Misleading Metadata Detection on YouTube
(b) training dataset: FVC70
['Classifier', 'P', 'R', 'F']
[leftmargin=*,itemsep=0.05em] has_clickbait_phrase: This feature is true if the title has a phrase commonly found in clickbaits. For eg. ‘blow your mind’, ‘here is why’, ‘shocking’, ‘exposed’, ‘caught on cam’. We used 70 such phrases gathered manually. ratio_violent_words: A dictionary of several ‘violent’ words like ‘kill’, ‘assault’, ‘hack’, ‘chop’ was used. The value of this feature is equal to the fraction of violent words in the title. We hypothesize that violent words generate fear which leads to more views for the video, hence more used in spams. ratio_caps: This feature is equal to the ratio of number of words in the title which are in upper case to the total number of words in the title. Tweet Classifier Score - Title: The Image verification corpus (IVC) We trained a multi-layer perceptron on IVC to predict the probability of a tweet (i.e., the accompanying image) being fake using only simple linguistic features on the tweet text. Now, we use the same trained network and feed it the title of a video as input. The probability of fakeness that it outputs is then taken as a feature,. dislike_like_ratio: Ratio of number of dislikes to number of likes on the video. comments_fakeness: This is equal to the ratio of comments on the video which mention that the video is fake. To categorize if a comment says that the video is fake or not, we detect presence of words and regexes like ‘fa+ke+’, ‘hoax’, ‘photoshopped’, ‘clickbait’, ‘bullshit’, ‘fakest’, ‘bs’. comments_inappropriateness: This feature is equal to the ratio of number of comments with swear words to the total number of comments on the video. comments_conversation_ratio : This is the ratio of comments with at least one reply to the total number of comments on the video. Since some of these classifiers are sensitive to correlations in the features, we first decided to remove the lesser important feature among each pair of correlated features. For this, we calculated correlations among the features on all the 100K videos and identified the pair of features with a correlation of more than 0.2. Then we generated feature importance scores using the standard random forests feature selection method and eliminated the lesser important feature from each pair. We trained our classifiers using only these remaining features.