A Multimodal Framework for Topic Propagation Classification in Social Networks
Journal:
arXiv
Published Date:
Mar 5, 2025
Abstract
The rapid proliferation of the Internet and the widespread adoption of social
networks have significantly accelerated information dissemination. However,
this transformation has introduced complexities in information capture and
processing, posing substantial challenges for researchers and practitioners.
Predicting the dissemination of topic-related information within social
networks has thus become a critical research focus. This paper proposes a
predictive model for topic dissemination in social networks by integrating
multidimensional features derived from key dissemination characteristics.
Specifically, we introduce two novel indicators, user relationship breadth and
user authority, into the PageRank algorithm to quantify user influence more
effectively. Additionally, we employ a Text-CNN model for sentiment
classification, extracting sentiment features from textual content. Temporal
embeddings of nodes are encoded using a Bi-LSTM model to capture temporal
dynamics. Furthermore, we refine the measurement of user interaction traces
with topics, replacing traditional topic view metrics with a more precise
communication characteristics measure. Finally, we integrate the extracted
multidimensional features using a Transformer model, significantly enhancing
predictive performance. Experimental results demonstrate that our proposed
model outperforms traditional machine learning and unimodal deep learning
models in terms of FI-Score, AUC, and Recall, validating its effectiveness in
predicting topic propagation within social networks.
