Sentiment Analysis for Troll Detection on Weibo

TL;DR

This paper presents a novel approach using sentiment analysis and machine learning to detect trolls on Sina Weibo, including a real-time Chrome extension for practical application.

Contribution

It introduces a new sentiment analysis-based method for troll detection on Sina Weibo, combining Chinese language processing and machine learning, with a real-time detection tool.

Findings

Effective troll detection accuracy demonstrated

Sentiment analysis improves detection performance

Real-time detection enabled by Chrome extension

Abstract

The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter and Facebook. In China, the micro-blogging service provider, Sina Weibo, is the most popular such service. To influence public opinion, Weibo trolls -- the so called Water Army -- can be hired to post deceptive comments. In this paper, we focus on troll detection via sentiment analysis and other user activity data on the Sina Weibo platform. We implement techniques for Chinese sentence segmentation, word embedding, and sentiment score calculation. In recent years, troll detection and sentiment analysis have been studied, but we are not aware of previous research that considers troll detection based on sentiment analysis. We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies. Experimental results are generated and analyzed. A Chrome extension is presented that implements our proposed technique, which enables real-time troll detection when a user browses Sina Weibo.