Mpox Narrative on Instagram: A Labeled Multilingual Dataset of Instagram Posts on Mpox for Sentiment, Hate Speech, and Anxiety Analysis

The world is currently experiencing an outbreak of mpox, which has been declared a Public Health Emergency of International Concern by WHO. During recent virus outbreaks, social media platforms have played a crucial role in keeping the global population informed and updated regarding various topics. As a result, in the last few years, researchers from different disciplines have focused on the development of social media datasets related to different virus outbreaks, as such datasets serve as a rich data resource for the investigation of a wide range of research questions. No prior work in this field 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 is available at https://dx.doi.org/10.21227/7fvc-y093 and 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 also performed. This process included classifying each post into (i) one of the fine-grain 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 for the training and testing of machine learning algorithms for sentiment, hate speech, and anxiety or stress detection, as well as for other applications. Second, this paper also 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% 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.