A Neural Network Approach for Truth Discovery in Social Sensing

Jermaine Marshall, Arturo Argueta, Dong Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationProceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages343-347
Number of pages5
ISBN (Electronic)9781538623237
DOIs
StatePublished - Nov 14 2017
Externally publishedYes
Event14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017 - Orlando, United States
Duration: Oct 22 2017Oct 25 2017

Publication series

NameProceedings - 14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017

Conference

Conference14th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, MASS 2017
Country/TerritoryUnited States
CityOrlando
Period10/22/1710/25/17

Keywords

  • Deep Learning
  • Neural Network
  • Relational Dependency
  • Social Sensing
  • Truth Discovery
  • Twitter

ASJC Scopus subject areas

  • Mechanical Engineering
  • Instrumentation
  • Electrical and Electronic Engineering

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