# Joint Inference of User Community and Interest Patterns in Social Interaction Networks

## TL;DR

This paper introduces probabilistic models to jointly infer user communities and interest patterns in social media interaction networks, demonstrated on Twitter data from Purdue University, revealing insights into community structure and interaction topics.

## Contribution

The paper presents novel models for simultaneously inferring social communities and user interests from social media data, capturing both population and individual interaction patterns.

## Key findings

- Interaction networks follow power-law degree distribution.
- Models successfully identified community structures and key interaction topics.
- Application demonstrated for targeted information dissemination.

## Abstract

Online social media have become an integral part of our social beings. Analyzing conversations in social media platforms can lead to complex probabilistic models to understand social interaction networks. In this paper, we present a modeling approach for characterizing social interaction networks by jointly inferring user communities and interests based on social media interactions. We present several pattern inference models: i) Interest pattern model (IPM) captures population level interaction topics, ii) User interest pattern model (UIPM) captures user specific interaction topics, and iii) Community interest pattern model (CIPM) captures both community structures and user interests. We test our methods on Twitter data collected from Purdue University community. From our model results, we observe the interaction topics and communities related to two big events within Purdue University community, namely Purdue Day of Giving and Senator Bernie Sanders' visit to Purdue University as part of Indiana Primary Election 2016. Constructing social interaction networks based on user interactions accounts for the similarity of users' interactions on various topics of interest and indicates their community belonging further beyond connectivity. We observed that the degree-distributions of such networks follow power-law that is indicative of the existence of fewer nodes in the network with higher levels of interactions, and many other nodes with less interactions. We also discuss the application of such networks as a useful tool to effectively disseminate specific information to the target audience towards planning any large-scale events and demonstrate how to single out specific nodes in a given community by running network algorithms.

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Source: https://tomesphere.com/paper/1704.01706