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

Abstract

Social media platforms, thanks to their inherent nature of quick and far-reachingdissemination of information, have gradually supplanted the conventional mediaand become the new loci of political communication. These platforms not onlyease and expedite communication among crowds, but also provide researchershuge and easily accessible information. This huge information pool, if it isprocessed with a systematic analysis, can be a fruitful data source for researchers.Systematic analysis of data from social media, however, poses various challengesfor political analysis. Significant advances in automated textual analysis havetried to address such challenges of social media data. This paper introduces onesuch novel technique to assist researchers doing textual analysis on Twitter. Morespecifically, we develop a clustering methodology based on Longest CommonSubsequence Similarity Metric, which automatically groups tweets with similarcontent. To illustrate the usefulness of this technique, we present some of ourfindings from a project we conducted on Turkish sentiments on Twitter towardsSyrian refugees.