A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866914049238761472 |
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| author | Ding, Xiaohan Ping, Kaike Çarık, Buse Rho, Eugenia |
| author_facet | Ding, Xiaohan Ping, Kaike Çarık, Buse Rho, Eugenia |
| contents | Understanding causal language in informal discourse is a core yet underexplored challenge in NLP. Existing datasets largely focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions, particularly those found in informal, user-generated social media posts. We introduce CausalTalk, a multi-level dataset of five years of Reddit posts (2020-2024) discussing public health related to the COVID-19 pandemic, among which 10120 posts are annotated across four causal tasks: (1) binary causal classification, (2) explicit vs. implicit causality, (3) cause-effect span extraction, and (4) causal gist generation. Annotations comprise both gold-standard labels created by domain experts and silver-standard labels generated by GPT-4o and verified by human annotators. CausalTalk bridges fine-grained causal detection and gist-based reasoning over informal text. It enables benchmarking across both discriminative and generative models, and provides a rich resource for studying causal reasoning in social media contexts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_16722 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse Ding, Xiaohan Ping, Kaike Çarık, Buse Rho, Eugenia Computation and Language Understanding causal language in informal discourse is a core yet underexplored challenge in NLP. Existing datasets largely focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions, particularly those found in informal, user-generated social media posts. We introduce CausalTalk, a multi-level dataset of five years of Reddit posts (2020-2024) discussing public health related to the COVID-19 pandemic, among which 10120 posts are annotated across four causal tasks: (1) binary causal classification, (2) explicit vs. implicit causality, (3) cause-effect span extraction, and (4) causal gist generation. Annotations comprise both gold-standard labels created by domain experts and silver-standard labels generated by GPT-4o and verified by human annotators. CausalTalk bridges fine-grained causal detection and gist-based reasoning over informal text. It enables benchmarking across both discriminative and generative models, and provides a rich resource for studying causal reasoning in social media contexts. |
| title | A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.16722 |