Stop Clickbait: Detecting and preventing clickbaits in online news media

Chakraborty, Abhijnan; Paranjape, Bhargavi; Kakarla, Sourya; Ganguly, Niloy

doi:10.1109/asonam.2016.7752207

Cited by 309 publications

(316 citation statements)

References 8 publications

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“…Clickbait detection and prevention are known challenges to media sites [26]. We are also motivated to explore more sophisticated metrics than raw clicks to be used as the reward of MAB.…”

Section: Discussionmentioning

confidence: 99%

A Batched Multi-Armed Bandit Approach to News Headline Testing

Mao¹,

Chen²,

Wagle³

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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Optimizing news headlines is important for publishers and media sites. A compelling headline will increase readership, user engagement and social shares. At Yahoo Front Page, headline testing is carried out using a test-rollout strategy: we first allocate equal proportion of the traffic to each headline variation for a defined testing period, and then shift all future traffic to the best-performing variation. In this paper, we introduce a multi-armed bandit (MAB) approach with batched Thompson Sampling (bTS) to dynamically test headlines for news articles. This method is able to gradually allocate traffic towards optimal headlines while testing. We evaluate the bTS method based on empirical impressions/clicks data and simulated user responses. The result shows that the bTS method is robust, converges accurately and quickly to the optimal headline, and outperforms the test-rollout strategy by 3.69% in terms of clicks.

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“…Clickbait detection and prevention are known challenges to media sites [26]. We are also motivated to explore more sophisticated metrics than raw clicks to be used as the reward of MAB.…”

Section: Discussionmentioning

confidence: 99%

A Batched Multi-Armed Bandit Approach to News Headline Testing

Mao¹,

Chen²,

Wagle³

et al. 2018

2018 IEEE International Conference on Big Data (Big Data)

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“…• Structure: POS tags, linguistic features (function words, pronouns, etc. ), and features for clickbait title classification from (Chakraborty et al, 2016);…”

Section: Nela Featuresmentioning

confidence: 99%

Detecting Toxicity in News Articles: Application to Bulgarian

Dinkov¹,

Koychev²,

Nakov³

2019

Proceedings - Natural Language Processing in a Deep Learning World

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Online media aim for reaching ever bigger audience and for attracting ever longer attention span. This competition creates an environment that rewards sensational, fake, and toxic news. To help limit their spread and impact, we propose and develop a news toxicity detector that can recognize various types of toxic content. While previous research primarily focused on English, here we target Bulgarian. We created a new dataset by crawling a website that for five years has been collecting Bulgarian news articles that were manually categorized into eight toxicity groups. Then we trained a multi-class classifier with nine categories: eight toxic and one non-toxic. We experimented with different representations based on ElMo, BERT, and XLM, as well as with a variety of domain-specific features. Due to the small size of our dataset, we created a separate model for each feature type, and we ultimately combined these models into a meta-classifier. The evaluation results show an accuracy of 59.0% and a macro-F1 score of 39.7%, which represent sizable improvements over the majority-class baseline (Acc=30.3%, macro-F1=5.2%).

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“…'you'), numbers, and celebrity references (Chen et al, 2015). These features can therefore be used within standard NLP methodologies: Chakraborty et al (2016) achieved 93% classification accuracy on a corpus including 7,500 English clickbait headlines using a set of 14 such features in a Support Vector Machine (SVM) classifier.…”

Section: Clickbaitmentioning

confidence: 99%

“…), and is therefore unlikely to be detected through these stylometric means. It is therefore rather more subtle than archetypal clickbait as targeted by the methods suggested by Chen et al (2015); Chakraborty et al (2016).…”

Section: Clickbaitmentioning

confidence: 99%

Incongruent Headlines: Yet Another Way to Mislead Your Readers

Chesney¹,

Liakata²,

Poesio³

et al. 2017

Proceedings of the 2017 EMNLP Workshop: Natural Language Processing Meets Journalism

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This paper discusses the problem of incongruent headlines: those which do not accurately represent the information contained in the article with which they occur. We emphasise that this phenomenon should be considered separately from recognised problematic headline types such as clickbait and sensationalism, arguing that existing natural language processing (NLP) methods applied to these related concepts are not appropriate for the automatic detection of headline incongruence, as an analysis beyond stylistic traits is necessary. We therefore suggest a number of alternative methodologies that may be appropriate to the task at hand as a foundation for future work in this area. In addition, we provide an analysis of existing data sets which are related to this work, and motivate the need for a novel data set in this domain.

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Stop Clickbait: Detecting and preventing clickbaits in online news media

Cited by 309 publications

References 8 publications

A Batched Multi-Armed Bandit Approach to News Headline Testing

A Batched Multi-Armed Bandit Approach to News Headline Testing

Detecting Toxicity in News Articles: Application to Bulgarian

Incongruent Headlines: Yet Another Way to Mislead Your Readers

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