A Twitter-based approach of news media impartiality in multipartite political scenes

Abstract

News dissemination in the modern world deploys online social networks (OSNs) to instantly and freely convey facts and opinions to Internet users worldwide. Recent research studies the structure of the graph formed by the relationships between news readers and media outlets in OSNs to investigate the profile of the media and derive their political leanings. In this work, we focus on the notion of political impartiality in multipartite political scenes. Our aim is to describe the graph-theoretic attributes of the ideal outlet that exhibits an impartial stance towards all political groups and propose a methodology based on Twitter, an OSN with profound informative and political profile, to algorithmically approximate this ideal medium and evaluate the deviation of popular outlets from it. The magnitude of deviation is used to rank the existing outlets based on their political impartiality and, hence, tackle the bewildering question: Which are the most impartial news media in a political scene?. We utilize our techniques on a snapshot of the Twitter subgraph concerning the Greek political and news media scene in April 2018. The results of our approach are juxtaposed with the findings of a survey provided to a group of political scientists and the efficiency of our proposed methodology is soundly confirmed.

Supplementary Material

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Citation

@article{gyftopoulos2020twitter,
title={A Twitter-based approach of news media impartiality in multipartite political scenes},
author={Gyftopoulos, Sotirios and Drosatos, George and Stamatelatos, Giorgos and Efraimidis, Pavlos S},
journal={Social Network Analysis and Mining},
volume={10},
number={36},
pages={36},
year={2020},
doi = {10.1007/s13278-020-00642-x},
url = {https://link.springer.com/article/10.1007%2Fs13278-020-00642-x},
publisher={Springer}
}

BibTeX

@article{gyftopoulos2020twitter,
  title={A Twitter-based approach of news media impartiality in multipartite political scenes},
  author={Gyftopoulos, Sotirios and Drosatos, George and Stamatelatos, Giorgos and Efraimidis, Pavlos S},
  journal={Social Network Analysis and Mining},
  volume={10},
  number={36},
  pages={36},
  year={2020},
  doi = {10.1007/s13278-020-00642-x},
  url = {https://link.springer.com/article/10.1007%2Fs13278-020-00642-x},
  publisher={Springer}
}