Where Do People Tell Stories Online? Story Detection Across Online Communities
This work addresses the problem of identifying storytelling in online communities for researchers in narratology and social media analysis, though it is incremental as it builds on existing detection methods with new data and tools.
The authors tackled the challenge of detecting stories scattered across online communities by creating the StorySeeker toolkit, which includes a dataset of 502 annotated Reddit posts and models for document and span-level detection, revealing distinctive textual features of storytelling.
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.