Multimodal fake news dataset Weibo23

We propose a more challenging dataset known as Weibo23. By amalgamating all available fake news from the Weibo Management Community until March 2023 with existing samples from public datasets [1], we formed a comprehensive collection of fake news for Weibo23. Fabricated news articles were thoroughly examined and authenticated by certified experts. To facilitate the accurate differentiation between fake and real news, minimizing content-related disparities between them is imperative. Otherwise, the model tends to rely on specific content cues to identify fake news excessively. Therefore, for each instance of fake news, we employed the Baidu API to extract keywords and retrieved relevant news on Weibo based on the publication date and keywords. Subsequently, through manual scrutiny, all collected pertinent news was categorized into fake news, real news, and others (tweets about personal life, emotions, entertainment, etc.). The HANLP API was utilized to compute the similarity between samples within the same class and eliminate duplicates based on their similarity scores. Furthermore, samples collected before December 31, 2021 were partitioned into training and validation sets, while those obtained from January 1, 2022 onwards constituted the testing set.

[1] Q. Nan, J. Cao, Y. Zhu, Y. Wang, and J. Li, ‘‘Mdfend: Multi-domain fake news detection,’’ in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, ser. CIKM ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 3343–3347. [Online]. Available: https://doi.org/10.1145/3459637.3482139

本研究提出了一个更具挑战性的数据集——Weibo23。我们将截至2023年3月的微博管理社区（Weibo Management Community）公开的全部虚假新闻样本，与公开数据集[1]中的现有样本进行融合，构建了Weibo23数据集的完整虚假新闻集合。所有编造的新闻文本均经过持证专家的严格审核与真伪验证。

为实现虚假新闻与真实新闻的精准区分，最小化两类样本间的内容差异至关重要；否则模型极易过度依赖特定内容线索完成虚假新闻判别。因此，针对每一条虚假新闻样本，我们借助百度API（Baidu API）提取关键词，并基于发布时间与关键词在微博平台检索相关新闻。随后通过人工审核，将所有收集到的相关新闻划分为三类：虚假新闻、真实新闻及其他（如涉及个人生活、情感、娱乐等话题的推文）。我们使用HANLP API计算同类样本间的相似度，并基于相似度得分去除重复样本。此外，我们将2021年12月31日前收集的样本划分为训练集与验证集，2022年1月1日及之后收集的样本则作为测试集。

[1] Q. Nan、J. Cao、Y. Zhu、Y. Wang与J. Li，《Mdfend：多领域虚假新闻检测》，收录于第30届ACM信息与知识管理国际会议论文集（CIKM ’21），美国纽约：ACM协会，2021年，第3343-3347页。[在线]. 可访问：https://doi.org/10.1145/3459637.3482139

搜集汇总

背景与挑战

背景概述

该数据集是一个名为Weibo23的多模态假新闻数据集，专注于微博平台。它整合了截至2023年3月的微博管理社区的假新闻以及现有公开数据集的样本，形成了一个全面的假新闻集合，且所有伪造新闻都经过认证专家的审查验证。数据格式包括图像（.jpg）、文本（.json）和标签（.csv）文件，支持多模态分析，但目前数据文件尚未上传至平台。