Community Detection and Impact of Bots on Sentiment Polarity of Twitter Networks

Abstract

The globe has been dominated today by Online Social Networks (OSN) like Facebook, Instagram, and Twitter. However, the rise of popularity and attractiveness of these web based services has also led to the rise of malicious accounts known as bots. Not only do they cause Internet traffic, but also a group of these accounts working together can spread disinformation, incorrect, or manipulated news. The detection of bots, as well as their interactions with their communities and the rest of the world, is essential. This paper focuses on plotting a network of Twitter users, based on a particular hashtag, and detecting the communities in it, followed by detecting and locating the bots in these communities. In addition to this, sentiment analysis is conducted on normal users’ tweets as well as the bots in these communities. This paper also aims to identify the overall sentiment of the communities, and thus provide promising conclusions relating to bot behavior in the overall network.