Automated Text Analysis and International Relations: The Introduction and Application of a Novel Technique for Twitter

Year 2019, , 183 - 204, 01.07.2019

Emre Hatipoğlu , Osman Zeki Gökçe İnanç Arın Yücel Saygın

https://doi.org/10.20991/allazimuth.476852

Cited By: 3

Abstract

Social
media platforms, thanks to their inherent nature of quick and far-reaching
dissemination of information, have gradually supplanted the conventional media and
become the new loci of political communication. These platforms not only ease
and expedite communication among crowds, but also provide researchers huge and easily
accessible information. This huge information pool, if it is processed with a
systematic analysis, can be a fruitful data source for researchers. Systematic
analysis of data from social media, however, poses various challenges for
political analysis. Significant advances in automated textual analysis have
tried to address such challenges of social media data. This paper introduces
one such novel technique to assist researchers doing textual analysis on
Twitter. The technique develops a measure, the Longest Common Subsequence
Similarity Metric (LCSSM), which automatically clusters tweets with content. To
illustrate the usefulness of this technique, we present some of our findings
from a project we conducted on Turkish sentiments on Twitter towards Syrian
refugees.

Keywords

Social media, foreign policy, refugee, public opinion, Turkey

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