# A Survey on Truth Discovery

arXiv: https://arxiv.org/abs/1505.02463 · HTML: https://mlsanity.com/paper/1505.02463/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2015.

## Built on by (most cited first, 30 of 36)

- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Peering into the Mind of Language Models: An Approach for Attribution in Contextual Question Answering](https://mlsanity.com/paper/2405.17980/) (2024)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)
- [Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale](https://mlsanity.com/paper/2204.07309/) (2022)
- [The Role of Context in Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2104.07423/) (2021)
- [Findings of the NLP4IF-2021 Shared Tasks on Fighting the COVID-19 Infodemic and Censorship Detection](https://mlsanity.com/paper/2109.12986/) (2021)
- [Data Poisoning Attacks and Defenses to Crowdsourcing Systems](https://mlsanity.com/paper/2102.09171/) (2021)
- [Dataset of Fake News Detection and Fact Verification: A Survey](https://mlsanity.com/paper/2111.03299/) (2021)
- [CrowdChecked: Detecting Previously Fact-Checked Claims in Social Media](https://mlsanity.com/paper/2210.04447/) (2022)
- [From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques](https://mlsanity.com/paper/2107.00948/) (2021)
- [Reliability Estimation of News Media Sources: Birds of a Feather Flock Together](https://mlsanity.com/paper/2404.09565/) (2024)
- [Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems](https://mlsanity.com/paper/2107.00068/) (2021)
- [Truth Discovery in Sequence Labels from Crowds](https://mlsanity.com/paper/2109.04470/) (2021)
- [A Second Pandemic? Analysis of Fake News About COVID-19 Vaccines in Qatar](https://mlsanity.com/paper/2109.11372/) (2021)
- [Uncertainty Management in the Construction of Knowledge Graphs: a Survey](https://mlsanity.com/paper/2405.16929/) (2024)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [Combining Machine Learning with Knowledge Engineering to detect Fake News in Social Networks-a survey](https://mlsanity.com/paper/2201.08032/) (2022)
- [Trustworthy Knowledge Graph Completion Based on Multi-sourced Noisy Data](https://mlsanity.com/paper/2201.08580/) (2022)
- [WhatTheWikiFact: Fact-Checking Claims Against Wikipedia](https://mlsanity.com/paper/2105.00826/) (2021)
- [Empirical Bayes approach to Truth Discovery problems](https://mlsanity.com/paper/2206.04816/) (2022)
- [A Light-weight, Effective and Efficient Model for Label Aggregation in Crowdsourcing](https://mlsanity.com/paper/2212.00007/) (2022)
- [Objective Decoupling in Social Reinforcement Learning: Recovering Ground Truth from Sycophantic Majorities](https://mlsanity.com/paper/2602.08092/) (2026)
- [Nexus at ArAIEval Shared Task: Fine-Tuning Arabic Language Models for Propaganda and Disinformation Detection](https://mlsanity.com/paper/2311.03184/) (2023)
- [Connecting Large Language Models with Blockchain: Advancing the Evolution of Smart Contracts from Automation to Intelligence](https://mlsanity.com/paper/2412.02263/) (2024)
- [E-CaTCH: Event-Centric Cross-Modal Attention with Temporal Consistency and Class-Imbalance Handling for Misinformation Detection](https://mlsanity.com/paper/2508.11197/) (2025)
- [CPTAM: Constituency Parse Tree Aggregation Method](https://mlsanity.com/paper/2201.07905/) (2022)
- [Who's the Expert? On Multi-source Belief Change](https://mlsanity.com/paper/2205.00077/) (2022)

## Abstract (arXiv)

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.