基于转移学习的跨领域社会谣言传播模型
概括
本研究介绍了一个跨领域的谣言传播模型,使用转移学习来解决稀缺数据. 该模型利用表示学习和情感分析来准确地预测各种主题的谣言.
科学领域:
- 计算机科学 计算机科学
- 社交网络分析 社交网络分析
- 计算社会科学 计算社会科学
背景情况:
- 谣言在各个领域都表现出各种各样的文本特征,但在情感上有共同之处.
- 在特定的谣言主题领域的稀缺数据阻碍了准确的传播预测.
- 了解用户情绪和网络结构对于谣言分析至关重要.
研究的目的:
- 通过转移学习提出一种新的跨领域谣言传播模型.
- 为了应对谣言主题领域数据稀缺的挑战.
- 为了提高传闻传播预测在不同领域的准确性.
主要方法:
- 开发了User-Retweet-Rumor2vec (URR2vec) 用于潜在的特征提取和表示学习.
- 采用深度学习模型进行文本情感分析,以捕捉用户的情感认知.
- 为预测提出了一个文本-情感分析-图形卷积网络 (TSA-GCN),通过域调整进行了增强.
主要成果:
- URR2vec有效地代表了用户-转发-谣言关系和主题信息.
- 情感分析模型识别了谣言传播期间用户之间的情感相关性.
- 具有域调整的TSA-GCN模型实现了准确的跨域谣言传播预测.
结论:
- 提议的转移学习模型有效地克服了谣言传播中的数据稀缺问题.
- 域调整技术通过迁移参数和图形结构来提高预测准确性.
- 这种方法为分析和预测各种主题的谣言动态提供了强大的解决方案.
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