@inproceedings{4f861110c6f3460db17220a75a435b57,
title = "ClimateMiSt: Climate Change Misinformation and Stance Detection Dataset",
abstract = "Climate change has been a worldwide concern for more than 50 years, and climate change misinformation has also become a critical issue as it questions the causes and effects of climate change, thereby disrupting climate action. Climate misinformation has been a major obstacle to mitigating climate change and its effects, aggravating the issue and polarizing the public. In this paper, we introduce ClimateMiSt, a new climate change misinformation and stance detection dataset consisting of social media data with manually verified labels. The data is collected from Twitter/X and our dataset contains 146,670 tweets. We implement state-of-the-art baseline models for both misinformation and stance detection on our dataset and discover that GPT-4 outperforms them in both tasks. To the best of our knowledge, ClimateMiSt is the first dataset focused on climate change that includes both veracity and stance annotations collected from a social media platform. Our novel dataset can be used for climate change misinformation and stance detection, and it can further contribute to research in this field.",
keywords = "Climage Change, Climate Change Dataset, Misinformation Detection, Online Social Media, Stance Detection",
author = "Choi, {Yeon Jung} and Lanyu Shang and Dong Wang",
note = "This research is supported in part by the National Science Foundation under Grant No. IIS-2202481, CHE-2105032, IIS-2130263, CNS-2131622, CNS-2140999. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation here on.; 16th International Conference on Social Networks Analysis and Mining, ASONAM 2024 ; Conference date: 02-09-2024 Through 05-09-2024",
year = "2025",
doi = "10.1007/978-3-031-78538-2_28",
language = "English (US)",
isbn = "9783031785375",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "321--330",
editor = "Aiello, {Luca Maria} and Tanmoy Chakraborty and Sabrina Gaito",
booktitle = "Social Networks Analysis and Mining - 16th International Conference, ASONAM 2024, Proceedings",
address = "Germany",
}