ClimateStance/README.md

### Citation Information

```
@inproceedings{vaid-etal-2022-towards,
                    title = "Towards Fine-grained Classification of Climate Change related Social Media Text",
                    author = "Vaid, Roopal  and
                    Pant, Kartikey  and
                    Shrivastava, Manish",
                    booktitle = "Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop",
                    month = may,
                    year = "2022",
                    address = "Dublin, Ireland",
                    publisher = "Association for Computational Linguistics",
                    url = "https://aclanthology.org/2022.acl-srw.35",
                    doi = "10.18653/v1/2022.acl-srw.35",
                    pages = "434--443",
                    abstract = "With climate change becoming a cause of concern worldwide, it becomes essential to gauge people{'}s reactions. This can help educate and spread awareness about it and help leaders improve decision-making. This work explores the fine-grained classification and Stance detection of climate change-related social media text. Firstly, we create two datasets, ClimateStance and ClimateEng, consisting of 3777 tweets each, posted during the 2019 United Nations Framework Convention on Climate Change and comprehensively outline the dataset collection, annotation methodology, and dataset composition. Secondly, we propose the task of Climate Change stance detection based on our proposed ClimateStance dataset. Thirdly, we propose a fine-grained classification based on the ClimateEng dataset, classifying social media text into five categories: Disaster, Ocean/Water, Agriculture/Forestry, Politics, and General. We benchmark both the datasets for climate change stance detection and fine-grained classification using state-of-the-art methods in text classification. We also create a Reddit-based dataset for both the tasks, ClimateReddit, consisting of 6262 pseudo-labeled comments along with 329 manually annotated comments for the label. We then perform semi-supervised experiments for both the tasks and benchmark their results using the best-performing model for the supervised experiments. Lastly, we provide insights into the ClimateStance and ClimateReddit using part-of-speech tagging and named-entity recognition.",
                }
            }
```