Summary of Five Years Of Covid-19 Discourse on Instagram: a Labeled Instagram Dataset Of Over Half a Million Posts For Multilingual Sentiment Analysis, by Nirmalya Thakur
Five Years of COVID-19 Discourse on Instagram: A Labeled Instagram Dataset of Over Half a Million Posts for Multilingual Sentiment Analysis
by Nirmalya Thakur
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning educator may summarize this paper as follows: This research presents a multilingual dataset of COVID-19-related Instagram posts, comprising 500,153 posts in 161 languages and 535,021 hashtags. The dataset includes sentiments classified as positive, negative, or neutral, which are analyzed per year from 2020 to 2024. The findings reveal trends in sentiment shifts over time, with a decline in positive sentiment and an increase in neutral sentiment. Additionally, the paper presents language-specific sentiment analysis, highlighting differences between languages such as English and Hindi. This study contributes to understanding COVID-19-related social media discourse, shedding light on the evolution of public opinion during the pandemic. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at Instagram posts about COVID-19 from 2020 to 2024. Researchers made a big dataset with lots of posts (500,000+!) and classified each one as happy, sad, or neutral. They found that people’s feelings about COVID-19 changed over time – more people were unhappy than happy by the end! They also looked at how different languages (like English and Hindi) affected what people said. |
Keywords
» Artificial intelligence » Discourse » Machine learning
