计算机科学 ›› 2022, Vol. 49 ›› Issue (8): 330-335.doi: 10.11896/jsjkx.210600046
姜梦函, 李邵梅, 郑洪浩, 张建朋
JIANG Meng-han, LI Shao-mei, ZHENG Hong-hao, ZHANG Jian-peng
摘要： 随着在线社交网络的兴起,人们传播和获取信息的方式发生了翻天覆地的变化。社交媒体在方便人们生活的同时,也加速了谣言的产生和传播。因此,如何准确高效地检测谣言成为了亟待解决的问题。为了提高谣言检测的精度,对基于全局-局部注意网络的谣言检测模型进行了改进,考虑到文本中词与词之间的位置关系对谣言检测的影响,引入了一种新的相对位置编码方法来改进原有模型的局部特征提取模块。该方法能够更准确地提取谣言中文本的语义信息和位置信息并将它们聚合,得到更优的区分谣言与非谣言的文本特征,将该特征和描述转发行为的全局特征相结合,进而提升对谣言的检测效果。实验结果表明,与其他主流检测方法相比,所提方法在微博数据集上的F1值可达95.0％,具有更好的检测效果。
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