Trend and Thoughts: Understanding Climate Change Concern using Machine Learning and Social Media Data
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Abstract (arXiv)
Nowadays social media platforms such as Twitter provide a great opportunity to understand public opinion of climate change compared to traditional survey methods. In this paper, we constructed a massive climate change Twitter dataset and conducted comprehensive analysis using machine learning. By conducting topic modeling and natural language processing, we show the relationship between the number of tweets about climate change and major climate events; the common topics people discuss climate change; and the trend of sentiment. Our dataset was published on Kaggle (urlhttps://www.kaggle.com/leonshangguan/climate-change-tweets-ids-until-aug-2021) and can be used in further research.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 6 Nov 2021.
Builds on
6 of 15 references.
References · 6
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
- 2019Tackling Climate Change with Machine Learningcited by 264
- 2019SemEval-2017 Task 4: Sentiment Analysis in Twittercited by 209
- 2020Stance Detection on Social Media: State of the Art and Trendscited by 93
- 2020Narratives and Needs: Analyzing Experiences of Cyclone Amphan Using Twitter Discoursecited by 2
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Citing papers per year: 2024: 4.
4 papers.
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