Revealing the Political Affinity of Online Entities Through Their Twitter Followers

Abstract

In this work, we show that the structural features of the Twitter online social network can divulge valuable information about the political affinity of the participating nodes. More precisely, we show that Twitter followers can be used to predict the political affinity of prominent Nodes of Interest (NOIs) they opt to follow. We utilize a series of purely structure-based algorithmic approaches, such as modularity clustering, the minimum linear arrangement (MinLA) problem and the DeGroot opinion update model in order to reveal diverse aspects of the NOIs’ political profile. Our methods are applied to a dataset containing the Twitter accounts of the members of the Greek Parliament as well as an enriched dataset that additionally contains popular news sources. The results confirm the viability of our approach and provide evidence that the political affinity of NOIs can be determined with high accuracy via the Twitter follower network. Moreover, the outcome of an independently performed expert study about the offline political scene confirms the effectiveness of our methods.

Supplementary Material

Download PDF Download Dataset

The files ipm-politicians-* refer to the dataset with the MPs while the files ipm-enriched-* refer to the enriched dataset that contains the MPs and the news sources.

For each group, the collection contains the bipartite graph as well as the 7 projections used in this work.

The answers to the expert survey are contained in the respective file.

All the files are ASCII encoded edge lists with CRLF line terminators.

Twitter snapshot as of 14-04-2018.

Citation

@article{STAMATELATOS2020102172,
title = {Revealing the political affinity of online entities through their Twitter followers},
journal = {Information Processing \& Management},
volume = {57},
number = {2},
pages = {102172},
year = {2020},
issn = {0306-4573},
doi = {10.1016/j.ipm.2019.102172},
url = {http://www.sciencedirect.com/science/article/pii/S0306457319305023},
author = {Giorgos Stamatelatos and Sotirios Gyftopoulos and George Drosatos and Pavlos S. Efraimidis},
keywords = {Social network analysis, Twitter followers, News media, Political affinity}
}

BibTeX

@article{STAMATELATOS2020102172,
  title = {Revealing the political affinity of online entities through their Twitter followers},
  journal = {Information Processing \& Management},
  volume = {57},
  number = {2},
  pages = {102172},
  year = {2020},
  issn = {0306-4573},
  doi = {10.1016/j.ipm.2019.102172},
  url = {http://www.sciencedirect.com/science/article/pii/S0306457319305023},
  author = {Giorgos Stamatelatos and Sotirios Gyftopoulos and George Drosatos and Pavlos S. Efraimidis},
  keywords = {Social network analysis, Twitter followers, News media, Political affinity}
}