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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2311.09675 |
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| _version_ | 1866918099388727296 |
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| author | Antoniak, Maria Mire, Joel Sap, Maarten Ash, Elliott Piper, Andrew |
| author_facet | Antoniak, Maria Mire, Joel Sap, Maarten Ash, Elliott Piper, Andrew |
| contents | Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans. We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans. We illuminate distributional characteristics of storytelling on a large community-centric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter- and intra-community research. Finally, we discuss implications of our tools and analyses for narratology and the study of online communities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_09675 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Where Do People Tell Stories Online? Story Detection Across Online Communities Antoniak, Maria Mire, Joel Sap, Maarten Ash, Elliott Piper, Andrew Computation and Language Story detection in online communities is a challenging task as stories are scattered across communities and interwoven with non-storytelling spans within a single text. We address this challenge by building and releasing the StorySeeker toolkit, including a richly annotated dataset of 502 Reddit posts and comments, a detailed codebook adapted to the social media context, and models to predict storytelling at the document and span levels. Our dataset is sampled from hundreds of popular English-language Reddit communities ranging across 33 topic categories, and it contains fine-grained expert annotations, including binary story labels, story spans, and event spans. We evaluate a range of detection methods using our data, and we identify the distinctive textual features of online storytelling, focusing on storytelling spans. We illuminate distributional characteristics of storytelling on a large community-centric social media platform, and we also conduct a case study on r/ChangeMyView, where storytelling is used as one of many persuasive strategies, illustrating that our data and models can be used for both inter- and intra-community research. Finally, we discuss implications of our tools and analyses for narratology and the study of online communities. |
| title | Where Do People Tell Stories Online? Story Detection Across Online Communities |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2311.09675 |