@inproceedings{57bf70de35074784a4ade4f60b850db1,
title = "Measuring polarization in Twitter enabled in online political conversation: The case of 2016 US Presidential election",
abstract = "Political Polarization is the divergence of attitudes toward ideological extremes and it is more likely to happen in groups of like-minded individuals. We measured polarization on Twitter during the 2016 United States Presidential elections, analyzing Twitter back- channeling conversations occurring during the three Presidential debates. The polarization metric we use is based on an existing metaphor of the electric dipole. In each debate the polarization dynamic followed a U- shaped pattern, with polarization being high at the beginning of the debate, decreasing over time, and finally bouncing back to a value that was higher than the initial one. The temporary decline of polarization is due to the increase in interaction with participants holding opposite opinions, but apparently this interaction is more conducive to confrontation than to revision of existing beliefs. We argue that the characteristics of Twitter are generally conducive to polarized and manipulable online debate.",
keywords = "Conversational analysis, E-politics, Polarization, Social media, Twitter",
author = "S. Primario and D. Borrelli and G. Zollo and L. Iandoli and C. Lipizzi",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 18th IEEE International Conference on Information Reuse and Integration, IRI 2017 ; Conference date: 04-08-2017 Through 06-08-2017",
year = "2017",
month = nov,
day = "8",
doi = "10.1109/IRI.2017.73",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017",
pages = "607--613",
editor = "Latifur Khan and Balaji Palanisamy and Chengcui Zhang and Sarvestani, {Sahra Sedigh}",
booktitle = "Proceedings - 2017 IEEE International Conference on Information Reuse and Integration, IRI 2017",
}