Papers with Reddit dataset
FANATIC: FAst Noise-Aware TopIc Clustering (2021.findings-emnlp)
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| Challenge: | a large amount of data can be computationally prohibitive for extracting topic noise . many clustering algorithms assign documents to one of the available clusters . a novel algorithm that efficiently distinguishes documents from genuine topics is developed . |
| Approach: | They propose an algorithm that efficiently distinguishes documents from genuine topics . they use a reddit dataset to showcase the algorithm as it contains short, noisy data . |
| Outcome: | The proposed algorithm outperforms hdbscan and hANATIC on a Twitter dataset. |
Splits! Flexible Sociocultural Linguistic Investigation at Scale (2026.acl-long)
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| Challenge: | Variation in language use offers a rich lens into cultural perspectives, values, and opinions. |
| Approach: | They propose to construct a "sandbox" for systematic and flexible sociolinguistic research by splitting a reddit dataset into demographically/topically split SLPs. |
| Outcome: | The proposed method analyzes a demographically/topically split Reddit dataset validated by self-identification and replicating several known SLPs from existing literature. |
Promoting Constructive Deliberation: Reframing for Receptiveness (2024.findings-emnlp)
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| Challenge: | Current methods for promoting pro-social discussion and debate online are limited. |
| Approach: | They propose automatic reframing of disagreeing responses to signal receptiveness to a preceding comment. |
| Outcome: | The proposed framework can be used to promote constructive debate and debate online. |
FACTOID: A New Dataset for Identifying Misinformation Spreaders and Political Bias (2022.lrec-1)
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| Challenge: | Proactively identifying misinformation spreaders is an important step towards mitigating the impact of fake news on our society. |
| Approach: | They propose a new reddit dataset for fake news spreader analysis, called FACTOID, which tracks political discussions on Reddit since the beginning of 2020. |
| Outcome: | The proposed dataset contains over 4K users with 3.4M posts and includes their credibility level (very low to very high) and political bias strength (extreme right to extreme left). |
The Language of Trauma: Modeling Traumatic Event Descriptions Across Domains with Explainable AI (2024.findings-emnlp)
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| Challenge: | Psychological trauma can manifest following various distressing events, but studies focus on a single aspect of trauma, often neglecting the transferability of findings across different scenarios. |
| Approach: | They propose a language model that fine-tunes a single aspect of trauma to better predict traumatic events across domains. |
| Outcome: | The proposed model outperforms large language models on trauma-related datasets . it also outperformed models on court data, counseling conversations, and forum posts . |