Linguistic Features of Humor in Academic Writing
Abstract
A corpus of 313 freshman college essays was analyzed in order to better understand the forms and functions of humor in academic writing. Human ratings of humor and wordplay were statistically aggregated using Factor Analysis to provide an overall Humor component score for each essay in the corpus. In addition, the essays were also scored for overall writing quality by human raters, which correlated (r = .195) with the humor component score. Correlations between the humor component scores and linguistic features were examined. To investigate the potential for linguistic features to predict the Humor component scores, regression analysis identified four linguistic indices that accounted for approximately 17.5% of the variance in humor scores. These indices were related to text descriptiveness (i.e., more adjective and adverb use), lower cohesion (i.e., less paragraph-to-paragraph similarity), and lexical sophistication (lower word frequency). The findings suggest that humor can be partially predicted by linguistic features in the text. Furthermore, there was a small but significant correlation between the humor and essay quality scores, suggesting a positive relation between humor and writing quality.
Keywords: humor, academic writing, text analysis, essay score, human rating
Full Text:
PDFReferences
American Psychological Association. (2010). Publication manual of the American psychological association. Washington DC: American Psychological Association.
Attardo, S., & Raskin, V. (1991). Script theory revis(it)ed: Joke similarity and joke representation model. Humor: International Journal of Humor Research 3(4). 293-347.
Biber, D., Conrad, S., & Leech, G. (2002). Longman student grammar of spoken and written English. Essex, GB: Longman.
Burfoot, C., & Baldwin, T. (2009). Automatic satire detection: Are you having a laugh? Association for Computational Linguistics International Joint Conference on Natural Language Processing 2009 Conference Short Papers, 161–164.
Buscaldi, D., & Rosso, P. (2007). Some experiments in humour recognition using the Italian wikiquote collection. In F. Masulli, S. Mita, & G. Pasi (eds.). Applications of Fuzzy Sets Theory, 464-468. Berlin, DE: Springer Berlin Heidelberg.
Campbell, J. D. & Katz, A. N. (2012). Are there necessary conditions for inducing a sense of sarcastic irony? Discourse Processes 49(6), 459–480.
Carvalho, P., Sarmento, L., Silva, M., & de Oliveira, E. (2009). Clues for detecting irony in user-generated contents: Oh ...!! It’s “so easy” ; - ). TSA ’09 1st International CIKM Workshop on Topic-Sentiment Analysis for Mass Opinion, 53–56.
Chafe, W. R. (1976). Givenness, contrastiveness, definiteness, subjects, topics, and point of view in subject and topic. In Charles. N. Li (ed.), Subject and topic, 25–56. New York, NY: Academic Press.
Cohen, J. (1992). A power primer. Psychological Bulletin, 112(1), 155-159.
Cook, G. (2000). Language play, language learning. New York, NY: Oxford University Press.
Coulson, S., & Kutas, M. (2001). Getting it: Human event-related brain response to jokes in good and poor comprehenders. Neuroscience Letters, 316(2), 71-74.
Cronk, B. C. & Schweigert, W. A. (1992). The comprehension of idioms: The effects of familiarity, literalness, and usage. Applied Psycholinguistics, 13(2), 131–146.
Crossley, S. A., Kyle, K., & McNamara, D. S. (2015). The tool for the automatic analysis of text cohesion (TAACO): Automatic assessment of local, global, and text cohesion. Behavior Research Methods, 1-11.
Crossley, S.A. & McNamara, D.S. (2010) Cohesion, coherence, and expert evaluations of writing proficiency. In Catrambone, R. and Ohlsson, S. (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society, pp. 984–989, Cognitive Science Society, Austin, TX.
Crossley, S. A., & McNamara, D. S. (2011). Text coherence and judgments of essay quality: Models of quality and coherence. In L. Carlson, C. Hoelscher, & T. F. Shipley (Eds.), Proceedings of the 29th Annual Conference of the Cognitive Science Society. (1236-1241). Austin, TX: Cognitive Science Society.
Deane, P. (2014). Using writing process and product features to assess writing quality and explore how those features relate to other literacy tasks. ETS Research Report Series, 2014(1), 1-23.
Devitt, A. J., Reiff, M. J., & Bawarshi, A. S. (2004). Scenes of writing: Strategies for composing with genres. Pearson/Longman.
Kyle, K., & Crossley, S. A. (2015). Automatically assessing lexical sophistication: Indices, tools, findings, and application. TESOL Quarterly, 49(4), 757-786.
Landauer, T., McNamara, D.S., Dennis, S., & Kintsch, W. (2007). LSA: A road to meaning. Mahwah, NJ: Lawrence Erlbaum Associates.
Martin, R. A. (2007). The psychology of humor: An integrative approach. San Diego, CA: Elsevier.
McNamara, D. S., Crossley, S. A., & McCarthy, P. M. (2010). Linguistic features of writing quality. Written Communication, 27(1), 57-86.
McNamara, D. S., Crossley, S. A., & Roscoe, R. (2013). Natural Language Processing in an Intelligent Writing Strategy Tutoring System. Behavior Research Methods, 45(2), 499-515.
Mihalcea, R. & Pulman, S. (2007). Characterizing humour: An exploration of features in humorous texts. In A. Gelbukh (Ed.), Computational Linguistics and Intelligent Text Processing, 337-347. New York: Springer Berlin Heidelberg.
Mihalcea, R. & Strapparava, C. (2005). Making computers laugh: Investigations in automatic humor recognition. Association for Computational Linguistics (ACL) Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, 531-538.
Mihalcea, R. & Strapparava, C. (2006). Learning to laugh (automatically): Computational models for humor recognition. Computational Intelligence, 22(2), 126-142.
Mihalcea, R., Strapparava, C., & Pulman, S. (2010). Computational models for incongruity detection in humour. In Alexander Gelbukh (Ed.), Computational linguistics and intelligent text processing, 364-374. Berlin, DE: Springer Berlin Heidelberg.
Palmquist, M. (2010). Joining the conversation: Writing in college and beyond. New York, NY: Bedford/St. Martin's.
Pitler, E. & Nenkova, A. (2008). Revisiting readability: A unified framework for predicting text quality. Association for Computational Linguistics (ACL) Proceedings of the Conference on Empirical Methods in Natural Language Processing, 186-195.
Reyes, A., Potthast, M., Rosso, P., & Stein, B. (2010). Evaluating humour features on web comments. Proceedings of the 7th International Conference on Language Resources and Evaluation, 1138-1141.
Reyes, A. & Rosso, P. (2011). Mining subjective knowledge from customer reviews: A specific case of irony detection. Association for Computational Linguistics (ACL) Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis, 118-124.
Reyes, A., Rosso, P., & Buscaldi, D. (2012). From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering, 74, 1-12.
Reyes, A., Rosso, P., & Veale, T. (2013). A multidimensional approach for detecting irony in twitter. Language Resources and Evaluation, 47(1), 239-268.
Ritchie, G. D. (2004). The linguistic analysis of jokes. New York, NY: Routledge.
Simpson, P. (2003). On the discourse of satire: Towards a stylistic model of satirical humor. Philadelphia, PA: John Benjamin’s Publishing Company.
Sheridan, H., Reingold, E., & Daneman, M. (2009). Using puns to study contextual influences on lexical ambiguity resolution: Evidence from eye movements. Psychonomic Bulletin & Review, 16(5), 875-881.
Skalicky, S., & Crossley, S. (2015). A statistical analysis of satirical Amazon. com product reviews. The European Journal of Humour Research, 2(3), 66-85.
Vaid, J., Hull, R., Heredia, R., Gerkens, D., & Martinez, F. (2003). Getting a joke: The time course of meaning activation in verbal humor. Journal of Pragmatics, 35(9), 1431-1449.
Witten, I. H., Frank, E., & Hall, M. (2011). Data mining: Practical machine learning tools and techniques. Burlington, MA: Morgan Kaufmann Publishers.
Refbacks
- There are currently no refbacks.
![Creative Commons License](http://licensebuttons.net/l/by/4.0/88x31.png)
This work is licensed under a Creative Commons Attribution 4.0 International License.
2010-2023 (CC-BY) Australian International Academic Centre PTY.LTD.
Advances in Language and Literary Studies
You may require to add the 'aiac.org.au' domain to your e-mail 'safe list’ If you do not receive e-mail in your 'inbox'. Otherwise, you may check your 'Spam mail' or 'junk mail' folders.