.pdf}
        \label{fig:network-nonclickbait-size}
     }
     \caption{Endorsement Feature Path. The size of each node represents the endorsement attribute of the corresponding comment, i.e., the number of likes a comment received.}
     \label{fig:network-size}
 \end{figure*}

We perform the random walk process $M$ times with a maximum length of $K$ for each attribute path, and denote each path with $RW_{s}(m)$ and $RW_{e}(m)$ for the $m^{th}$ walk of the \textit{sentiment} and \textit{endorsement} attribute respectively. If attribute paths $RW_s(m)$ and $RW_e(m)$ arrive at the source node $s$ before the length of the path reaches $K$, i.e., $v_k = s$ and $k < K$, $RW_s(m)$ and $RW_e(m)$ are padded with neutral sentiment and zero endorsement respectively. We store the $K$ attributes recorded in $M$ random walk paths into feature matrices ($H_s$ and $H_e$) of size $M \times K$, where $H_{s}(m, k) = RW_{s}(m, k)$ and $H_{e}(m, k) = RW_{e}(m, k)$.

\subsubsection{Network Representation Learning}

Given the high-dimensional network features of the comment network $\mathbf{G}$, namely $H_{s}$ and $H_{e}$ extracted by the Random Walk algorithm, we further encode the network features into a low-dimensional vector space and learn the latent vector representation for them. An autoencoder is a generative deep learning technique that captures hidden patterns of high dimensional data with artificial neural networks \cite{vincent2008extracting}. It consists of an \textit{encoder} $E(x): \mathbb{R}^n \rightarrow \mathbb{R}^m$ that reduces the feature space dimension from $n$ to $m$, where $m \ll n$. The reduction is done through one or more layers of neural networks, and a \textit{decoder} $D(z): \mathbb{R}^m \rightarrow \mathbb{R}^n$ that recovers the encoded vector to its original dimensionality.

It has been shown that deep autoencoder is an effective way of nonlinear dimensionality reduction \cite{hinton2006reducing}. In our scheme, we compress the network feature vectors $H_{s}$ and $H_{e}$ by independently training two stacked autoencoders with respect to the sentiment and endorsement attributes. Formally, our stacked autoencoder structure consists of six neural network layers, and the $i^{th}$ layer is defined as:
\begin{equation}
    \mathbf{Z}^i = \phi (\mathbf{W}^i \cdot \mathbf{X}^i + \mathbf{b}^i)
\end{equation}
where $\phi(\cdot)$ is a nonlinear activation function, $\mathbf{W}^i$ is the weighting factor and $\mathbf{b}^i$ is the bias term. $\mathbf{X}^i$ and $\mathbf{Z}^i$ are the input and output of each layer. We set the input of the first layer to be the network feature vectors $H_{s}$ and $H_{e}$ for the sentiment and endorsement autoencoder, respectively. We use Mean Square Error (MSE) as the loss function for the stacked autoencoder and the rectified linear unit (ReLU) as the activation function.

Finally, the latent features (i.e., $\mathbf{Z}_{s}$ and $\mathbf{Z}_{e}$) learned from the stacked autoencoders are aggregated by a concatenation function, $\mathbf{Z} = <\mathbf{Z}_{s}, \mathbf{Z}_{e}>$, which represents the network features extracted from the comment network.

\subsection{Linguistic Feature Extraction}\label{sec:linguistic}

In the second component, we extract linguistic features from the comment section by learning the document embedding of each comment. Figure \ref{fig:wordcloud} shows the word clouds of most frequent words from the comments of clickbait and non-clickbait videos, respectively. Intuitively, we observe that clickbait related words (e.g., ``clickbait", ``bait", ``fake'', ``thumbnail") appear more frequently in the comments of clickbait videos while content-related words (e.g., ``lava'', ``card'', ``voice'') appear more often in the discussion of non-clickbait videos.

\begin{figure*}[!htb]
\centering
     \subfigure[][Clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{wordcloud_clickbait.pdf}
         \label{fig:worldcloud-clickbait}
     }
     \subfigure[][Non-clickbait]{
         \centering
         \includegraphics[width=0.42\textwidth]{wordcloud_nonclickbait.pdf}
        \label{fig:worldcloud-nonclickbait}
     }
     \caption{Word Clouds}
     \label{fig:wordcloud}

 \end{figure*}

Therefore, we employ a widely adopted document embedding technique, namely Doc2vec \cite{Le2014doc2vec}, to extract linguistic features from comments (i.e., comment embedding). Doc2vec, derived from the famous Word2vec framework, is designed to learn fixed-length continuous distributed vector representations for word sequences of variable-length.

A na\"ive approach is to simply embed the whole comment section of each video. However, we found such an approach performs poorly due to the extremely long or short comment length of some videos.  In particular, we found a majority of comment threads only have one comment, but some threads have hundreds of comments. To capture the information of both short and long comment threads, we design a hierarchical embedding process as follows: 1) we first embed each single comment using the Doc2vec model; 2) each long comment thread is traversed using the Random Walk process and the recorded comment path is further embedded by calculating the mean of comment embeddings in the path (referred to as \textit{path embedding}); 3) we train another independent 8-layer stacked autoencoder to reduce the dimension of the path embeddings. Using this hierarchical embedding method, we can control the dimensionality of the linguistic feature space while preserving the information captured by the Doc2vec framework.

\subsection{Metadata Feature Extraction} \label{sec:meta}

 We further extract some complementary metadata features that are relevant in identifying clickbait videos but cannot be captured by \lanyu{the} network and linguistic clues discussed above. These metadata features are mainly selected based on empirical observations. For example, we observe that some clickbait videos contain URLs to malicious websites in the video descriptors.  We extract a total of 13 metadata features from the collected real-world dataset. These metadata features are shown in Table \ref{tab:metafeatures}. These features are computed to describe statistical characteristics of both the video content (e.g., video length, the number of views) and the comments of the video (e.g., the word count of a comment, the number of likes of a comment).  The correlation plot of all the metadata features is shown in Figure \ref{fig:correlation}. We observe that the extracted metadata features are relatively independent.

\begin{table}[htb!]
    \small
    \caption{Metadata Features}
    \label{tab:metafeatures}
    \centering
    \begin{tabular}{l p{5.5cm}}
        \toprule
        \midrule
        \textbf{Feature} &  \textbf{Description}  \\
        \toprule
        \texttt{Comment Count} & Total \# of comments for each video \\
        \hline
        \texttt{Dislike Count} & Total \# of dislikes for each video \\
        \hline
        \texttt{Like Count} & Total \# of likes for each video  \\
        \hline
        \texttt{View Count} & Total \# of views for each video\\
        \hline
        \texttt{Like to Dislike} & The ratio of like count to dislike count \\
        \hline
        \texttt{Daily View Count} & Avg. \# of daily views for each video\\
        \hline
        \texttt{Like to View } & The ratio of like count to view count \\
        \hline
        \texttt{Duration} & Length of video in minutes \\
        \hline
        \texttt{Description URL Count} & Avg. \# of URLs in the description \\
        \hline
        \texttt{Like Count per Comment} & Avg. \# of likes for each comment \\
        \hline
        \texttt{Word Count per Comment} & Avg. \# of words in each comment \\
        \hline
        \multirow{2}{*}{\texttt{Clickbait Count}} & Avg. \# of words related to clickbait in each comment \\
        \hline
        \multirow{3}{*}{\texttt{Weighted Clickbait Count}} & Avg. \# of words related to clickbait in each comment weighted by comment's like count \\
        \midrule
        \toprule
    \end{tabular}
\end{table}

\begin{figure}[htb!]
    \centering
    \includegraphics[width=0.8\linewidth]{meta_correlation.pdf}
    \caption{Metadata Feature Correlation}
    \label{fig:correlation}
\end{figure}

\subsection{Supervised Classification} \label{sec:classifier}
Finally, we combine all the network, linguistic and metadata features described above and perform the binary classification to detect online clickbait videos. In the supervised classification module, we adopt a few state-of-the-art classification algorithms and select the best-performed one as the classifier for our OVCP scheme. The supervised classifiers include probabilistic classifiers, support vector machine, boosting and ensemble methods, and neural networks. The detailed performance evaluation for the collection of classifiers and the OVCP scheme is presented in the following section.

redpajama_set_name: RedPajamaArXiv


text:
TV2 (ou TV 2) est le nom de plusieurs chaînes de télévision de différents pays :
  Algérie : TV2, une chaîne de télévision généraliste publique algérienne à dominante francophone ;
  Côte d'Ivoire : RTI 2, chaîne de télévision généraliste publique, anciennement TV2 ;
  Danemark : TV 2, détenu par TV 2 Danmark ;
  Hongrie : TV2 ;
  Malaisie : TV 2 ;
  Norvège : TV 2, détenu par le TV 2 Group ;
  Portugal : RTP2, chaîne de télévision généraliste publique, anciennement TV2.

Voir aussi

Liste de chaînes de télévision par pays

redpajama_set_name: RedPajamaWikipedia


A joint project between the Springfield Parks Foundation and the Lincoln Park Neighborhood association, Friends of Lincoln Park are an important part in maintaining Lincoln Park as a landmark of the Northend. Volunteers have helped with the planning and upgrading of the Lincoln Park Lagoon as well as general maintenance of the park.
Help us to keep Lincoln Park the landmark that it has always been. Volunteer today!


text:
Pecado de Amor (English title:Sin of Love) is a Venezuelan telenovela written by Mariela Romero and aired by Venevisión from 8 November 1995 to 21 January 1997, thus making it the longest running telenovela Venevisión has ever made. This telenovela lasted 325 episodes and was distributed internationally by Venevisión International.

Karina and Víctor Cámara starred as the protagonists with Joanna Benedek as the antagonist.

Synopsis

While still an adolescent, Consuelo Sanchez gets pregnant by her boyfriend, Adolfo Alamo. She soon finds out, however, that Adolfo is married to a rich alcoholic named Marisela, and that they have three children: Junior, Andreina and Rosalia. In spite of this, Consuelo goes ahead with her pregnancy and gives birth to a girl. But Consuelo's mother, Blanca, takes the baby away from her and abandons her at a small house in the city slums. This situation prompts Consuelo to leave home, determined to never see or speak to her mother again. The owner of the house, Carmen Rosa Barrios, takes in the abandoned baby, names her Rosa and raises her as her own. Many years later, Blanca, Rosa's real grandmother, unaware of the relationship between them, hires Carmen and Rosa as maids at a students' residence managed by her. A series of events link the young people living at the boarding house with Rosa and her past. Mildred, an outstanding philosophy student, becomes Rosa's best friend. Alejandro Marquez, Rosalia Alamo's fiance, falls in love with Rosa. A child is born of their relationship, but the scorned Rosalia manages to convince Alejandro that the baby's father is actually her brother, Junior Alamo. Meanwhile, Rosa's real mother, Consuelo, is living with Isaias Pena, a heavy drinker in need of counseling. When Consuelo takes Isaias to a meeting of Alcoholics Anonymous, she meets Marisela, not knowing that she is Adolfo Alamo's wife. This friendship leads to a surprise encounter between Consuelo and her teenage love, Adolfo, who is the father of her lost child... and to a dangerous rekindling of that old flame. Destiny also puts Consuelo in touch with Rosa. Without knowing that they are mother and daughter, they develop a very special, loving relationship. Later on, it is Consuelo who opens Rosa's eyes about what is happening in her life; she tells her that Junior and Rosalia, who have hurt her so much, are actually her half siblings, and that she is not Carmen's daughter but her own. Everyone endures deep suffering until Adolfo has a heart-to-heart talk with Rosa, and together they force Blanca to confess that she in fact took Rosa away from her mother and abandoned her at Carmen's house. Junior, ill with a terminal cancer also confesses to Alejandro that he was never Rosa's lover, and therefore not her baby's father. Adolfo convinces Marisela to give him a divorce, because he is still in love with Consuelo. Upon Junior's death, Rosa forgives Rosalia for her cruel schemes, and they finally accept each other as sisters; Alejandro and Rosa come together again, as well as Consuelo and Adolfo, and everyone prepares to forget the past and start a new life.

Cast

Karina as Rosa Álamo Sanchez
Víctor Cámara as Alejandro Márquez
Joanna Benedek as Rosalia Alamo
Amanda Gutierrez as Consuelo Sanchez
Gustavo Rodríguez as Adolfo Alamo
Denise Novell as Natalia
Javier Valcarcel as Junior Alamo
Elba Escobar as Marisela de Alamo
Emma Rabbe as Indira
Viviana Gibelli as Esperanza Hernandez
Isabel Moreno as Amalia Marquez
Hector Myerston as Aureliano
Jorge Aravena as Fernando
Raquel Castanos as Rosa
Alberto Marin
Carlos Oliver
Maurico Renteria
Gigi Zancheta
Dulce Ma. Pilonieta
Eva Blanco
Lucy Orta
Ma. de Lourdes Devonish
Estelita del Llano
Marta Carbillo
Eva Mondolfi
Lucio Bueno
Alexis Escamez
Humberto Tancredi
Omar Moinelo
Aitor Gaviria
Ricardo Hernandez
Luis Gerardo Nunez

References

External links

Opening credits

1995 telenovelas
Venevisión telenovelas
Venezuelan telenovelas
1995 Venezuelan television series debuts
1997 Venezuelan television series endings
Spanish-language telenovelas
Television shows set in Venezuela


text:
require "spec_helper"

describe Irwi::Helpers::WikiPageAttachmentsHelper do
  it { is_expected.not_to be_nil }

  context "included in class" do
    subject { Object.new }

    before(:each) do
      Irwi.config.page_attachment_class_name = 'WikiPageAttachment'

      subject.send :extend, Irwi::Helpers::WikiPagesHelper
    end

    describe :wiki_show_attachment do
      before do
        class WikiPageAttachment; end
      end

      it 'replaces Attachment_1_thumb with its corresponding image tag' do
        paperclip_attachment = double('paperclip attachment')
        attachment = double(WikiPageAttachment, wiki_page_attachment: paperclip_attachment)

        expect(WikiPageAttachment).to receive(:find).with('1').and_return(attachment)
        expect(paperclip_attachment).to receive(:url).with(:thumb).and_return(:thumb_image)

        expect(subject).to receive(:image_tag).with(:thumb_image, class: 'wiki_page_attachment').and_return('thumb_image_markup')

        expect(subject.wiki_show_attachments('Foo Attachment_1_thumb Bar')).to eq 'Foo thumb_image_markup Bar'
      end

      it 'does not affect text without attachments' do
        expect(subject.wiki_show_attachments('Foo Bar')).to eq 'Foo Bar'
      end

      it 'ignores absent attachments' do
        expect(WikiPageAttachment).to receive(:find).with('10').and_raise(ActiveRecord::RecordNotFound)

        expect(subject.wiki_show_attachments('Foo Attachment_10_thumb Bar')).to eq 'Foo  Bar'
      end
    end
  end
end

redpajama_set_name: RedPajamaGithub


text:
Adele Reveals That Sometimes She Finds It Difficult To Sing
By Jane On Dec 16, 2015
Recently during a live performance in New York, Adele blew the audience away with her rendition of All I Ask.
Some people broke into tears, after she finished the song, despite the fact that she admitted that she sometimes find it difficult to sing.
She told the audience that she was finding it difficult to perform the song due to to her newfound vocal range.
She said "It is, it's f***ing, it's really hard to sing. I had vocal surgery and then suddenly I could sing higher, I could sing lower, I could do whatever you wanted me to do.
"As soon as I'm in front of you lot, I just f**k myself."
Adele also revealed that she is a happier place now with her boyfriend Simon Konecki and her son Angelo.
She said "I don't sing this from the same place anymore."
"I sing it from an amazing place because of my man who is here tonight – and I love you – and this is the first show that he's ever seen me do!"
Adele's new album, 25 is currently topping charts worldwide.
Adelelive performanceNND
Jumoke
Indiscipline: Buhari Orders Head of Service To Sanction Senior Civil Servants
#DasukiGate: Goodluck Seeks Private Meeting With Buhari
"I don't mind cheating" – Ric Hassani reacts to Waje rumoured…
Why celebrity marriages fail – Phyna
'Happy birthday, hubby' — Sabinus' lover fetes skit maker
Singer Chuddy K weeps after losing wife to cancer

redpajama_set_name: RedPajamaCommonCrawl


text:
Central African Republic: Evacuation dilemma in the Central African Republic
Photo: Till Muellenmeister/IRIN. French soldiers protect a convoy of fleeing muslims from an angry mob
BANGUI, 28 February 2014 (IRIN) - Zannah Bassar, a Muslim woman living in the Central African Republic (CAR) capital, Bangui, has a simple message for the international community - she wants to be evacuated.
"I was born in this district," she told IRIN this week, "but my home has been wrecked. I've been sleeping in the street for the past month and I want to send an SOS. I want to go somewhere else."
Bassar and about 3,200 fellow Muslims are trapped in a kilometer-long district of Bangui known as PK12. Half a dozen other Muslims IRIN interviewed in the same district this week all said they wanted to leave.
"The people in PK12 have a deep desire to get out of there," said Jacques Seurt, head of mission for the International Organization for Migration (IOM) in CAR, which has stopped chartering rescue flights out of the country for lack of funding.
"They are under constant threat from the anti-balaka as are other Muslims in the west of the country."
The country's Muslims have been the target of reprisal attacks because the Seleka rebel alliance that toppled the government in March 2013 was predominantly made up of Muslims and committed widespread atrocities in many parts of the country.
The UN Refugee Agency (UNHCR) reported on 25 February that "more than 15,000 people in 18 locations are surrounded by armed groups across the west of the CAR… and at high risk of attack," adding that most of these people are Muslim.
"Areas we are particularly worried about include the PK12 neighbourhood in Bangui and the towns of Boda, Bouar and Bossangoa," a spokesman said.
Anti-balaka have been firing grenades at PK12 from surrounding hills and infiltrating the area, wounding several people, said Peter Neussl of the UN Office for the Coordination of Humanitarian Affairs (OCHA).
On 19 February, UNHCR reported, the anti-balaka attacked a convoy of people trying to escape from PK12. All 21 men in the convoy were killed, leaving 119 children and 19 women, who fled to a nearby village.
Security has improved in Bangui since December when around 1,000 people were killed there in a few days, but attacks are still happening almost daily. On 22 February three Muslims were dragged from a taxi and shot dead at an anti-balaka road block, two days later five men were killed in the PK5 district and on 26 February four Muslim children were kidnapped (an OCHA worker helped negotiate their release two days later).
Options for relocation
Until recently a high proportion of the Muslims in CAR were Chadians, or of Chadian descent. In the past two months Chad's government has been organizing large convoys, escorted by the Chadian army, to evacuate its citizens from CAR, but on 20 February, after the attack on the convoy from PK12, N'djamena announced an end to these operations and declared that 99 percent of its citizens that wanted to leave CAR had left.
Many Muslims interviewed by IRIN at PK12, Bouar and Bossangoa in the past two weeks said they want to go to Chad, as they have relatives there, but this option is becoming more difficult, and aid agencies are under increasing pressure to find other options.
Up to now aid agencies' involvement in evacuations from CAR has mainly been limited to work by IOM, which has chartered flights for around 5,000 third country nationals, and UNHCR, which has assisted some refugees with travel.
OCHA is now considering plans for moving populations within the country.
Facilitating a division of the country on religious (and largely ethnic) lines will be controversial for aid workers. Amnesty International said last month that nobody wanted a debate on the issue.
But some officials feel it can no longer be ignored.
"We shall be judged by the effectiveness of our actions, not the elegance of our principles," a senior aid worker said confidentially. "We don't want to be complicit with ethnic cleansing but nor do we want any share of the blame for ethnic extermination."
Peter Neussl explained current thinking: "We want to give people options for relocation. We are exploring possibilities inside Bangui of moving people to places that would be easier to protect."
Transferring people within Bangui would be easier logistically, he said, as it is hard to find trucks and drivers who will take the risk of driving Muslims long distances through hostile territory, even with an armed escort.
Apart from the ill-fated convoy from PK12, other trucks have also come under fire or had grenades thrown at them in CAR in recent weeks. Five people were killed in one such incident and more than 20 in another, while the anti-balaka attacked a convoy of 89 vehicles on 16 February, wounding 12 civilians, although the attack was beaten off by the escort of peacekeepers.
"In the long term, people want to live in their home areas in security," Neussl said, "but as a mid-term solution we are considering moving Muslims from PK12 to safer towns outside Bangui. We would have to be very careful about the possible impact of such transfers on the host population."
Neussl emphasized that relocation should be a last resort.
UN Secretary-General Special Representative in CAR Boubacar Gaye told IRIN: "The secretary-general made very clear before the [Security] Council this week that we should do our utmost to stop the exodus of the Muslim population, and this is also the understanding of [CAR's] Madam President Samba-Panza.
"Security is the first priority. The option of moving people under the international community's escort is an option, one of the planning options."
He stressed that any such move must be fully coordinated with the national authorities and fully explained, and that it would be "very dangerous for the future of the country to have all the Muslims in one part of the country and a de facto partition", as this would be "sowing the seeds for future confrontation".
The exodus of most Muslim traders and cattle herders from western CAR is already having serious economic effects, with meat and other products now scarce in the markets and concerns that supplies will deteriorate further.
Views from northwestern CAR
The UN's senior humanitarian official Valerie Amos canvassed people's views about relocation during her visit last week to Bossangoa, a town about 350km north of Bangui where some 1,200 Muslims are living in and around a school, l'Ecole de Liberté.
The town's imam Ismail Nahi told Amos:
"If there were peace and security all the Muslims would come back… but the Christians have made it clear they don't want to live with Muslims in this country. Wherever they find Muslims they will kill them and cut them into little pieces."
A women's group leader, Kadjidja Hassan, said people did not want to go to Chad because they had been in CAR a long time, but she added that they have now been in the camp for five months, and: "Muslims have been attacked in all the villages around Bossangoa. A Muslim can't move more than one kilometre from this site. We're all crammed in here, and our husbands can't do anything. How can we live in Bossangoa?"
With Chadian African Union troops providing security, there have been almost no killings of Muslims in Bossangoa in the past two months, but nearly all the camp dwellers IRIN spoke to said they needed more security, although not all said they wanted to leave.
A group of men accused the district administrator (prefect), who was with the visiting delegation, of distributing weapons to the anti-balaka and said that if she were removed from her post they could stay on in Bossangoa. The prefect denied the accusations.
The typical view from most people seemed to be that unless they have security, by which they appeared to mean free movement outside their little ghetto, they would prefer to leave Bossangoa.
Although aid agencies are providing water and basic rations, living conditions are worsening, as the supplies that many people brought to the camp when they abandoned their shops are now finished.
IRIN interviewed Muslims at a site around a mosque in Bouar, near Cameroon, on 14 February. The "camp president" there said many at the site wanted to stay in the area but most other interviewees said they wanted to leave, as did most of the Muslims interviewed at a displacement site in Baoro, 50km away.
At both sites Muslims said they could not walk around the town as the anti-balaka would kill them.
The Muslims' attitude may have changed in Bouar since the French military mission (Operation Sangaris) deployed about 300 men to the area on 15 February.
The situation at Bossangoa, Bouar and PK12 is not replicated everywhere in western CAR. UNHCR said on 25 February that "in some towns like Paoua (near the Chadian border) and in some quarters of Bangui, communities continue living and working together, although atrocities are frequently committed."
So far there has been much less communal violence in the centre and east of the country where the Muslim population is larger. Nationally the proportion of Muslims was estimated at around 15 percent, but it is now lower.
More police needed
International security forces are to be reinforced in CAR, with 1,000 European Union peacekeepers due to be deployed soon. International Crisis Group analyst Thierry Vircoulon argues that more police, rather than troops, should be the priority.
The government is currently deploying only about 150 gendarmes (with donor funding) and even they man checkpoints alongside the anti-balaka, while the international police forces are well below the mandated level.
Winning public support in the battle against the anti-balaka may be a long process, as they are seen by many of the population as liberators after their fight against the largely Muslim Seleka rebels.
Most of the international security forces have tended to avoid confronting the anti-balaka, who can cause major disruption, and have closed down the airport on several occasions.
On 25 February IRIN passed the corpse of a Muslim man that had been lying at a crossroads in Bangui for several hours. A group of youths standing nearby said they had killed the Muslim with rocks and machetes because he had crossed a "red line".
Peacekeeper patrols had passed the spot several times that morning but the youths were still at their checkpoint.
Labels: Africa, Central African Republic, IDPs, Refugees


text:
Q: VBA: Find first row with exact date or later date The code below shows me the first row in which the July 5th 2019 appears in column A. However, sometimes this exact date isn't in the column and I'd like to get the first row with a date after July 5th. How do I do that?
Sub Test()
    Debug.Print ThisWorkbook.Sheets(1).Range("A:A").Find(CDate("05.07.19")).Row
End Sub

A: I bet there are prettier ways, but this is one
Sub Test()
Dim Search As Range, myrng As Range
Dim rslt As Long, FRow As Long

Set myrng = ThisWorkbook.Sheets(1).Range("A:A")

Set Search = myrng.Find(CDate("05 July 19"))
If Not Search Is Nothing Then
    rslt = Search.Row
Else
    Set Search = myrng.Find(Mid(CDate("July 2019"), 3, 6)
    If Not Search Is Nothing Then
        FRow = Search.Row
        Do Until Search Is Nothing
            If Day(Search) > 5 Then
                rslt = Search.Row
                Exit Do
            End If
            Set Search = myrng.FindNext(Search)
            If Not Search Is Nothing Then
                If Search.Row = FRow Then Exit Do
            End If
        Loop
    End If
End If

Debug.Print rslt

As Prokurors rightfully points out though, the date format is likely to give problems.

A: Try this UDF:
Public Function GET_ROW_OF_DATE(ByVal vThisDate As Date, ByVal vOnThisRange As Range)

Dim rng As Range

With Application.WorksheetFunction
    If .CountIf(vOnThisRange, vThisDate) = 0 Then
        'if there is no exact match, count if any is higher
        If .CountIf(vOnThisRange, ">" & CDbl(vThisDate)) = 0 Then
            'There is no match
            GET_ROW_OF_DATE = 0
            Exit Function
        Else
            For Each rng In vOnThisRange
                If rng.Value > vThisDate Then
                    GET_ROW_OF_DATE = rng.Row
                    Exit Function
                End If
            Next rng
        End If
    Else
        'there is a match = to vThisDate
        For Each rng In vOnThisRange
            If rng.Value = vThisDate Then
                GET_ROW_OF_DATE = rng.Row
                Exit Function
            End If
        Next rng
    End If
End With

End Function

It there is no date equal or higher to the input, then it will return 0. Hope you can adapt this to your needs.

A: Maybe Give this a try :
Edited
Sub tryme()

   Dim rng As Range

Set rng = ThisWorkbook.Sheets(1).Range("A:A")

For Each c In rng.Cells
  If IsDate(c.Value) And c.Value = DateValue(Format("05/07/19", "dd/mm/yyyy")) Then
    firstValue = c.Value
    firstAddress = c.Address
    GoTo ENDSEARCH
  End If
Next

For Each c In rng.Cells
  If IsDate(c.Value) And c.Value > DateValue(Format("05/07/19", "dd/mm/yyyy")) Then
    firstValue = c.Value
    firstAddress = c.Address
    GoTo ENDSEARCH
  End If
Next

ENDSEARCH:
MsgBox "The first value greater than 05/07/2019 is in cell " & firstAddress & _
       "; - it has value " & firstValue

End Sub

redpajama_set_name: RedPajamaStackExchange


text:
Q: ¿Cómo puedo crear una espera explícita dinámica usando selenium en Python? Creé una programa en Python para hacer webscraping de datos en un website, al ejecutarlo a veces me arrojaba un problema, me explico:
WebDriverWait(driver, 10).until(EC.visibility_of_element_located((By.XPATH,'/Este_es_el_XPATH')))

Establecí la anterior espera explícita para obtener un dato en particular del website.
La mayoría de las veces que el programa realizó el proceso, funcionó muy bien.
Sin embargo, hubieron algunas ocasiones en las que se generó el siguiente error:

TimeoutException

Lo cual hacía que mi programa se detuviese y tuviese que repetir todo el largo proceso que ya había realizado.
El parámetro de espera de 10 segundos no siempre es efectivo.
Quisiera saber sí hay una forma de establecer este parámetro como una variable que siempre obtenga el tiempo de carga (en segundos) de una página que se haya cargado completamente, para así poder correr la espera explícita de arriba sin problemas.
Podría simplemente hacer que el proceso se repita " por la fuerza " hasta que la visibilidad del elemento esté finalmente cargada y así poder scrapear el dato, como por ejemplo, a través del uso de bloques try y exception.
Pero no sé qué debería escribir en el bloque exception para repetir lo que ponga en el try una y otra vez hasta que el elemento esté visible y pueda ser scrapeado.

A: Para hacerlo como dices(usando el try, except), puedes establecer una variable que cumplirá la función de un contador, esta se irá aumentando hasta que pase el tiempo que le indiques (valor máximo de el contador), esa misma variable, siempre la tendrás que establecer antes de empezar a buscar el primer elemento de todos, por ejemplo:
x = 0 # Contador

driver.get(url)

while x < 100: # intentar encontrar el elemento en 10 segundos, sino, continua con el código
    try:
        elemento = driver.find_element_by_name('name')
        elemento.click() # O el evento que desees ejecutar

        x = 0 # Ya encontró el elemento, así que el contador vuelve a 0
        break # Sale del bucle

    except: # en caso de no encontrarlo

        time.sleep(0.1) # Espera 100 milisegundos (decima parte de un segundo), recuerda importar el modulo time: import time
        x += 1 # Aumenta el contador

Como vez, los sleep tienen (0.1), esto para encontrarlo y actuar mas rápido, pero igualmente los podrías reemplazar con (1), y además quitar un 0 al principio del while (esto hará mas lenta la búsqueda).
Si en el futuro se tiene que buscar otro elemento, sería lo mismo, solo que ya no tendrás la necesidad de declarar el contador, ya que este ya se estableció en 0 anteriormente.
Espero y sirva para lo que buscas, no soy un experto, pero esta es una manera practica de realizar tal procedimiento.


text:
La 3 M - Montbelleux
Mémoire de la Mine de Montbelleux
Histoire, légendes
Les guerres
Chronology of the mine.
1903- : F.Kerforne, of Rennes University discovered samples of tungsten in Montbelleux.
1903- 1906: Research work between the Collet-Pintiaux and Surcouf shafts. The work involved two master-miners and eighty workers.
1907 – 1908: First period of ore extraction. On 10th October, work was interrupted because of the collapse of the price of tungsten. Before closure, the mine employed 204 workers, 118 of whom were miners.
1910 – 1911: Second period of activity under the company name of Gruzard – Clolus and Courtois under the management of Mr BAYLE. An average of 120 workers were employed in 1911.
1916 – 1918: Third period of activity, using three shafts. Personnel included 42 Frenchmen mobilized for the war effort on level 97 and in tracing operations, 90 prisoners of war on levels 27 and 62, and 10 prisoners of war at ground level.
07/04/1938: the licence to exploit the site was given to Edgar BRANDT.
1942 – 1944: Fourth period of activity, ensured by the German company Krupp with the support of the Todt organisation. A gantry was built over the Surcouf pit (10/09/1942). 300 men were employed, including 30 Germans. After, the number averaged 550.
1951-1958: Fifth period of activity, this time under the company Mines de Puys les Vignes (1948-1950).
Workforce: 140, 84 of whom were miners. In February 1955, 47 workers were laid off and a further 24 out of 56 jobs went in August 1957.
1976: Surveys on the surface of the downward slopes to locate granulites.
1977 to 1980: New exploration phase (pumping out the water that had flooded the old mine, renovation of infrastructure, buildings, numerous surveys).
1980 – 1983: A ramp was built to serve levels 60, 95 and 130 metres. A washing station with a capacity of 40 tonnes per hour was constructed in February 1983.
In November 1982 there were 58 employees; management/administrative 5 personnel, 30 underground workers (miners and drivers). 23 surface workers (washing and miscellaneous).
2006: The owner, Société des Mines de Montbelleux, initiated the procedure to put a final stop to mining activity. Work to rehabilitate the site could begin.
Fernand Kerforne
The mine - 1944
Montbelleux mine


text:
Jaclyn Jose: I don't like Bret
Posted at Jan 04 2015 06:16 PM | Updated as of Jan 05 2015 02:16 AM
MANILA – Despite spending a romantic New Year's Eve together, Andi Eigenmann clarified that she is not yet in a relationship with former "Pinoy Big Brother" housemate Bret Jackson.
In an interview with "The Buzz" on Sunday, Eigenmann said she just wants to take things slow since she just broke up with former boyfriend, Jake Ejercito.
"Nothing has happened yet. It's only the beginning. I just came out of a long and very complicated relationship. I would want to take things slow," she said.
Eigenmann said they just enjoy each other's company and that's why they are seen hanging out a lot.
"To me, I think everybody knows na I'm starting anew. Yung 2015, para sa akin hindi lang bagong taon. Siyempre bagong chapter din ng life ko because I lost two people who are really important to me. Now I just want to make sure na magiging masaya na ako," she said.
A photo posted by Bret Jackson II (@b.waw) on Jan 1, 2015 at 12:13am PST
Meanwhile, Eigenmann's mother, actress Jaclyn Jose, expressed her disapproval of her daughter's brewing romance with Jackson.
Asked why she doesn't like Jackson, Jose said: "Baka sumikat pa 'yan. ....I don't know him at all."
Jose, however, said Eigenmann still has the last word when it comes to her personal relationships.
"I don't judge his personality. It's up to Andi. It's her life. But for me, I don't like him. Sorry," she said.
Reacting to her mother's strong statements, Eigenmann said she does not mind what Jose thinks of Jackson -- yet.
"As of now it doesn't [matter] because pwede naman niya sabihin what she wants and at the same time, hindi pa rin naman kasi niya nakikilala si Bret. Open pa to judgment. She never really got the chance," she said.
Eigenmann also noted that her mother is definitely biased in favor of Ejercito.
"I know how close my mom is to my ex so maybe that's why. She loves Jake," she said.
Setting aside all intrigues, Eigenmann said she is looking forward to more projects this 2015.
Entertainment,Top Stories,Editor's Pick,Jaclyn Jose,Andi Eigenmann,Bret jackson,star news
Read More: Jaclyn Jose Andi Eigenmann Bret jackson


text:
Winnipeg Free Press / The Times
Groups send medical supplies to Ethiopia
Posted on 11 December 2020 5 May 2022 by Tracy
by Sydney Hildebrandt / WFP The Times / Friday, Dec. 11, 2020
An airplane full of medical supplies is on its way to Ethiopia, where thousands of people have been killed or displaced due to an ongoing conflict in the country's northern region of Tigray.
Ethio-Canadian Network for Advocacy and Support — whose vice-chair is Garden City resident Markos Tegegn — is co-ordinating a shipment of bandages, nasal cannulas, syringes, needles, sutures, catheters and other medical items to the East African country.
Markos Tegegn (left), vice-chair of Ethio-Canadian Network for Advocacy and Support, and Mengistu Ayalew Kindie, organized the shipment of medical supplies to Ethiopia. SUPPLIED PHOTO
The turmoil in Ethiopia can be described as a "war," Tegegn said. The conflict is rooted in a political dispute between leaders in Tigray and the country's federal government. According to media reports, thousands of civilians have been displaced or killed since Nov. 4, when the prime minister of Ethiopia, Abiy Ahmed, ordered his military to strike Tigray forces.
"You can imagine, when these things happen, when you're not prepared, the impact that will have. So because of that, they were in dying need of medical supplies in order to look after the wounded ones," Tegegn said. "It's more than needed right now."
Ethio-Canadian Network for Advocacy and Support is a Canada-wide non-profit organization comprised of Ethiopian diaspora, whose goal is to work with government agencies and community partners to support people affected by conflict.
The organization has been working with International Hope Canada, the Embassy of Ethiopia in Ottawa, and Ethiopian Airlines on this particular project. The airline company, which flies out of Pearson International Airport in Toronto, agreed to deliver the shipment for free, Tegegn said.
www.winnipegfreepress.com/our-communities/times/Groups-send-medical-supplies-to-Ethiopia-573372931.html
With appreciation to the Winnipeg Free Press and The Times for the article!
This entry was posted in Container Loading, News Stories and tagged ethiopia. Bookmark the permalink.
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MASCAC Postseason Honors
PSU Men's Ice Hockey stunned by Castleton
Plymouth State (5-6-1) 1 1 1 3
Castleton (4-8-2) 0 1 3 4
1st - 19:52 - Myles Abbate (Plymouth State)
2nd - 17:35 - Myles Abbate (Plymouth State)
2nd - 17:54 - Calvin Moise (Castleton)
3rd - 07:01 - Peter Laviolette (Plymouth State)
3rd - 08:37 - Matt Bloomer (Castleton)
3rd - 09:27 - Nick Gravina (Castleton)
3rd - 19:39 - Troy Taylor (Castleton)
G: Myles Abbate - 2
A: Jake Gerbner - 2
Sh: Myles Abbate - 5
Sv: Jake Sabourin - 23
G: 4 Players (#8, #19, #24, #39) - 1
A: Anton Tarvainen - 2
Sv: Brandon Collett - 40
Abbate scores twice in setback
RUTLAND, Vt. – Freshman Myles Abbate (Norwell, Mass.) scored a pair of goals, but Castleton University scored three times in the final 11:23 to rally past Plymouth State University, 4-3, in a non-conference men's ice hockey game at Spartan Arena on Saturday night.
Abbate gave PSU a 2-0 lead with his team-leading sixth and seventh goals of the year, and junior Peter Laviolette (Brentwood, Tenn.) made it a 3-1 advantage seven minutes into the third, but Castleton rallied with a pair of goals less than a minute apart to tie the game before notching the game-winner with just 21 seconds remaining.
With the setback Plymouth State falls to 5-6-1, while Castleton improves to 4-8-2.
Plymouth State 3, Castleton 4
Plymouth State (5-6-1)
Castleton (4-8-2)
How it Happened.
Plymouth State pressured throughout the first period but the game remained scoreless until the final seconds. PSU got on the board when junior Jake Gerbner (Havertown, Pa.) forced a turnover at the blue line and skated in alone. Though his shot was denied, Abbate was there to clean up the rebound with just eight seconds left in the frame.
Abbate would double the lead with a strong individual play late in the second period. He skated around traffic from the right side before whipping a wrist shot on net, then jumped on the rebound to fire a shot to the bottom right corner for a 2-0 lead with 2:25 left in the second.
Castleton came right back just 19 seconds later when freshman Calvin Moise (Saint-Eustache, Quebec) split the defense and juked around junior goaltender Jake Sabourin (Catonsville, Md.) for the goal.
The Panthers restored the two-goal lead at the 7:01 mark of the third period when senior Kyle Orgel (Los Angeles, Calif.) took the puck from behind the Castleton goal and sent a backhander through the crease to Laviolette for the easy score.
The Spartans started the comeback 1:36 later when junior Matt Bloomer (Rochester, N.Y.) rifled a wrister from the top of the faceoff circle and less than a minute later junior Nick Gravina (Bloomingdale, Ill.) beat Sabourin to the top left corner on a shot from the left wing circle to even the game at 3-3.
PSU came up empty on a late power play chance and senior Troy Taylor (Mississauga, Ontario) notched the game-winner with a wrap-around goal with just 21 seconds remaining.
Inside the Numbers:
Abbate leads the Panthers with seven goals and 10 points.
Gerbner also had a multi-point game with a pair of assists.
Plymouth State held a 43-27 edge in shots.
Neither team was successful on the power play. PSU finished 0-for-1, while the Spartans were held scoreless in three chances.
Sabourin made 23 stops for the Panther but dropped to 0-2-0, while freshman Brandon Collett (Brockton, Mass.) improved to 2-6-1 with his 40-save effort.
Plymouth State travels to #15 New England College on Tuesday (Jan. 7) for a 7:00 p.m. contest.
Sat, 01/18 | Men's Ice Hockey vs. Westfield State W, 7-1 (Final) RC | BX
Thu, 01/16 | Men's Ice Hockey at Framingham State W, 4-1 (Final) RC | BX
Sat, 01/11 | Men's Ice Hockey vs. Fitchburg State T, 2-2 (Final - OT) RC | BX
Tue, 01/07 | Men's Ice Hockey at New England College L, 7-4 (Final) RC | BX
Sat, 01/04 | Men's Ice Hockey at Castleton L, 4-3 (Final) RC | BX