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

Abstract

This paper 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 challenge of social sensing 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, 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. Consider the correlation between different features, 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 further develop a constrained EM with multiple features (CEM-MultiF) that incorporates multiple corroborating features as a constraint to boost the estimation accuracy. Finally, we evaluate the performance of the proposed algorithms 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)
JournalIEEE Transactions on Computers
DOIs
StateAccepted/In press - 2020

Keywords

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

ASJC Scopus subject areas

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

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