FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
By Kai Shu, Deepak Mahudeswaran, Suhang Wang, Dongwon Lee, Huan Liu. arXiv.org, 2018
320 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/ad6f321c4bf36ddfff1b18b0e1d31aa8c836217b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xinyi Zhou
- Kai Shu
- Neema Kotonya
- Ray Oshikawa
- Karishma Sharma
- Preslav Nakov
- Van-Hoang Nguyen
- Jing Qian
Citations in the corpus
- Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation (2022, methodology): “Evaluation Data We use two sets of human-written articles released by Nguyen et al. (2020) and Shu et al. (2018) to evaluate the effectiveness of our approach.”
- A Survey on Natural Language Processing for Fake News Detection (2018, methodology): “For example, FAKENEWSNET (Shu et al., 2017a; Shu et al., 2017b; Shu et al., 2018) is an ongoing data collection project for fake news research.”
- Combating Fake News (2019, methodology): “Another analysis of true and fake news captured the variation of sentiments in user replies, wherein it was observed that sentiment in replies to true news tendedmore toward neutral, in contrast to that for fake news, which tended more toward negative sentiments [100].”
- dEFEND: Explainable Fake News Detection (2019, methodology): “We utilize one of the comprehensive fake news detection benchmark dataset called FakeNewsNet [38, 40].”
- A Survey of Fake News (2020, methodology): “, CREDBANK [Mitra and Gilbert 2015], LIAR [Wang 2017], FakeNewsNet [Shu et al. 2018], FakevsSatire [Golbeck et al.”
- A survey on fake news and rumour detection techniques (2019, background): “by [84], that has collected true and false statements from news and social media with a number of other context-based features.”
- Explainable Automated Fact-Checking: A Survey (2020, background): “There are also corpora which consist of real-world or naturally occurring claims, including claims from multiple domains, such as Mul-tiFC (Augenstein et al., 2019), FakeNewsNet (Shu et al., 2018), and a corpus with fine-grained evi-1 dence (Hanselowski et al., 2019).”
- FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (2020, methodology): “We conducted our experiments on a Twitter dataset collected by related work on rumor classification [20, 25] and fake news detection [37].”
- Beyond News Contents: The Role of Social Context for Fake News Detection (2017, methodology): “We utilize one of the comprehensive fake news detection benchmark dataset called FakeNewsNet [31, 32].”