Does social media users’ commenting behavior differ by their local community tie? A computer–assisted linguistic analysis approach

  • Weiai Wayne Xu Department of Communication at the University at Buffalo, The State University of New York
  • Liangyue Li Department of Electrical and Computer Engineering at Northeastern University in Boston, Mass.
  • Michael A. Stefanone Department of Communication at the University at Buffalo, The State University of New York
  • Yun Fu Assistant Professor and Founding Director of the SMILE Lab in the Department of Electrical and Computer Engineering at Northeastern University at Boston, Mass.
Keywords: linguistic analysis, social media, opinion mining, liwc

Abstract

This study is an exploratory attempt to use automatic linguistic analysis for understanding social media users’ news commenting behavior. The study addresses geographically–based dynamics in human–computer interaction, namely, users’ tie to a geographic community. Specifically, the study reveals that commenting behavior differs between users of different levels of local community tie. Comments by local users, those with higher level of local community tie, exhibit different linguistic patterns in comparison to national users who are less involved in local community. The linguistic differences are reflected in the use of pronouns, personal pronouns, social words, swear words, anxiety words and anger words. We argue that identification of the difference is crucial in the practice of mining social media conversations for public opinion.

Published
2013-12-31
How to Cite
Xu, W. W., Li, L., Stefanone, M. A., & Fu, Y. (2013). Does social media users’ commenting behavior differ by their local community tie? A computer–assisted linguistic analysis approach. First Monday, 19(1). https://doi.org/10.5210/fm.v19i1.4821