Identifying Clickbait

Kumar, Vaibhav; Khattar, Dhruv; Gairola, Siddhartha; Lal, Yash Kumar; Varma, Vasudeva

doi:10.1145/3209978.3210144

Cited by 40 publications

(9 citation statements)

References 5 publications

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“…They usually generate revenue via (1) advertisements on their websites or (2) a subscription-based model for articles that readers might be interested in. Writers have started using clickbait to attract more readers and boost the number of clicks on their material, therefore raising their agency's revenue [39].…”

Section: Clickbait or Nonclickbaitmentioning

confidence: 99%

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

Maleki¹,

Padmanabhan²,

Dutta³

2023

J Med Internet Res

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Background While there is high-quality online health information, a lot of recent work has unfortunately highlighted significant issues with the health content on social media platforms (eg, fake news and misinformation), the consequences of which are severe in health care. One solution is to investigate methods that encourage users to post high-quality content. Objective Incentives have been shown to work in many domains, but until recently, there was no method to provide financial incentives easily on social media for users to generate high-quality content. This study investigates the following question: What effect does the provision of incentives have on the creation of social media health care content? Methods We analyzed 8328 health-related posts from an incentive-based platform (Steemit) and 1682 health-related posts from a traditional platform (Reddit). Using topic modeling and sentiment analysis–based methods in machine learning, we analyzed these posts across the following 3 dimensions: (1) emotion and language style using the IBM Watson Tone Analyzer service, (2) topic similarity and difference from contrastive topic modeling, and (3) the extent to which posts resemble clickbait. We also conducted a survey using 276 Amazon Mechanical Turk (MTurk) users and asked them to score the quality of Steemit and Reddit posts. Results Using the Watson Tone Analyzer in a sample of 2000 posts from Steemit and Reddit, we found that more than double the number of Steemit posts had a confident language style compared with Reddit posts (77 vs 30). Moreover, 50% more Steemit posts had analytical content and 33% less Steemit posts had a tentative language style compared with Reddit posts (619 vs 430 and 416 vs 627, respectively). Furthermore, more than double the number of Steemit posts were considered joyful compared with Reddit posts (435 vs 200), whereas negative posts (eg, sadness, fear, and anger) were 33% less on Steemit than on Reddit (384 vs 569). Contrastive topic discovery showed that only 20% (2/10) of topics were common, and Steemit had more unique topics than Reddit (5 vs 3). Qualitatively, Steemit topics were more informational, while Reddit topics involved discussions, which may explain some of the quantitative differences. Manual labeling marked more Steemit headlines as clickbait than Reddit headlines (66 vs 26), and machine learning model labeling consistently identified a higher percentage of Steemit headlines as clickbait than Reddit headlines. In the survey, MTurk users said that at least 57% of Steemit posts had better quality than Reddit posts, and they were at least 52% more likely to like and comment on Steemit posts than Reddit posts. Conclusions It is becoming increasingly important to ensure high-quality health content on social media; therefore, incentive-based social media could be important in the design of next-generation social platforms for health information.

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Section: Clickbait or Nonclickbaitmentioning

confidence: 99%

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

Maleki¹,

Padmanabhan²,

Dutta³

2023

J Med Internet Res

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“…Kaur et al (2020) suggested an approach for detecting clickbait that combines CNN and RNN, which provides higher results than earlier methods based simply on CNN (Agrawal, 2016). Using various neural network topologies, Kumar et al , 2018 and Dong et al (2019) attempted to capture the probable link between news headlines and news content.…”

Section: Literature Reviewmentioning

confidence: 99%

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Wang,

Maslim,

Liu

2023

DTA

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PurposeA clickbait is a deceptive headline designed to boost ad revenue without presenting closely relevant content. There are numerous negative repercussions of clickbait, such as causing viewers to feel tricked and unhappy, causing long-term confusion, and even attracting cyber criminals. Automatic detection algorithms for clickbait have been developed to address this issue. The fact that there is only one semantic representation for the same term and a limited dataset in Chinese is a need for the existing technologies for detecting clickbait. This study aims to solve the limitations of automated clickbait detection in the Chinese dataset.Design/methodology/approachThis study combines both to train the model to capture the probable relationship between clickbait news headlines and news content. In addition, part-of-speech elements are used to generate the most appropriate semantic representation for clickbait detection, improving clickbait detection performance.FindingsThis research successfully compiled a dataset containing up to 20,896 Chinese clickbait news articles. This collection contains news headlines, articles, categories and supplementary metadata. The suggested context-aware clickbait detection (CA-CD) model outperforms existing clickbait detection approaches on many criteria, demonstrating the proposed strategy's efficacy.Originality/valueThe originality of this study resides in the newly compiled Chinese clickbait dataset and contextual semantic representation-based clickbait detection approach employing transfer learning. This method can modify the semantic representation of each word based on context and assist the model in more precisely interpreting the original meaning of news articles.

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“…Because we perceive of these functions as integral to headlines, we do not refer to headlines as clickbait in this study. We prefer to reserve the term clickbait for strategies aimed at provoking the reader to click by overpromising or misrepresenting what the reader can expect from reading the article and/or strategies that are otherwise misleading (Kumar et al 2018;Chakraborty et al 2016;Chen et al 2015).…”

Section: Headlines and Clickbaitmentioning

confidence: 99%

Raising Clickworthiness: Effects of Foregrounding News Values in Online Newspaper Headlines

Lagerwerf

Govaert

2020

News Values From an Audience Perspective

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Identifying Clickbait

Cited by 40 publications

References 5 publications

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

The Effect of Monetary Incentives on Health Care Social Media Content: Study Based on Topic Modeling and Sentiment Analysis

CA-CD: context-aware clickbait detection using new Chinese clickbait dataset with transfer learning method

Raising Clickworthiness: Effects of Foregrounding News Values in Online Newspaper Headlines

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