A Survey on Truth Discovery

By Yaliang Li, Jing Gao, Chuishi Meng, Qi Li, Lu Su, Bo Zhao, Wei Fan, Jiawei Han. 2015

449 citations recorded by the source corpus.

Thanks to information explosion, data for the objects of interest can be collected from increasingly more sources. However, for the same object, there usually exist conflicts among the collected multi-source information. To tackle this challenge, truth discovery, which integrates multi-source noisy information by estimating the reliability of each source, has emerged as a hot topic. Several truth discovery methods have been proposed for various scenarios, and they have been successfully applied in diverse application domains. In this survey, we focus on providing a comprehensive overview of truth discovery methods, and summarizing them from different aspects. We also discuss some future directions of truth discovery research. We hope that this survey will promote a better understanding of the current progress on truth discovery, and offer some guidelines on how to apply these approaches in application domains.

Subfields: Artificial Intelligence, Computer Science Applications, Information Systems, Natural Language Processing

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Citations in the corpus

Snorkel: Rapid Training Data Creation with Weak Supervision (2017, background): “Related Data Management Problems: Researchers have considered related problems in data management, such as data fusion [15,40] and truth discovery [28].”
Predicting Factuality of Reporting and Bias of News Media Sources (2018, background): “Yet another survey was performed by Li et al. (2016), covering truth discovery in general.”
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, methodology): “Most existing data fusion methods adopt iterative procedures like EM to estimate the quality of sources and compute the true values of objects [25].”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Truth discovery is the problem of detecting true facts from multiple conflicting sources [46].”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, background): “State of the Art and Limitations: Prior work on “truth discovery” (see Li et al. (2016) for survey) 1 largely focused on structured facts, typically in the form of subject-predicate-object triples, or on social media platforms like Twitter, Sina Weibo, etc. Recently, methods have been proposed to…”
Credibility Assessment of Textual Claims on the Web (2016, background): “State of the Art and its Limitations: Prior work on credibility analysis (see [9] for a survey) has focused on factual claims (e.”
A Survey on Computational Propaganda Detection (2020, background): “Finally, it is worth noting that, even though there have been several recent surveys on fake news detection [Shu at al., 2017; Zhou et al., 2019], fact-checking [Thorne and Vlachos, 2018], and truth discovery [Li et al., 2016], none of them focuses on computational propaganda.”
SemEval-2020 Task 11: Detection of Propaganda Techniques in News Articles (2020, background): “Thorne and Vlachos (2018) surveyed fact-checking approaches to fake news and related problems, and Li et al. (2016) focused on truth discovery in general.”
Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document (2021, background): “Thus, automatic factchecking has been proposed as a possible alternative (Li et al. 2016; Shu et al. 2017; Rashkin et al. 2017; Hassan et al. 2017; Vo and Lee 2018; Lee, Wu, and Fung 2018; Li et al. 2018; Thorne and Vlachos 2018; Lazer et al. 2018; Vosoughi, Roy, and Aral 2018; Zhang et al. 2020b), ”
A Survey on Automated Fact-Checking (2021, background): “For real-world applications, evidence must be curated through the manual efforts of journalists (Borel, 2016), automated means (Li et al., 2015), or their combination.”
Snuba: Automating Weak Supervision to Label Training Data (2018, unknown): “Areas like data fusion [13, 41, 38] and truth discovery [30] also look at the problem of estimating how reliable different data sources are while utilizing probabilistic graphical models like Snuba.”