EPJ Data Sci. (2020) 9: 25
https://doi.org/10.1140/epjds/s13688-020-00243-w

Regular article

Estimating community feedback effect on topic choice in social media with predictive modeling

¹ Spoken Language Systems (LSV) Group, Saarland Informatics Campus, Saarland University, Saarbruc̈ken, Germany
² Graduate School of Frontier Sciences, The University of Tokyo, Chiba, Japan
³ Mathematics and Informatics Center, The University of Tokyo, Tokyo, Japan
⁴ National Institute of Informatics, Tokyo, Japan
⁵ JST, PRESTO, Saitama, Japan
⁶ Qatar Computing Research Institute, HBKU, Doha, Qatar
⁷ University of Massachusetts, Massachusetts, USA

^* e-mail: grabowicz@cs.umass.edu

Received: 7 December 2019
Accepted: 3 August 2020
Published online: 31 August 2020

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.