Studying Positive Speech on Twitter
This work addresses the challenge of measuring constructive communication on social media for communities in conflict, but it is incremental as it applies existing methods to new data without major breakthroughs.
The researchers tackled the problem of identifying positive speech on Twitter in conflict-prone regions, finding that it accounted for less than 1% of the data through semi-manual analysis and testing automated methods like unsupervised statistical analysis and supervised text classification.
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.