Abstract:
Social sensing has emerged as a new application paradigm in networked sensing communities where a colossal amount of observations about the physical world are contributed...Show MoreMetadata
Abstract:
Social sensing has emerged as a new application paradigm in networked sensing communities where a colossal amount of observations about the physical world are contributed by people or devices they use. Our work solves a critical challenge in social sensing applications where the goal is to estimate the reliability of social sensors and the truthfulness of observed variables (typically known as claims) with little prior knowledge on either of them. This challenge is referred to as truth discovery. An important limitation in the previous truth discovery solutions is that they assume the relationship between source reliability and claim truthfulness can be represented by simplified functions (e.g., linear, quadratic and binomial). This assumption leads to suboptimal truth discovery results because the exact relational dependency between sources and claims is often unknown a priori. In this paper, we show that a neural network approach can learn the complex relational dependency better than the previous truth discovery methods. In particular, we develop a multi-layer neural network model that solves the truth discovery problem in social sensing without any assumption on the prior knowledge of the source-claim relational dependency distribution. The performance of our model is evaluated through two real-world events using data crawled from Twitter. The evaluation results show that our neural network approach significantly outperforms previous truth discovery methods.
Date of Conference: 22-25 October 2017
Date Added to IEEE Xplore: 16 November 2017
ISBN Information:
Electronic ISSN: 2155-6814