User Interest and Interaction Structure in Online Forums

TL;DR

This paper introduces a novel similarity measure for online forum posts that incorporates content, thread context, and authorship, enabling improved visualization and analysis of user interactions and network structure.

Contribution

It presents a new post similarity measure that considers multiple information sources and applies principal coordinate analysis for user similarity and visualization.

Findings

Including author information smooths principal coordinate projections.

Our method provides a more detailed view of local and global network structures.

Demonstrated on real corporate forum data, outperforming standard classification methods.

Abstract

We present a new similarity measure tailored to posts in an online forum. Our measure takes into account all the available information about user interest and interaction --- the content of posts, the threads in the forum, and the author of the posts. We use this post similarity to build a similarity between users, based on principal coordinate analysis. This allows easy visualization of the user activity as well. Similarity between users has numerous applications, such as clustering or classification. We show that including the author of a post in the post similarity has a smoothing effect on principal coordinate projections. We demonstrate our method on real data drawn from an internal corporate forum, and compare our results to those given by a standard document classification method. We conclude our method gives a more detailed picture of both the local and global network structure.