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        <title>Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues</title>
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            <namePart type="given">Anna</namePart>
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    <abstract>The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.</abstract>
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%0 Conference Proceedings
%T Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues
%A Levi, Or
%A Hosseini, Pedram
%A Diab, Mona
%A Broniatowski, David
%Y Feldman, Anna
%Y Da San Martino, Giovanni
%Y Barrón-Cedeño, Alberto
%Y Brew, Chris
%Y Leberknight, Chris
%Y Nakov, Preslav
%S Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F levi-etal-2019-identifying
%X The blurry line between nefarious fake news and protected-speech satire has been a notorious struggle for social media platforms. Further to the efforts of reducing exposure to misinformation on social media, purveyors of fake news have begun to masquerade as satire sites to avoid being demoted. In this work, we address the challenge of automatically classifying fake news versus satire. Previous work have studied whether fake news and satire can be distinguished based on language differences. Contrary to fake news, satire stories are usually humorous and carry some political or social message. We hypothesize that these nuances could be identified using semantic and linguistic cues. Consequently, we train a machine learning method using semantic representation, with a state-of-the-art contextual language model, and with linguistic features based on textual coherence metrics. Empirical evaluation attests to the merits of our approach compared to the language-based baseline and sheds light on the nuances between fake news and satire. As avenues for future work, we consider studying additional linguistic features related to the humor aspect, and enriching the data with current news events, to help identify a political or social message.
%R 10.18653/v1/D19-5004
%U https://aclanthology.org/D19-5004
%U https://doi.org/10.18653/v1/D19-5004
%P 31-35

Markdown (Informal)

[Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues](https://aclanthology.org/D19-5004) (Levi et al., NLP4IF 2019)

• Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues (Levi et al., NLP4IF 2019)

ACL

• Or Levi, Pedram Hosseini, Mona Diab, and David Broniatowski. 2019. Identifying Nuances in Fake News vs. Satire: Using Semantic and Linguistic Cues. In Proceedings of the Second Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda, pages 31–35, Hong Kong, China. Association for Computational Linguistics.