Constraint-aware dynamic truth discovery in big data social media sensing | IEEE Conference Publication | IEEE Xplore

Constraint-aware dynamic truth discovery in big data social media sensing


Abstract:

Social media sensing has emerged as a new big data application paradigm to collect observations and claims about the measured variables in physical environment from commo...Show More

Abstract:

Social media sensing has emerged as a new big data application paradigm to collect observations and claims about the measured variables in physical environment from common citizens. A fundamental problem in social media sensing applications lies in estimating the evolving truth of claims and the reliability of data sources without knowing either of them a priori, which is referred to as dynamic truth discovery. We identified two critical challenges that are not fully addressed by solutions from current literature. The first challenge is “physical constraint-awareness” where the transition of truth is constrained by some physical rules that must be followed to ensure correct estimation of the evolving truth. The second one is “noisy and incomplete data” where the social media sensing data is sparse in nature and contains a lot of rumors and misinformation, making it difficult to capture the constantly evolving truth of measured variables. In this paper, we developed a new Constraint-Aware Dynamic Truth Discovery (CA-DTD) scheme to address the above challenges. To address the physical constraint-awareness challenge, CA-DTD develops a new constraint-aware Hidden Markov Model to effectively infer the evolving truth of measured variables by incorporating physical constraints. To address the noisy and incomplete data challenge, CA-DTD fuses sensing observations from online social media with information from traditional news media using a principled approach. We evaluate CA-DTD scheme using two real-world social media sensing data traces and the results show that CA-DTD significantly outperforms the state-of-the-art baselines.
Date of Conference: 11-14 December 2017
Date Added to IEEE Xplore: 15 January 2018
ISBN Information:
Conference Location: Boston, MA, USA

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