Misleading Online Content: Recognizing Clickbait as "False News"

By Yimin Chen, Niall Conroy, Victoria L. Rubin. WMDD@ICMI, 2015

493 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Literature and Literary Theory, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/dc049b06ae2a33ea5afe76d7f5136cb7afd5317d

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Citations in the corpus

FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media (2018, background): “For example, study has shown that the clickbaits headlines usually can serve as a good indicator for recognizing fake news articles (Chen, Conroy, and Rubin 2015; Shu et al. 2018).”
Stop Clickbait: Detecting and preventing clickbaits in online news media (2016, background): “[18] argued for labeling clickbaits as misleading content or false news.”
Characterizing and Detecting Hateful Users on Twitter (2018, background): “An interesting direction would be to analyze the sensationalism of their statements, as it has been done in the context of clickbaits (Chen, Conroy, and Rubin 2015).”
A survey on fake news and rumour detection techniques (2019, background): “Clickbait refers to article titles or social media posts whose aim is to attract readers to follow a link to the actual article page [19].”
FakeNewsNet: A Data Repository with News Content, Social Context, and Spatiotemporal Information for Studying Fake News on Social Media (2018, unknown): “For example, a study has shown that the clickbait’s headlines usually can serve as a good indicator for recognizing fake news articles (Chen, Conroy, and Rubin 2015; Shu et al. ).”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Often, news titles will summarize the major viewpoints of the article that the author wants to convey, and thus misleading and deceptive clickbait titles can serve as a good indicator for recognizing fake news articles [13].”
Text Mining in Big Data Analytics (2020, background): “Applications of blog mining vary and, among others, include opinion mining for agriculture [96], prospective industrial technologies [97], decision support in fashion buying processes [98], detection of major events [99], retrieval of information regarding popular tourist locations, and travel route”
Multimodal Fusion with Co-Attention Networks for Fake News Detection (2021, background): “…of paragraphs in the text (Volkova et al., 2017), the percentage of negative words (Potthast et al., 2017; Bond et al., 2017), the number of punctuation and emojis (Castillo et al., 2011), and semantic features, such as writing styles (Chen et al., 2015) and language styles (Feng et al., 2012).”
A Survey of Fake News (2020, background): “…false news [Vosoughi et al. 2018], satire news [Rubin et al. 2015; Tandoc et al. 2018; Wardle 2017], disinformation [Kshetri and Voas 2017; Wardle 2017], misinformation [Kucharski 2016; Wardle 2017], cherry-picking [Asudeh et al. 2020], clickbait [Chen et al. 2015], and rumor [Zubiaga et al. 2018].”
CSI: A Hybrid Deep Model for Fake News Detection (2017, background): “Advice columns encourage readers to consider how a story makes them feel – does it provoke either anger or an emotional response? e advice stems from the observation that fake news oen contains opinionated and inammatory language, craed as click bait or to incite confusion [8, 33].”
SpotFake: A Multi-modal Framework for Fake News Detection (2019, result): “is a bit different as compared to the earlier studies [1], [2].”
Preserving integrity in online social networks (2020, background): “Clickbaits may involve deception, which can be an indicator of misinformation [25].”