  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.
