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

arXiv: https://arxiv.org/abs/2409.05292 · HTML: https://mlsanity.com/paper/2409.05292/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 104 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 13 of 13)

- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [#Election2020: The First Public Twitter Dataset on the 2020 US Presidential Election](https://mlsanity.com/paper/2010.00600/) (2020)
- [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](https://mlsanity.com/paper/2010.08768/) (2020)
- [Let-Mi: An Arabic Levantine Twitter Dataset for Misogynistic Language](https://mlsanity.com/paper/2103.10195/) (2021)
- [#RoeOverturned: Twitter Dataset on the Abortion Rights Controversy](https://mlsanity.com/paper/2302.01439/) (2023)
- [A Stance Data Set on Polarized Conversations on Twitter about the Efficacy of Hydroxychloroquine as a Treatment for COVID-19](https://mlsanity.com/paper/2009.01188/) (2020)
- [Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications](https://mlsanity.com/paper/2206.14358/) (2022)
- [A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave](https://mlsanity.com/paper/2208.07810/) (2022)
- [Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets](https://mlsanity.com/paper/1904.05308/) (2019)
- [TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter-related Microblogs on Twitter](https://mlsanity.com/paper/2108.12521/) (2021)
- [Natural Hazards Twitter Dataset](https://mlsanity.com/paper/2004.14456/) (2020)
- [Understanding Public Opinion on Using Hydroxychloroquine for COVID-19 Treatment via Social Media](https://mlsanity.com/paper/2201.00237/) (2022)
- [A Labelled Dataset for Sentiment Analysis of Videos on YouTube, TikTok, and Other Sources about the 2024 Outbreak of Measles](https://mlsanity.com/paper/2406.07693/) (2024)

## Abstract (arXiv)

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.