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.

翻译：社交网络是全球范围内分享内容最重要的在线渠道之一。在此背景下，预测帖子是否能产生互动影响力，对于驱动这些媒体的盈利性开发至关重要。现有文献中，多项研究通过利用帖子的直接特征（通常与文本内容和发布用户相关）来解决该问题。本文认为，互动量的提升还与另一关键要素相关，即用户在社交媒体上发布帖子间的语义关联。因此，我们提出TweetGage——一种基于新型图模型的图神经网络解决方案，该图模型可表征帖子间的关系，从而实现用户互动量的预测。为验证所提方案，我们以Twitter平台为研究对象，开展了全面的实验评估，实验结果充分证明了该方法的有效性。