# Humor Detection in English-Hindi Code-Mixed Social Media Content : Corpus and Baseline System

arXiv: https://arxiv.org/abs/1806.05513 · HTML: https://mlsanity.com/paper/1806.05513/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [The Decades Progress on Code-Switching Research in NLP: A Systematic Survey on Trends and Challenges](https://mlsanity.com/paper/2212.09660/) (2022)
- [Multi-modal Sarcasm Detection and Humor Classification in Code-mixed Conversations](https://mlsanity.com/paper/2105.09984/) (2021)
- [Getting Serious about Humor: Crafting Humor Datasets with Unfunny Large Language Models](https://mlsanity.com/paper/2403.00794/) (2024)
- [M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations](https://mlsanity.com/paper/2108.01260/) (2021)
- [Switch Point biased Self-Training: Re-purposing Pretrained Models for Code-Switching](https://mlsanity.com/paper/2111.01231/) (2021)
- [Towards Multimodal Prediction of Spontaneous Humour: A Novel Dataset and First Results](https://mlsanity.com/paper/2209.14272/) (2022)
- [Comparative Study of Pre-Trained BERT Models for Code-Mixed Hindi-English Data](https://mlsanity.com/paper/2305.15722/) (2023)
- [Nipping in the Bud: Detection, Diffusion and Mitigation of Hate Speech on Social Media](https://mlsanity.com/paper/2201.00961/) (2022)
- [Revealing the impact of synthetic native samples and multi-tasking strategies in Hindi-English code-mixed humour and sarcasm detection](https://mlsanity.com/paper/2412.12761/) (2024)
- [Harmonizing Code-mixed Conversations: Personality-assisted Code-mixed Response Generation in Dialogues](https://mlsanity.com/paper/2401.12995/) (2024)
- [From Generalized Laughter to Personalized Chuckles: Unleashing the Power of Data Fusion in Subjective Humor Detection](https://mlsanity.com/paper/2312.11296/) (2023)

## Abstract (arXiv)

The tremendous amount of user generated data through social networking sites led to the gaining popularity of automatic text classification in the field of computational linguistics over the past decade. Within this domain, one problem that has drawn the attention of many researchers is automatic humor detection in texts. In depth semantic understanding of the text is required to detect humor which makes the problem difficult to automate. With increase in the number of social media users, many multilingual speakers often interchange between languages while posting on social media which is called code-mixing. It introduces some challenges in the field of linguistic analysis of social media content (Barman et al., 2014), like spelling variations and non-grammatical structures in a sentence. Past researches include detecting puns in texts (Kao et al., 2016) and humor in one-lines (Mihalcea et al., 2010) in a single language, but with the tremendous amount of code-mixed data available online, there is a need to develop techniques which detects humor in code-mixed tweets. In this paper, we analyze the task of humor detection in texts and describe a freely available corpus containing English-Hindi code-mixed tweets annotated with humorous(H) or non-humorous(N) tags. We also tagged the words in the tweets with Language tags (English/Hindi/Others). Moreover, we describe the experiments carried out on the corpus and provide a baseline classification system which distinguishes between humorous and non-humorous texts.