构建首个多语言猴痘社交舆情数据集，支持情感与焦虑分析。
Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis
- 收集6万条跨语言猴痘帖文，涵盖52种语言。
- 发现超7成帖文显焦虑，近4.3%含仇恨内容。
- 适合公共卫生、社会媒体分析研究者使用。
当前全球正经历猴痘疫情爆发，世界卫生组织已将其列为国际突发公共卫生事件。然而，此前尚无针对社交媒体中猴痘话题的数据集研究。本文填补这一空白，首次构建了一个包含60,127条Instagram帖子的多语言数据集，时间跨度为2022年7月23日至2024年9月5日，覆盖52种语言。每条数据包含帖子ID、描述、发布时间、语言及经Google Translate API翻译的英文版本。在此基础上，对每条帖子进行了情感分类（恐惧、惊讶、喜悦、悲伤、愤怒、厌恶、中性）、仇恨言论检测与焦虑/压力识别。结果显示，情感分布为：恐惧27.95%、惊讶2.57%、喜悦8.69%、悲伤5.94%、愤怒2.69%、厌恶1.53%、中性50.64%；仇恨言论占比4.25%，未含仇恨的占95.75%；72.05%的帖子无焦虑表现，27.95%存在焦虑或压力迹象。该数据集已公开，供学术研究使用。
原文摘要 · Abstract (English)
The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. No prior work related to social media mining has focused on the development of a dataset of Instagram posts about the mpox outbreak. The work presented in this paper aims to address this research gap and makes two scientific contributions to this field. First, it presents a multilingual dataset of 60,127 Instagram posts about mpox, published between July 23, 2022, and September 5, 2024. The dataset, available at https://dx.doi.org/10.21227/7fvc-y093, contains Instagram posts about mpox in 52 languages. For each of these posts, the Post ID, Post Description, Date of publication, language, and translated version of the post (translation to English was performed using the Google Translate API) are presented as separate attributes in the dataset. After developing this dataset, sentiment analysis, hate speech detection, and anxiety or stress detection were performed. This process included classifying each post into (i) one of the sentiment classes, i.e., fear, surprise, joy, sadness, anger, disgust, or neutral, (ii) hate or not hate, and (iii) anxiety/stress detected or no anxiety/stress detected. These results are presented as separate attributes in the dataset. Second, this paper presents the results of performing sentiment analysis, hate speech analysis, and anxiety or stress analysis. The variation of the sentiment classes - fear, surprise, joy, sadness, anger, disgust, and neutral were observed to be 27.95%, 2.57%, 8.69%, 5.94%, 2.69%, 1.53%, and 50.64%, respectively. In terms of hate speech detection, 95.75% of the posts did not contain hate and the remaining 4.25% of the posts contained hate. Finally, 72.05% of the posts did not indicate any anxiety/stress, and the remaining 27.95% of the posts represented some form of anxiety/stress.
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