ZhankuiHe/reddit_movie_raw

Dataset Card for Reddit-Movie-raw

Dataset Summary

This dataset provides the raw text from Reddit related to movie recommendation conversations. The dataset is extracted from the data dump of pushshift.io and only for research use.

Disclaimer

⚠️ Please note that conversations processed from Reddit raw data may include content that is not entirely conducive to a positive experience (e.g., toxic speech). Exercise caution and discretion when utilizing this information.

Folder Structure

We explain our data folder as follows:

reddit_movie_raw
├── IMDB-database
│   ├── clean.py # script to obtain clean IMDB movie titles, which can be used for movie name matching if needed.
│   ├── movie_clean.tsv # results after movie title cleaning 
│   ├── title.basics.tsv # original movie title information from IMDB
│   └── title.ratings.tsv # # original movie title and rating information from IMDB
├── Reddit-Movie-large
│   ├── sentences.jsonl # raw sentences from the subreddit/* data, it can be used for following processing
│   └── subreddit # raw text from different subreddits from Jan. 2012 to Dec. 2022 (large)
│       ├── bestofnetflix.jsonl
│       ├── movies.jsonl
│       ├── moviesuggestions.jsonl
│       ├── netflixbestof.jsonl
│       └── truefilm.jsonl
└── Reddit-Movie-small
    ├── sentences.jsonl # raw sentences from the subreddit/* data, it can be used for following processing
    └── subreddit # raw text from different subreddits from Jan. 2022 to Dec. 2022 (small)
        ├── bestofnetflix.jsonl
        ├── movies.jsonl
        ├── moviesuggestions.jsonl
        ├── netflixbestof.jsonl
        └── truefilm.jsonl

Data Processing

We also provide first-version processed Reddit-Movie datasets as Reddit-Movie-small-V1 and Reddit-Movie-large-V1. Join us if you want to improve the processing quality as well!

Citation Information

Please cite these two papers if you used this raw data, thanks!

@inproceedings{baumgartner2020pushshift,
  title={The pushshift reddit dataset},
  author={Baumgartner, Jason and Zannettou, Savvas and Keegan, Brian and Squire, Megan and Blackburn, Jeremy},
  booktitle={Proceedings of the international AAAI conference on web and social media},
  volume={14},
  pages={830--839},
  year={2020}
}

@inproceedings{he23large,
  title = Large language models as zero-shot conversational recommenders",
  author = "Zhankui He and Zhouhang Xie and Rahul Jha and Harald Steck and Dawen Liang and Yesu Feng and Bodhisattwa Majumder and Nathan Kallus and Julian McAuley",
  year = "2023",
  booktitle = "CIKM"
}

Please contact Zhankui He if you have any questions or suggestions.