Computing Distrust in Social Media

Abstract

A myriad of social media services are emerging in recent years that allow people to communicate and express themselves conveniently and easily. The pervasive use of social media generates massive data at an unprecedented rate. It becomes increasingly difficult for online users to find relevant information or, in other words, exacerbates the information overload problem. Meanwhile, users in social media can be both passive content consumers and active content producers, causing the quality of user-generated content can vary dramatically from excellence to abuse or spam, which results in a problem of information credibility. Trust, providing evidence about with whom users can trust to share information and from whom users can accept information without additional verification, plays a crucial role in helping online users collect relevant and reliable information. It has been proven to be an effective way to mitigate information overload and credibility problems and has attracted increasing attention.

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Document Details

Document Type
Technical Report
Publication Date
May 01, 2015
Accession Number
AD1007379

Entities

People

  • Jiliang Tang

Organizations

  • Arizona State University

Tags

Communities of Interest

  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Science
  • Data Analysis
  • Data Mining
  • Feature Extraction
  • Information Overload
  • Information Science
  • Machine Learning
  • Network Science
  • Random Walk
  • Reliability
  • Social Media
  • Social Networks
  • Social Sciences
  • Statistics
  • Supervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Organizational Psychology.