Estimating Community Feedback Effect on Topic Choice in Social Media with Predictive Modeling

TL;DR

This study investigates how community feedback influences social media users' choices of topics to post about, using an interpretable predictive model to account for confounding factors and identify susceptible users.

Contribution

It introduces a predictive modeling approach that accounts for user heterogeneity and external factors to measure feedback effects on topic choice.

Findings

33% of Reddit users are influenced by feedback

14% of Twitter users are influenced by feedback

Feedback can change topic continuation probability by up to 14%

Abstract

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback -- called community feedback -- to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author's decision to continue the topic of their previous post. We explore the confounding factors, including author's topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33\% and 14\% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14\%, depending on the user and the amount of feedback.