# Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis

arXiv: https://arxiv.org/abs/2410.03293 · HTML: https://mlsanity.com/paper/2410.03293/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 99 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 14 of 14)

- [PDEBENCH: An Extensive Benchmark for Scientific Machine Learning](https://mlsanity.com/paper/2210.07182/) (2022)
- [The COVID-19 Social Media Infodemic](https://mlsanity.com/paper/2003.05004/) (2020)
- [GeoCoV19: A Dataset of Hundreds of Millions of Multilingual COVID-19 Tweets with Location Information](https://mlsanity.com/paper/2005.11177/) (2020)
- [ArCOV19-Rumors: Arabic COVID-19 Twitter Dataset for Misinformation Detection](https://mlsanity.com/paper/2010.08768/) (2020)
- [TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels](https://mlsanity.com/paper/2110.03664/) (2021)
- [Large Arabic Twitter Dataset on COVID-19](https://mlsanity.com/paper/2004.04315/) (2020)
- [A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave](https://mlsanity.com/paper/2208.07810/) (2022)
- [ArCOV-19: The First Arabic COVID-19 Twitter Dataset with Propagation Networks](https://mlsanity.com/paper/2004.05861/) (2020)
- [An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection](https://mlsanity.com/paper/2208.10252/) (2022)
- [A Multilingual Dataset of COVID-19 Vaccination Attitudes on Twitter](https://mlsanity.com/paper/2206.14619/) (2022)
- [Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset](https://mlsanity.com/paper/2003.10359/) (2020)
- [A First Instagram Dataset on COVID-19](https://mlsanity.com/paper/2004.12226/) (2020)
- [Italian Twitter semantic network during the Covid-19 epidemic](https://mlsanity.com/paper/2106.05815/) (2021)
- [IRLCov19: A Large COVID-19 Multilingual Twitter Dataset of Indian Regional Languages](https://mlsanity.com/paper/2107.12360/) (2021)

## Built on by (most cited first, 1 of 1)

- [COVID-19 on YouTube: A Data-Driven Analysis of Sentiment, Toxicity, and Content Recommendations](https://mlsanity.com/paper/2412.17180/) (2024)

## Abstract (arXiv)

The work presented in this paper makes three scientific contributions with a specific focus on mining and analysis of COVID-19-related posts on Instagram. First, it presents a multilingual dataset of 500,153 Instagram posts about COVID-19 published between January 2020 and September 2024. This dataset, available at https://dx.doi.org/10.21227/d46p-v480, contains Instagram posts in 161 different languages as well as 535,021 distinct hashtags. After the development of this dataset, multilingual sentiment analysis was performed, which involved classifying each post as positive, negative, or neutral. The results of sentiment analysis are presented as a separate attribute in this dataset. Second, it presents the results of performing sentiment analysis per year from 2020 to 2024. The findings revealed the trends in sentiment related to COVID-19 on Instagram since the beginning of the pandemic. For instance, between 2020 and 2024, the sentiment trends show a notable shift, with positive sentiment decreasing from 38.35% to 28.69%, while neutral sentiment rising from 44.19% to 58.34%. Finally, the paper also presents findings of language-specific sentiment analysis. This analysis highlighted similar and contrasting trends of sentiment across posts published in different languages on Instagram. For instance, out of all English posts, 49.68% were positive, 14.84% were negative, and 35.48% were neutral. In contrast, among Hindi posts, 4.40% were positive, 57.04% were negative, and 38.56% were neutral, reflecting distinct differences in the sentiment distribution between these two languages.