# Researchers eye-view of sarcasm detection in social media textual content

arXiv: https://arxiv.org/abs/2304.08582 · HTML: https://mlsanity.com/paper/2304.08582/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 64 references that are in the map.
- First posted on arXiv 17 Apr 2023.

## Builds on (most cited first, 16 of 16)

- [Towards Multimodal Sarcasm Detection (An _Obviously_ Perfect Paper)](https://mlsanity.com/paper/1906.01815/) (2019)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [iSarcasm: A Dataset of Intended Sarcasm](https://mlsanity.com/paper/1911.03123/) (2019)
- [Reasoning with Sarcasm by Reading In-between](https://mlsanity.com/paper/1805.02856/) (2018)
- [A Transformer-based approach to Irony and Sarcasm detection](https://mlsanity.com/paper/1911.10401/) (2019)
- [Modelling Context with User Embeddings for Sarcasm Detection in Social Media](https://mlsanity.com/paper/1607.00976/) (2016)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Sarcasm SIGN: Interpreting Sarcasm with Sentiment Based Monolingual Machine Translation](https://mlsanity.com/paper/1704.06836/) (2017)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [Harnessing Cognitive Features for Sarcasm Detection](https://mlsanity.com/paper/1701.05574/) (2017)
- [Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media](https://mlsanity.com/paper/2005.11424/) (2020)
- [Sarcasm Detection in Tweets with BERT and GloVe Embeddings](https://mlsanity.com/paper/2006.11512/) (2020)
- [Sarcasm Detection using Context Separators in Online Discourse](https://mlsanity.com/paper/2006.00850/) (2020)
- [Augmenting Data for Sarcasm Detection with Unlabeled Conversation Context](https://mlsanity.com/paper/2006.06259/) (2020)

## Abstract (arXiv)

The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challenging task for machines to differentiate sarcastic sentences from non-sarcastic sentences. There are no exact rules based on which model will accurately detect sarcasm from many text corpus in the current situation. So, one needs to focus on optimistic and forthcoming approaches in the sarcasm detection domain. This paper discusses various sarcasm detection techniques and concludes with some approaches, related datasets with optimal features, and the researcher's challenges.