# Studying Positive Speech on Twitter

## TL;DR

This paper empirically investigates positive speech on Twitter, revealing it constitutes less than 1% of data and evaluating automated methods for its detection in a conflict context.

## Contribution

It introduces automated approaches for detecting positive speech on Twitter and compares their effectiveness in a conflict-prone environment.

## Key findings

- Positive speech is less than 1% of Twitter data.
- Unsupervised and supervised methods have distinct benefits and challenges.
- Empirical evidence supports the feasibility of automated detection.

## Abstract

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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Source: https://tomesphere.com/paper/1702.08866