Truth Discovery with Multi-Modal Data in Social Sensing

Huajie Shao, Dachun Sun, Shuochao Yao, Lu Su, Zhibo Wang, Dongxin Liu, Shengzhong Liu, Lance Kaplan, Tarek Abdelzaher

Research output: Contribution to journalArticlepeer-review


This article proposes unsupervised truth-finding algorithms that combine consideration of multi-modal content features with analysis of propagation patterns to evaluate the veracity of observations in social sensing applications. A key social sensing challenge is to develop effective algorithms for estimating both the reliability of sources and the veracity of their observations without prior knowledge. In contrast to prior solutions that use labeled examples to learn content features that are correlated with veracity, our approach is entirely unsupervised. Hence, given no prior training data, we jointly learn the importance of different content features together with the veracity of observations using propagation patterns as an indicator of perceived content reliability. A novel penalized expectation maximization (PEM) algorithm is proposed to improve the quality of estimation results for observations bolstered by multiple features. In addition, we develop a constrained expectation maximum likelihood with multiple features (CEM-MultiF) that introduces a novel constraint to boost the probability of correctness of some claims. Finally, we evaluate the performance of the proposed algorithms, called EM-Multi, CEM-Multi and PEM-MultiF, respectively, on real-world data sets collected from Twitter. The evaluation results demonstrate that the proposed algorithms outperform the existing fact-finding approaches, and offer tunable knobs for controlling robustness/performance trade-offs in the presence of malicious sources.

Original languageEnglish (US)
Article number9138694
Pages (from-to)1325-1337
Number of pages13
JournalIEEE Transactions on Computers
Issue number9
StatePublished - Sep 1 2021


  • Social networks
  • estimation accuracy
  • multi-modal data
  • penalized expectation maximization (PEM)
  • truth discovery

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computational Theory and Mathematics


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