The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Misleading Online Content: Recognizing Clickbait as "False News"
Abstract
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.
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Cites
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BackgroundAutomatic deception detection: Methods for finding fake news
Conroy, N. K., Rubin, V. L., & Chen, Y. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010082 · Cited by 979
Self-citationDeception detection for news: Three types of fakes
Rubin, V. L., Chen, Y., & Conroy, N. K. (2015) · Proceedings of the Association for Information Science and Technology · doi:10.1002/pra2.2015.145052010083 · Cited by 546
Self-citationHard and soft news: A review of concepts, operationalizations and key findings
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Click bait: Forward-reference as lure in online news headlines
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Tabloid journalism and the public sphere: a historical perspective on tabloid journalism
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Cited by
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
CSI: A Hybrid Deep Model for Fake News Detection
Ruchansky, N., Seo, S., & Liu, Y. (2017) · Proceedings of the 2017 ACM on Conference on Information and Knowledge Management · doi:10.1145/3132847.3132877 · Cited by 1,039
An overview of online fake news: Characterization, detection, and discussion
Zhang, X., & Ghorbani, A. A. (2020) · Information Processing & Management · doi:10.1016/j.ipm.2019.03.004 · Cited by 997
A survey on fake news and rumour detection techniques
Bondielli, A., & Marcelloni, F. (2019) · Information Sciences · doi:10.1016/j.ins.2019.05.035 · Cited by 658
Combating Fake News: A Survey on Identification and Mitigation Techniques
Sharma, K., Qian, F., Jiang, H., Ruchansky, N., Zhang, M., & Liu, Y. (2019) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3305260 · Cited by 514
Metadata
- Authors
- Chen, Y., Conroy, N. J., & Rubin, V. L.
- Venue
- Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection
- Publisher
- ACM
- Published
- 2015-11-13
- Pages
- 15-19
- OpenAlex
- W2248267741
- Cited by
- 432
- References
- 22
- FWCI
- 65.69
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chen, Y., Conroy, N. J., & Rubin, V. L. (2015). Misleading Online Content: Recognizing Clickbait as "False News". Proceedings of the 2015 ACM on Workshop on Multimodal Deception Detection. https://doi.org/10.1145/2823465.2823467
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