Application of neural networks to identify trolls in social networks

TL;DR

This paper presents a neural network-based algorithm using Kohonen's self-organized maps to automatically detect trolls in social networks by grouping users with similar communication behaviors.

Contribution

It introduces a novel method combining user behavior features with neural networks for automated troll detection in social media.

Findings

Effective grouping of users based on communication patterns.

Successful identification of trolls using the proposed neural network approach.

Potential for real-time troll detection in social networks.

Abstract

In this paper we developed and tested a new algorithm of detecting in social networks users (so-called trolls) who behave in an insulting and provocative way towards other users. In order to detect trolls it is proposed to unite users in groups where all the members have a similar way of communicating. Defining the number of group and distributing the users into these groups is carried out automatically due to application of neural networks of special type - Kohonens self-organized maps. As for users characteristics according to which the distribution into groups might be done we suggest using such data as the number of comments, the average comment length and indicators determining the emotional state of the user (the frequency of encountering certain characters in comments).