Predicting Tweet Engagement with Graph Neural Networks
- URL: http://arxiv.org/abs/2305.10103v1
- Date: Wed, 17 May 2023 10:09:40 GMT
- Title: Predicting Tweet Engagement with Graph Neural Networks
- Authors: Marco Arazzi, Marco Cotogni, Antonino Nocera, Luca Virgili
- Abstract summary: We propose TweetGage, a Graph Neural Network solution to predict the user engagement based on a novel graph-based model.
To validate our proposal, we focus on the Twitter platform and perform a thorough experimental campaign.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Social Networks represent one of the most important online sources to share
content across a world-scale audience. In this context, predicting whether a
post will have any impact in terms of engagement is of crucial importance to
drive the profitable exploitation of these media. In the literature, several
studies address this issue by leveraging direct features of the posts,
typically related to the textual content and the user publishing it. In this
paper, we argue that the rise of engagement is also related to another key
component, which is the semantic connection among posts published by users in
social media. Hence, we propose TweetGage, a Graph Neural Network solution to
predict the user engagement based on a novel graph-based model that represents
the relationships among posts. To validate our proposal, we focus on the
Twitter platform and perform a thorough experimental campaign providing
evidence of its quality.
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