Identifying Users with Opposing Opinions in Twitter Debates

Abstract

In recent times, social media sites such as Twitter have been extensively used for debating politics and public policies. These debates span millions of tweets and numerous topics of public importance. Thus, it is imperative that this vast trove of data is tapped in order to gain insights into public opinion especially on hotly contested issues such as abortion, gun reforms etc. Thus, in our work, we aim to gauge users' stance on such topics in Twitter. We propose ReLP, a semi-supervised framework using a retweet-based label propagation algorithm coupled with a supervised classifier to identify users with differing opinions. In particular, our framework is designed such that it can be easily adopted to different domains with little human supervision while still producing excellent accuracy.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2014
Accession Number
AD1124388

Entities

People

  • Ashwin Rajadesingan
  • Huan Liu

Organizations

  • Arizona State University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Algorithms
  • Computational Linguistics
  • Computational Science
  • Computer Languages
  • Data Mining
  • Language
  • Learning
  • Linguistics
  • Machine Learning
  • Media
  • Online Communications
  • Precision
  • Public Opinion
  • Public Policy
  • Social Computing
  • Social Media
  • Social Networking Services
  • Social Networks
  • Supervised Machine Learning
  • Supervision
  • Unsupervised Machine Learning

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Neural Network Machine Learning.
  • Strategic Security Studies

Technology Areas

  • AI & ML
  • AI & ML - Information Retrieval
  • AI & ML - Neural Networks