Abstract
Today, as social networks play an increasingly important role, people are more likely to use them to discuss hot topics. Thus, reposting behavior plays a crucial role in such networks for information diffusion. However, the existing models do not consider the impact of some important numerical features on the spread of tweets. In addition, the potential correlation of the user information in different groups and their tweets will also affect the effect of retweet prediction. Considering the above problems, in this article, we propose a novel deep multitask learning-based method, CH-Transformer, for retweet prediction. First, we extract numerical features to represent tweet information features and social features. Then, the numerical features are concatenated with textual features. After feature extraction, we obtain the feature embeddings and feed them into our model to achieve propagation prediction. Finally, we evaluate the proposed method using well-known evaluation measures. The experimental results demonstrate the effectiveness of our method.
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Acknowledgements
This work was supported by the Key Research and Development Program of Shaanxi (2021GY-014) and the Fundamental Research Funds for the Central Universities (JB210309, JB210312).
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Wang, J., Yang, Y. Tweet Retweet Prediction Based on Deep Multitask Learning. Neural Process Lett 54, 523–536 (2022). https://doi.org/10.1007/s11063-021-10642-3
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DOI: https://doi.org/10.1007/s11063-021-10642-3