A system for intergroup prejudice detection: The case of microblogging under terrorist attacks

Haimonti Dutta, Kyounghee Kwon, H. Raghav Rao

Research output: Contribution to journalArticle

5 Scopus citations

Abstract

Intergroup prejudice is a distorted opinion held by one social group about another, without examination of facts. It is heightened during crises or threat. It finds expression in social media platforms when a group of people express anger, resentment and dissent towards another. This paper presents a system for automated detection of prejudiced messages from social media feeds. It uses a knowledge discovery framework that preprocesses data, generates theory-driven linguistic features along with other features engineered from textual content, annotates and models historical data to determine what drives detection of intergroup prejudice especially during a crisis. It is tested on tweets collected during the Boston Marathon bombing event. The system can be used to curb abuse and harassment by timely detection and reporting of intergroup prejudice.

Original languageEnglish (US)
JournalDecision Support Systems
DOIs
StateAccepted/In press - Jan 1 2018

Keywords

  • Intergroup prejudice detection system
  • Logistic regression with regularization
  • Machine learning
  • Social media text classification

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management

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