metadata
language:
- en
task_categories:
- text-classification
pretty_name: HumanMOD (AMCIS 2023)
size_categories:
- 10M<n<100M
We are excited to share the release of the HumanMOD dataset, unveiled in our AMCIS 2023 paper .
Wang, Kanlun; Fu, Zhe; Zhou, Lina; and Zhang, Dongsong, "How Does User Engagement Support Content Moderation? A Deep Learning-based Comparative Study" (2023). AMCIS 2023 Proceedings. 3. https://aisel.aisnet.org/amcis2023/sig_aiaa/sig_aiaa/3
Dataset Summary:
- The data collection was limited to public online communities to comply with the platform's privacy policy.
- We leveraged a Pushshift Reddit API to scrape posts from 40 subreddits daily across four different domains from Aug 24 to October 28, 2022, resulting in 104,674 posts.
- To enhance the ecological validity of the study findings, we used a PRAW API to perform another round of data collection of the collected posts 2 months later to validate whether the post content was moderated or not.
- Thereafter, we used a snowballing approach to collect the corresponding comments on all the posts.
- The metadata includes post content, post time, comment content, comment time, karma score, etc.
- We set a threshold for the minimum number of comments to 2 and an upper bound for the number of direct comments to 15 to facilitate the extraction of graph-based structural information.
- The final dataset consists of 8,511 moderated posts and another 8,511 not moderated posts that were randomly selected from the remainder of the dataset.
- All the posts were commented on, with a total of 148,344 comments.