Fake News: A Survey of Research, Detection Methods, and Opportunities

By Xinyi Zhou, Reza Zafarani. arXiv.org, 2018

314 citations recorded by the source corpus.

The explosive growth in fake news and its erosion to democracy, justice, and public trust has increased the demand for fake news analysis, detection and intervention. This survey comprehensively and systematically reviews fake news research. The survey identifies and specifies fundamental theories across various disciplines, e.g., psychology and social science, to facilitate and enhance the interdisciplinary research of fake news. Current fake news research is reviewed, summarized and evaluated. These studies focus on fake news from four perspective: (1) the false knowledge it carries, (2) its writing style, (3) its propagation patterns, and (4) the credibility of its creators and spreaders. We characterize each perspective with various analyzable and utilizable information provided by news and its spreaders, various strategies and frameworks that are adaptable, and techniques that are applicable. By reviewing the characteristics of fake news and open issues in fake news studies, we highlight some potential research tasks at the end of this survey.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/bc8c416f821b93795370524247c8a455c373ee6e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Kai Shu
2. Pratik Narang
3. Rohit Kumar Kaliyar
4. Huan Liu
5. Suhang Wang
6. Dongwon Lee
7. Michael M. Bronstein
8. Darsh J. Shah

In Sociology and Political Science

1. Kai Shu
2. Pratik Narang
3. Rohit Kumar Kaliyar
4. Xinyi Zhou
5. Huan Liu
6. Suhang Wang
7. Tal Schuster
8. Dongwon Lee

In Signal Processing

1. Federico Monti
2. Michael M. Bronstein
3. Regina Barzilay
4. Pratik Narang
5. Huan Liu
6. Rohit Kumar Kaliyar

Citations in the corpus

• The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, result): “This definition matches the one of Zhou and Zafarani (2018) and is in line with the disinformation focused view of Wardle and Derakhshan (2017).”
• dEFEND: Explainable Fake News Detection (2019, background): “Fake news detection methods generally focus on using news contents and social contexts [40, 51, 52].”
• FakeBERT: Fake news detection in social media with a BERT-based deep learning approach (2021, methodology): “In one of the studies, Zhou et al [53] have investigated the ability of social media to aggregate the judgments of a large community of users.”
• Fake News Detection on Social Media using Geometric Deep Learning (2019, background): “Existing approaches for fake news detection can be divided into three main categories, based on content , social context , and propagation [36, 44].”