The popularity of various social media platforms (e.g., Twitter, Facebook, Instagram, and Weibo) has led to the generation of millions of micro-blogs each day. Retweet (message forwarding function) is considered to be one of the most effective behavior for information propagation on social networks. The task of retweet behavior prediction has received much attention in recent years, such as modelling the followers’ preference to predict if a tweet from others would be retweeted or not. But one important aspect in retweet behavior prediction is still being overlooked: the followers’ retweet time prediction, which is helpful to understand the popularity of a tweet, the relationships between users, and the influence of users on their followers. However, due to the complex entanglement of multimodal features in social media such as text, social relationships, users’ active time and many others, it is nontrivial to effectively predict the retweet time of followers. In this work, in order to predict the followers’ retweet time on Twitter, we present an end-to-end deep learning model, namely DFMF (Deep Fusion of Multimodal Features), to implicitly learn the latent features and interactions of tweets, social relationships, and the posting time. Specifically, we adopt a word embedding layer to learn the high-level semantics of tweets and a node embedding layer to learn the hidden representations of the complex social relationships. Then, together with the one-hot representation of a tweet’s posting time, the multimodal information is concatenated and fed into fully-connected forward neural networks for implicit cross-modality feature fusion, which is used to predict the retweet time. Finally, we evaluate the proposed method with a real-world Twitter dataset, the experimental results demonstrate that our proposed DFMF is more accurate in predicting the retweet time and can achieve as much as 11.25% performance improvement on the recall accuracy compared to Logistic Regression (LR) and Support Vector Machine (SVM).

各种社交媒体平台（例如Twitter，Facebook，Instagram和微博）的普及导致每天产生数百万个微博客。转推（消息转发功能）被认为是在社交网络上传播信息的最有效行为之一。转推行为预测的任务近年来受到了广泛的关注，例如对追随者的偏好进行建模，以预测其他人的推文是否将被转发。但是转推行为预测中的一个重要方面仍然被忽略：关注者的转推时间预测，这有助于了解推文的受欢迎程度，用户之间的关系以及用户对其关注者的影响。但是，由于社交媒体（例如文本）中的多模式功能复杂地纠缠在一起，社交关系，用户的活跃时间以及许多其他因素，有效地预测关注者的转发时间是很重要的。在这项工作中，为了预测关注者在Twitter上的转发时间，我们提出了一种端到端的深度学习模型，即DFMF（多模式特征的深度融合），以隐式地学习推文，社交和社交网络的潜在特征和交互。关系和发布时间。具体来说，我们采用词嵌入层来学习推文的高级语义，并采用节点嵌入层来学习复杂的社会关系的隐藏表示。然后，与推文发布时间的一站式表示形式相结合，将多模式信息串联起来，并馈入全连接的前向神经网络中，以进行隐式跨模态特征融合，从而预测转发时间。