Detecting Mis/Dis-information from Social Media with Semantic Enhancement

被引：0
|
作者：
Hao W. [1 ,2 ]
Lijuan G. [1 ,2 ]
Zeyu Z. [1 ,2 ]
Tao F. [1 ,2 ]
Yongsheng W. [1 ,2 ]
机构：
[1] School of Information Management, Nanjing University, Nanjing
[2] Jiangsu Key Laboratory of Data Engineering and Knowledge Service, Nanjing
来源：
Data Analysis and Knowledge Discovery | 2023年 / 7卷 / 02期
基金：
中国国家自然科学基金;
关键词：
False Information; Multi-Modal; Semantic Enhancement; Sentiment Analysis; Sina Weibo;
D O I：
10.11925/infotech.2096-3467.2022.0923
中图分类号：
学科分类号：
摘要：
[Objective] This paper builds an automated detection model to effectively identify mis/dis-information from social media, aiming to balance the speed and accuracy of processing massive data. [Methods] The classification model is the mainstream processing technique to detect for mis/dis-information. However, most of them could not extract deep semantic features from the texts. Therefore, we used the single text feature BFID model (BERT False-Information-Detection) as the benchmark model, and proposed two new methods with fused semantic enhancement to detect the mis/dis-information. [Results] We examined the new models with data from Sina Weibo. The accuracy of the model based on fused sentiment feature BFID-SEN (BFID-Sentiment) increased about 1.59 percentage point, while the accuracy of model with fused image feature BFID-IMG (BFID-Image) model improved by 0.78 percentage point. [Limitations] The ability to fuse semantic enhancement is limited due to the small corpus size, sentiment categories and multimodal disinformation training datasets. [Conclusions] The proposed methods are able to more effectively identify false information from social media. © 2023, Chinese Academy of Sciences. All rights reserved.
引用
收藏
页码：48 / 60
页数：12
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