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Neural Information Diffusion Prediction with Topic-Aware Attention Network

Published: 30 October 2021 Publication History

Abstract

Information diffusion prediction targets on forecasting how information items spread among a set of users. Recently, neural networks have been widely used in modeling information diffusion, owing to the great successes of deep learning. However, in real-world information diffusion scenarios, users are likely to have different behaviors to information items from different topics. Existing neural-based methods failed to model the topic-specific diffusion patterns and dependencies, which have been shown to be useful in conventional non-neural methods. In this paper, we propose Topic-aware Attention Network (TAN) to take advantage of both topic-specific diffusion modeling and deep learning techniques. We jointly model the text content of information items and cascade sequences by incorporating topical context and user/position dependencies into user representations via attention mechanisms. A time-decayed aggregation module is further employed to integrate user representations for cascade representations, which can encode the topic-specific diffusion dependencies independently. Experimental results on diffusion prediction tasks over three realistic cascade datasets show that our model can achieve a relative improvement up to 9% against the best performing baseline in terms of Hits@10.

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cover image ACM Conferences
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management
October 2021
4966 pages
ISBN:9781450384469
DOI:10.1145/3459637
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Published: 30 October 2021

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Author Tags

  1. cascade modeling
  2. information diffusion
  3. topic-aware modeling

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  • (2024)A Survey of Deep Learning-Based Information Cascade PredictionSymmetry10.3390/sym1611143616:11(1436)Online publication date: 29-Oct-2024
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