Unsupervised Fact-Finding With Multi-Modal Data In Social Sensing

This paper develops unsupervised fact-finding algorithms that combine consideration of multi-modal microblog content features with analysis of propagation patterns to determine veracity of microblog observations. 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. To offer robustness, we describe fact-finding extensions that handle the existence of malicious colluding sources. 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.