Next Topic Recommendation for Influencers on Social Media

To maintain popularity on social media over the long term, users need to shift to a new topic instead of sticking to one topic. When selecting a new topic, a user needs to consider both its popularity on the entire social media and its popularity among the current followers. The former affects the expected number of new followers, and the latter affects the expected ratio of the current followers the user can retain after the topic change. The timing is also important. The user should change to a new topic before the current topic becomes less popular and the user loses many of the current followers. If the user change the topic after losing the followers, it is more difficult to obtain new followers. In this paper, we introduce a new task based on these observations: recommending appropriate new topics for currently popular social media users at appropriate timing. As an example of opportunities in the research on this task, we also propose a simple method of predicting the popularity a given user would gain after shifting to a given new topic. Our method predicts it based on the similarity between the user’s current topic and the given new topic. In our experiment with data collected from X (formerly Twitter), our method improves the prediction accuracy compared with a baseline method.

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Publication Details

Published
2024-12-15
DOI
https://doi.org/10.1109/bigdata62323.2024.10825496
Primary Topic
Complex Network Analysis Techniques
Type
article
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article

Next Topic Recommendation for Influencers on Social Media

Masanobu Iwanaga, Yoko Yamakata, Keishi Tajima
Complex Network Analysis Techniques
article

Next Topic Recommendation for Influencers on Social Media

Masanobu Iwanaga, Yoko Yamakata, Keishi Tajima
article en

Abstract

To maintain popularity on social media over the long term, users need to shift to a new topic instead of sticking to one topic. When selecting a new topic, a user needs to consider both its popularity on the entire social media and its popularity among the current followers. The former affects the expected number of new followers, and the latter affects the expected ratio of the current followers the user can retain after the topic change. The timing is also important. The user should change to a new topic before the current topic becomes less popular and the user loses many of the current followers. If the user change the topic after losing the followers, it is more difficult to obtain new followers. In this paper, we introduce a new task based on these observations: recommending appropriate new topics for currently popular social media users at appropriate timing. As an example of opportunities in the research on this task, we also propose a simple method of predicting the popularity a given user would gain after shifting to a given new topic. Our method predicts it based on the similarity between the user’s current topic and the given new topic. In our experiment with data collected from X (formerly Twitter), our method improves the prediction accuracy compared with a baseline method.

Kyoto University (JP), The University of Tokyo (JP)
Openalex Percentile: Top 76%
Complex Network Analysis Techniques
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