A Survey on Natural Language Processing for Fake News Detection

By Ray Oshikawa, Jing Qian, William Yang Wang. 2018

380 citations recorded by the source corpus.

Fake news detection is a critical yet challenging problem in Natural Language Processing (NLP). The rapid rise of social networking platforms has not only yielded a vast increase in information accessibility but has also accelerated the spread of fake news. Thus, the effect of fake news has been growing, sometimes extending to the offline world and threatening public safety. Given the massive amount of Web content, automatic fake news detection is a practical NLP problem useful to all online content providers, in order to reduce the human time and effort to detect and prevent the spread of fake news. In this paper, we describe the challenges involved in fake news detection and also describe related tasks. We systematically review and compare the task formulations, datasets and NLP solutions that have been developed for this task, and also discuss the potentials and limitations of them. Based on our insights, we outline promising research directions, including more fine-grained, detailed, fair, and practical detection models. We also highlight the difference between fake news detection and other related tasks, and the importance of NLP solutions for fake news detection.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2022, background): “Malicious manipulations can be detected by employing approaches for automated fact checking, propaganda, or fake news detection using existing annotated datasets (Thorne et al., 2018; Oshikawa et al., 2020; Martino et al., 2020b; Zhou and Zafarani, 2020; Guo et al., 2022).”
A Survey of Fake News (2020, methodology): “These surveys generally classify fake news detection models by the types of (deep) ML methods used [Oshikawa et al. 2018] or by whether they utilize social context information [Shu et al. 2017].”
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “Abusive language detection methods can build upon recent advances at detecting fake news to identify content-sharing likely to lead to violence (McLaughlin, 2018; Oshikawa et al., 2018).”
NewsEmbed: Modeling News through Pre-trained Document Representations (2021, background): “News classification tasks include fake news detection [34, 46] and new category classification.”