The emergence of social media as news sources has led to the rise of clickbait posts attempting to attract users to click on article links without informing them on the actual article content. This paper presents our efforts to create a clickbait detector inspired by fake news detection algorithms, and our submission to the Clickbait Challenge 2017. The detector is based almost exclusively on text-based features taken from previous work on clickbait detection, our own work on fake post detection, and features we designed specifically for the challenge. We use a two-level classification approach, combining the outputs of 65 first-level classifiers in a second-level feature vector. We present our exploratory results with individual features and their combinations, taken from the post text and the target article title, as well as feature selection. While our own blind tests with the dataset led to an F-score of 0.63, our final evaluation in the Challenge only achieved an F-score of 0.43. We explore the possible causes of this, and lay out potential future steps to achieve more successful results.
The problem of clickbait posts is relatively recent, yet active research is already developing around it. One of the first publications on the subject @cite_4 proposed a set of potential features that could be used for the task, without providing a quantitative analysis of their potential. The proposed features included lexical and semantic features in order to distinguish between high- vs low-quality text by analyzing their stylometry, and syntactic and pragmatic features to measure the emotional impact of headlines. Besides textual features, they also proposed image and user behavior analysis in order to extract information from the context of the post.
@cite_4
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Tabloid journalism is often criticized for its propensity for exaggeration, sensationalization, scare-mongering, and otherwise producing misleading and low quality news. As the news has moved online, a new form of tabloidization has emerged: ?clickbaiting.? ?Clickbait? refers to ?content whose main purpose is to attract attention and encourage visitors to click on a link to a particular web page? [?clickbait,? n.d.] and has been implicated in the rapid spread of rumor and misinformation online. This paper examines potential methods for the automatic detection of clickbait as a form of deception. Methods for recognizing both textual and non-textual clickbaiting cues are surveyed, leading to the suggestion that a hybrid approach may yield best results.