Stop Clickbait: Detecting and preventing clickbaits in online news media

By Abhijnan Chakraborty, Bhargavi Paranjape, Sourya Kakarla, Niloy Ganguly. International Conference on Advances in Social Networks Analysis and Mining, 2016

398 citations recorded by the source corpus.

Most of the online news media outlets rely heavily on the revenues generated from the clicks made by their readers, and due to the presence of numerous such outlets, they need to compete with each other for reader attention. To attract the readers to click on an article and subsequently visit the media site, the outlets often come up with catchy headlines accompanying the article links, which lure the readers to click on the link. Such headlines are known as Clickbaits. While these baits may trick the readers into clicking, in the long-run, clickbaits usually don't live up to the expectation of the readers, and leave them disappointed. In this work, we attempt to automatically detect clickbaits and then build a browser extension which warns the readers of different media sites about the possibility of being baited by such headlines. The extension also offers each reader an option to block clickbaits she doesn't want to see. Then, using such reader choices, the extension automatically blocks similar clickbaits during her future visits. We run extensive offline and online experiments across multiple media sites and find that the proposed clickbait detection and the personalized blocking approaches perform very well achieving 93% accuracy in detecting and 89% accuracy in blocking clickbaits.

Subfields: Communication, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/37e6cac9c4a9a93b524b539aa58a2dbd99e1228e

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Citations in the corpus

Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…et al. 2016], user interests [Chakraborty et al. 2016; Zheng et al. 2017] and headline stance [Bourgonje et al. 2017]) within a supervised learning framework, e.g., gradient boosted decision trees [Biyani et al. 2016; Zheng et al. 2017], to detect or block clickbaits [Chakraborty et al. 2016].”
Detecting Fake News in Social Media Networks (2018, background): “The result is that a majority of the users end up disappointed, leaving the sites as soon as they have visited it, and resulting in high bounce rates for such web pages [10].”
Cognitive attraction and online misinformation (2018, background): “…incorrect information, that online interactions can preserve anonymity (Allcott and Gentzkow, 2017) and that search engines, and especially social media algorithms, are optimised for (shallow) engagement, giving disproportionate weight to ‘like’ and previous traffic (Chakraborty et al. 2016).”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, methodology): “…: POS tags, linguistic features based on the use of speciﬁc words (function words, pronouns, etc.), and features for clickbait title classiﬁcation from (Chakraborty et al., 2016); • Sentiment : sentiment scores using lexicons (Recasens et al., 2013; Mitchell et al., 2013) and full systems (Hutto…”
A survey on fake news and rumour detection techniques (2019, methodology): “bi-grams and tri-grams) as features [16, 34, 73, 78].”
Crowdsourcing a Large Corpus of Clickbait on Twitter (2018, methodology): “Rony et al. (2017), Chakraborty et al. (2016), and Agrawal (2016) report to have double-checked the induced labels; each instance has been reviewed by at least three student volunteers.”
Federated Hierarchical Hybrid Networks for Clickbait Detection (2019, methodology): “Chakraborty et al. collected extensive titles for both clickbait and non-clickbait categories and manually extracted features for SVM classi-ﬁer, Decision Trees classiﬁer, and Random Forests classiﬁer (Chakraborty et al., 2016).”