Finding influential users of an online health community: a new metric based on sentiment influence

TL;DR

This paper introduces a new sentiment influence metric to identify influential users in online health communities by analyzing their ability to affect others' sentiments, improving community management and understanding of social influence.

Contribution

The paper proposes a novel sentiment-based influence metric derived from text mining and sentiment analysis, enhancing the identification of influential users in OHCs.

Findings

The new metric effectively identifies influential users.

Combining the metric with traditional measures improves accuracy.

Sentiment influence correlates with user impact in OHCs.

Abstract

What characterizes influential users in online health communities (OHCs)? We hypothesize that (1) the emotional support received by OHC members can be assessed from their sentiment ex-pressed in online interactions, and (2) such assessments can help to identify influential OHC members. Through text mining and sentiment analysis of users' online interactions, we propose a novel metric that directly measures a user's ability to affect the sentiment of others. Using dataset from an OHC, we demonstrate that this metric is highly effective in identifying influential users. In addition, combining the metric with other traditional measures further improves the identification of influential users. This study can facilitate online community management and advance our understanding of social influence in OHCs.