Truth Discovery with Multiple Conflicting Information Providers on the Web

By Xiaoxin Yin, Jiawei Han, Philip S. Yu. IEEE Transactions on Knowledge and Data Engineering, 2007

771 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems

View Paper: https://www.semanticscholar.org/paper/51a89ef64da0f80503a7ddacd214c947400e5195

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Citations in the corpus

Integrating Conflicting Data: The Role of Source Dependence (2009, methodology): “We used a golden standard that contains 100 randomly selected books and the list of authors found on the cover of each book (the same as used in [16]).”
SLiMFast: Guaranteed Results for Data Fusion and Source Reliability (2015, background): “…collaborators from a medical school—who are currently engaged in extracting information from scientific articles to populate a structured data repository—we find that using data fusion can be challenging for users despite the numerous approaches proposed in the literature [9, 14, 21, 22, 28, 38].”
Review Graph Based Online Store Review Spammer Detection (2011, methodology): “For ease of presentation, we give the notations of node features that are related to the computation of the above definitions in Table I.”
Credibility Assessment of Textual Claims on the Web (2016, methodology): “Our work draws motivation from the following areas: Truth discovery: In approaches to truth discovery [2, 3, 7, 10, 14, 19], the goal is to resolve conflicts in multi-source data.”
News Verification by Exploiting Conflicting Social Viewpoints in Microblogs (2016, methodology): “By formulating the credibility propagation on the credibility network as a graph optimization problem, entities’ credibility values are propagated to obtain a ﬁnal result (Yin, Han, and Yu 2008; Yin and Tan 2011; Vydiswaran, Zhai, and Roth 2011).”
Language-Aware Truth Assessment of Fact Candidates (2014, methodology): “Implemented as described in (Yin et al., 2007). iii) Investment is also based on transitive voting, however scores are updated differently.”
Big data integration (2013, background): “To address such veracity related challenges, techniques have been proposed to find the single truth from conflicting values [49], [32], [48], [39], [38], [18], [9], [47] and to find multiple truths [50].”
Explaining Credibility in News Articles using Cross-Referencing (2019, background): “Ourmain assumption is that well-communicated and cross-referenced information is more likely to be credible, in line with [14, 16].”
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration (2012, methodology): “While this dataset has been used previously [4, 14], there all the authors observed by each source for the same book were concatenated as one single claim.”
A Confidence-Aware Approach for Truth Discovery on Long-Tail Data (2014, methodology): “Many truth discovery methods [8,16,19,20,23,23–26,31, 34–37] use weighted voting or averaging more or less to achieve the truths, which overcome the issue of conventional voting or averaging schema that assumes all the sources are equally reliable.”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, methodology): “introduced TruthFinder as an unsupervised fact-finder for trust analysis on a providersfacts network [33].”
Fusing data with correlations (2014, background): “Among these approaches, [6, 14, 19, 20, 21, 23, 24] jointly infer truth and source quality, but they assume the conflicting-triple, closedworld semantics.”
Truth Finding on the Deep Web: Is the Problem Solved? (2012, methodology): “TRUTHFINDER tends to compute very high accuracy, on average .97, 14% higher than the sampled ones.”
Mining heterogeneous information networks: a structural analysis approach (2013, methodology): “By constructing an information network that links multiple information providers with multiple versions of the stated facts for each entity to be resolved, novel network analysis methods, such as TruthFinder [28] and”
Knowing What to Believe (when you already know something) (2010, methodology): “TruthFinder (Yin et al., 2008) is pseudoprobabilis-tic: the basic version of the algorithm below calculates the “probability” of a claim by assuming that each source’s trustworthiness is the probability of it being correct and then averages claim beliefs to obtain trustworthiness scores.”
DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning (2018, methodology): “Truth discovery: Prior approaches for truth discovery (Yin et al., 2008; Dong et al., 2009, 2015; Li et al., 2011, 2014, 2015; Pasternack and Roth, 2011, 2013; Ma et al., 2015; Zhi et al., 2015; Gao et al., 2015; Lyu et al., 2017) have focused on structured data with the goal of addressing the probl”
Evaluating Event Credibility on Twitter (2012, methodology): “[27] introduced a fact finder algorithm TruthFinder which performs trust analysis on a providers-claims network.”
Stacked Ensembles of Information Extractors for Knowledge-Base Population (2015, background): “facts from multiple sources, each with a latent credibility (Yin et al., 2008).”