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.

本文提出了一个基于图神经网络的解决方案，TweetGage，用于预测社交媒体上发布的帖子在用户参与度方面的影响，并通过一系列实验对其效果进行了验证。

使用图神经网络预测推文互动

Predicting Tweet Engagement with Graph Neural Networks

Reasoning about the relationships between object pairs in images is a crucial
task for holistic scene understanding. Most of the existing works treat this
task as a pure visual classification task: each type of relationship or phrase
is classified as a relation category based on the extracted visual features.
However, each kind of relationships has a wide variety of object combination
and each pair of objects has diverse interactions. Obtaining sufficient
training samples for all possible relationship categories is difficult and
expensive. In this work, we propose a natural language guided framework to
tackle this problem. We propose to use a generic bi-directional recurrent
neural network to predict the semantic connection between the participating
objects in the relationship from the aspect of natural language. The proposed
simple method achieves the state-of-the-art on the Visual Relationship
Detection (VRD) and Visual Genome datasets, especially when predicting unseen
relationships (e.g. recall improved from 76.42% to 89.79% on VRD zero-shot
testing set).

本研究提出了一种自然语言引导的框架来解决视觉关系检测任务中对象对之间关系分类的问题，并使用双向循环神经网络以及语义连接来预测参与关系的对象之间的语义连接。该方法在 Visual Relationship Detection 和 Visual Genome 数据集上取得了最佳状态，特别是在未预测关系的情况下。