Studying Positive Speech on Twitter

We present results of empirical studies on positive speech on Twitter. By positive speech we understand speech that works for the betterment of a given situation, in this case relations between different communities in a conflict-prone country. We worked with four Twitter data sets. Through semi-man...

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Published in:arXiv.org 2017-02
Main Authors: Sokolova, Marina, Sazonova, Vera, Huang, Kanyi, Chakraboty, Rudraneel, Matwin, Stan
Format: Article
Language:English
Subjects:
Data mining
Empirical analysis
Freedom of speech
Libel & slander
Social networks
Speech
Statistical analysis
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Sazonova, Vera
Huang, Kanyi
Chakraboty, Rudraneel
Matwin, Stan
description We present results of empirical studies on positive speech on Twitter. By positive speech we understand speech that works for the betterment of a given situation, in this case relations between different communities in a conflict-prone country. We worked with four Twitter data sets. Through semi-manual opinion mining, we found that positive speech accounted for < 1% of the data . In fully automated studies, we tested two approaches: unsupervised statistical analysis, and supervised text classification based on distributed word representation. We discuss benefits and challenges of those approaches and report empirical evidence obtained in the study.
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subjects Data mining
Empirical analysis
Freedom of speech
Libel & slander
Social networks
Speech
Statistical analysis
title Studying Positive Speech on Twitter
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