Trait cheerfulness, seriousness, and bad mood outperform personality traits of the five-factor model in explaining variance in humor behaviors and well-being among adolescents

Wagner, Lisa; Ruch, Willibald

doi:10.1007/s12144-020-00629-z

Cited by 6 publications

(1 citation statement)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Mood is thought to impact what is considered to be humorous or not. (Wagner and Ruch, 2020) Currently in NLP shared tasks, there is scant admission of these issues. Humor is treated as a stable target, and humorous texts are subjected to binary classification and humor score prediction, with little recognition that gold standard labels for these constructs simply do not exist.…”

Section: Introductionmentioning

confidence: 99%

Crossing the Line: Where do Demographic Variables Fit into Humor Detection?

Meaney¹

2020

Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

View full text Add to dashboard Cite

Recent shared tasks in humor classification have struggled with two issues: scope and subjectivity. Regarding scope, many task datasets either comprise a highly constrained genre of humor which does not broadly represent the genre, or the data collection is so indiscriminate that the inter-annotator agreement on its comic content is drastically low. In terms of subjectivity, these tasks typically average over all annotators' judgments, in spite of the fact that humor is highly subjective and varies both between and within cultures. We propose a dataset which maintains a broad scope but which addresses subjectivity. We will collect demographic information about the data's humor annotators in order to bin ratings more sensibly. We also suggest the addition of an 'offensive' label to reflect the fact a text may be humorous to one group, but offensive to another. This would allow for more meaningful shared tasks and could lead to better performance on downstream applications, such as content moderation.

show abstract

Section: Introductionmentioning

confidence: 99%