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

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submitted on 2024-06-27, 11:29 and posted on 2024-06-27, 11:41 authored by David Ifeoluwa Adelani, Ryota Kobayashi, Ingmar Weber, Przemyslaw A. Grabowicz

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.

Other Information

Published in: EPJ Data Science
License: https://creativecommons.org/licenses/by/4.0
See article on publisher's website: https://dx.doi.org/10.1140/epjds/s13688-020-00243-w

Funding

JSPS KAKENHI (JP17H03279).

JSPS KAKENHI (JP18K11560).

JSPS KAKENHI (JP19H01133).

JST ACT- I (JPMJPR16UC).

JST PRESTO (JPMJPR1925).

Volkswagen Foundation ( 92136).

JP17H03279

History

Language

  • English

Publisher

Springer Nature

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License.

Institution affiliated with

  • Hamad Bin Khalifa University
  • Qatar Computing Research Institute - HBKU

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