The rJokes Dataset: a Large Scale Humor Collection

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 6136–6141 Marseille, 11–16 May 2020 c⃝ European Language Resources Association (ELRA), licensed under CC-BY-NC 6136 The rJokes Dataset: a Large Scale Humor Collection Orion Weller and Kevin Seppi Brigham Young University 3361 TMCB, Campus Dr, Provo, UT 84604 {orionw, kseppi}@byu.edu Abstract Humor is a complicated language phenomenon that depends upon many factors, including topic, date, and recipient. Because of this variation, it can be hard to determine what exactly makes a joke humorous, leading to difficulties in joke identification and related tasks. Furthermore, current humor datasets are lacking in both joke variety and size, with almost all current datasets having less than 100k jokes. In order to alleviate this issue we compile a collection of over 550,000 jokes posted over an 11 year period on the Reddit r/Jokes subreddit (an online forum), providing a large scale humor dataset that can easily be used for a myriad of tasks. This dataset also provides quantitative metrics for the level of humor in each joke, as determined by subreddit user feedback. We explore this dataset through the years, examining basic statistics, most mentioned entities, and sentiment proportions. We also introduce this dataset as a task for future work, where models learn to predict the level of humor in a joke. On that task we provide strong state-of-the-art baseline models and show room for future improvement. We hope that this dataset will not only help those researching computational humor, but also help social scientists who seek to understand popular culture through humor. Keywords: Computational Humor, Social Media, rJokes Corpus 1. Introduction Understanding humor has been a large area of research for fields such as psychology, linguistics, and even health re- search (Wolff et al., 1934; Norrick, 1993; Knapp et al., 1992). It is an intrinsic aspect of our nature, yet often fickle and difficult for even humans to understand. Computational research in the field of natural language processing has fo- cused on understanding (Hempelmann, 2008), classifying (Zhang and Liu, 2014; Chen and Soo, 2018), and generat- ing (He et al., 2019; Valitutti et al., 2013) humorous text in order to better understand the structures that create humor (L´opez and Vaid, 2017; Mihalcea and Strapparava, 2005). We see this focus on humor illustrated in recent Se- mEval competitions, which include pun detection in En- glish (Miller et al., 2017) in 2017 and a humorous headline regression task in 2020 (Hossain et al., 2019). However, these tasks (i.e. identifying humor in a dataset full of hu- morous and non-humorous instances), as well as other re- cent tasks in humor identification (Yang et al., 2015; Chen and Soo, 2018; Weller and Seppi, 2019) focus on small datasets with between 10-100k instances, usually on spe- cific areas of humor, e.g. puns (Yang et al., 2015), Ted Talks (Chen and Soo, 2018), or TV shows (Purandare and Litman, 2006). We see a similar rise of popularity in the area of humor generation, with recent models including …

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