Automatic Detection of Small Groups of Persons, Influential Members, Relations and Hierarchy in Written Conversations Using Fuzzy Logic

TL;DR

This paper presents a fuzzy logic-based method to automatically detect small groups, influential members, and hierarchies in online conversations, demonstrated on data from the TV show The Wire with high accuracy.

Contribution

It introduces a fuzzy logic classifier for social structure detection in online groups, including hierarchy ranking, as an alternative to traditional statistical methods.

Findings

Achieved 90% accuracy in identifying influential members.

Effectively detected subgroups and hierarchies in conversation data.

Demonstrated the method's usefulness on real-world data.

Abstract

Nowadays a lot of data is collected in online forums. One of the key tasks is to determine the social structure of these online groups, for example the identification of subgroups within a larger group. We will approach the grouping of individual as a classification problem. The classifier will be based on fuzzy logic. The input to the classifier will be linguistic features and degree of relationships (among individuals). The output of the classifiers are the groupings of individuals. We also incorporate a method that ranks the members of the detected subgroup to identify the hierarchies in each subgroup. Data from the HBO television show The Wire is used to analyze the efficacy and usefulness of fuzzy logic based methods as alternative methods to classical statistical methods usually used for these problems. The proposed methodology could detect automatically the most influential members of each organization The Wire with 90% accuracy.