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This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News

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arxiv 1703.09398 v1 pith:6WD2DDHR submitted 2017-03-28 cs.SI cs.CL

classification cs.SIcs.CL
keywords newsfakerealargumentsassumptionconcludecontentproblem
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. A Research Vision for Web Search on Emerging Topics

    cs.IR 2025-09 accept novelty 4.0 of 10

    The paper lays out three research questions to guide the study and redesign of web search for emerging topics, focused on user knowledge, dynamic topic awareness, and responsible opinion formation.

  2. Detecting Toxicity in News Articles: Application to Bulgarian

    cs.CL 2019-08 conditional novelty 4.0 of 10

    The authors introduce a 317-article Bulgarian news toxicity dataset and show that a stacked classifier using LSA, BERT, and other features outperforms the majority-class baseline.

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