This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News
By Benjamin D. Horne, Sibel Adali. Proceedings of the International AAAI Conference on Web and Social Media, 2017
681 citations recorded by the source corpus.
The problem of fake news has gained a lot of attention as it is claimed to have had a significant impact on 2016 US Presidential Elections. Fake news is not a new problem and its spread in social networks is well-studied. Often an underlying assumption in fake news discussion is that it is written to look like real news, fooling the reader who does not check for reliability of the sources or the arguments in its content. Through a unique study of three data sets and features that capture the style and the language of articles, we show that this assumption is not true. Fake news in most cases is more similar to satire than to real news, leading us to conclude that persuasion in fake news is achieved through heuristics rather than the strength of arguments. We show overall title structure and the use of proper nouns in titles are very significant in differentiating fake from real. This leads us to conclude that fake news is targeted for audiences who are not likely to read beyond titles and is aimed at creating mental associations between entities and claims.
Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/1b321bb759233167fc5a53009dd70782eee07cb9
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Citations in the corpus
- Predicting Factuality of Reporting and Bias of News Media Sources (2018, methodology): “Horne and Adali (2017) analyzed three small datasets ranging from a couple of hundred to a few thousand articles from a couple of dozen sources, comparing ( i ) real news vs. ( ii ) “fake news” vs. ( iii ) satire, and found that the latter two have a lot in common across a number of dimensions.”
- GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media (2020, background): “Session-based heterogeneous graph embedding (Jiang et al., 2018) is proposed to learn the traits of users so that they can be identified in shared accounts.”
- Combating Fake News (2019, background): “This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News.”
- False Information on Web and Social Media: A Survey (2018, methodology): “ithm category Opinion-based false information Fact-based false information (fake reviews) (false news and hoaxes) Feature-based Harris et al. [33], Jindal et al. [43], Gupta et al. [32], Horne et al. [37], Li et al. [51,52,54], Lin et al. [55], Kumar et al. [50], Perez et al. [74], Minnich et al. [5”
- Detecting opinion spams and fake news using text classification (2018, methodology): “Using the dataset of Horne and Adali, 17 we achieved an accuracy of 87%, which is considerably higher than the accuracy of 71% obtained by Horne and Adali using text features.”
- Investigating the emotional appeal of fake news using artificial intelligence and human contributions (2019, methodology): “Previous studies have confirmed that substantial differences exit and have suggested that the emotional appeal is one important feature by which to tell fake and real news apart (Bessi et al. , 2015; Chen et al. , 2015; Horne and Adali, 2017).”
- Detection of Online Fake News Using N-Gram Analysis and Machine Learning Techniques (2017, methodology): “[8] illustrated how obvious it is to distinguish between fake and honest articles.”