Clickbait Detection

Potthast, Martin; Köpsel, Sebastian; Stein, Benno; Hagen, Matthias

doi:10.1007/978-3-319-30671-1_72

Cited by 178 publications

(198 citation statements)

References 9 publications

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“…We crawled 8, 069 web articles from these domains during the month of September, 2015. To avoid 4. en.wikinews.org/wiki/Wikinews:Style guide#Headlines Figure 1: Distribution of the length of both clickbait and non-clickbait headlines false negatives (i.e.…”

Section: Datasetmentioning

confidence: 99%

“…Yet, Facebook users still complain that they continue to receive clickbaits and there is a renewed effort to clamp down on clickbaits 3 . In a recent work, Potthast et al [4] attempted to detect clickbaity tweets in Twitter. The problem with such standalone approaches is that clickbaits are prevalent not only on particular social media sites, but also on many other reputed websites across the web.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stop Clickbait: Detecting and preventing clickbaits in online news media

Chakraborty

Paranjape

Kakarla

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

297

264

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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 longrun, 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.2. newsroom.fb.com/news/2014/08/news-feed-fyi-click-baiting 3. thenextweb.com/facebook/2016/04/21/facebook-might-finally-killclickbait-new-algorithm-tweaks/

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Section: Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stop Clickbait: Detecting and preventing clickbaits in online news media

Chakraborty

Paranjape

Kakarla

et al. 2016

2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

297

264

View full text Add to dashboard Cite

show abstract

“…The term clickbait was coined as "exaggerated headlines whose main motive is to mislead the reader to click on them" [15]. The problem of clickbait has been widely studied and many solutions were provided [10,16,17]. The first automatic clickbait detector was proposed in [10], 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.…”

Section: Clickbait Detectionmentioning

confidence: 99%

“…This problem cannot be fully addressed by current content-based solutions which only focus on the text of the title [4,5], the image of the thumbnail [6,7], or the content of the video [8,9]. 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 [4], clickbait tweets detection using the linguistic feature analysis [10]). 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.…”

Section: Introductionmentioning

confidence: 99%

Towards reliable online clickbait video detection: A content-agnostic approach

Shang

Zhang

Wang

et al. 2019

Knowledge-Based Systems

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Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos. In this paper, we develop a novel content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by exploring the comments from the audience who watched the video. Different from existing solutions, OVCP does not directly analyze the content of the video and its pre-click information (e.g., title and thumbnail). Therefore, it is robust against sophisticated content creators who often generate clickbait videos that can bypass the current clickbait detectors. We evaluate OVCP with a realworld dataset collected from YouTube. Experimental results demonstrate that OVCP is effective in identifying clickbait videos and significantly outperforms both state-of-the-art baseline models and human annotators.

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“…These recent techniques include "click-baiting" or headlines whose main objective is to lure the user and whose presence was warned by the recent issue of Journalism (2016) in the article titled "The Future of Journalism: Risks, threats and opportunities". In this sense, some studies already constitute a kind of anti-clickbait movement, which proposes detection and blocking systems for clickbait headlines (Chen, Conroy & Rubin, 2015;Chakraborty, Paranjape & Kakarla, 2015;Anand, Chakraborty & Park, 2016;Potthast, Köpsel, Stein & Hagen, 2016). …”

Section: Introductionmentioning

confidence: 99%