# Computational Sarcasm Analysis on Social Media: A Systematic Review

arXiv: https://arxiv.org/abs/2209.06170 · HTML: https://mlsanity.com/paper/2209.06170/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 167 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 30 of 47)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://mlsanity.com/paper/1906.05317/) (2019)
- [12-in-1: Multi-Task Vision and Language Representation Learning](https://mlsanity.com/paper/1912.02315/) (2019)
- [Style Transfer from Non-Parallel Text by Cross-Alignment](https://mlsanity.com/paper/1705.09655/) (2017)
- [Unsupervised Neural Machine Translation](https://mlsanity.com/paper/1710.11041/) (2017)
- [Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm](https://mlsanity.com/paper/1708.00524/) (2017)
- [Fusion of Detected Objects in Text for Visual Question Answering](https://mlsanity.com/paper/1908.05054/) (2019)
- [A Large Self-Annotated Corpus for Sarcasm](https://mlsanity.com/paper/1704.05579/) (2017)
- [Unsupervised Statistical Machine Translation](https://mlsanity.com/paper/1809.01272/) (2018)
- [Attentional Encoder Network for Targeted Sentiment Classification](https://mlsanity.com/paper/1902.09314/) (2019)
- [Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue](https://mlsanity.com/paper/1709.05404/) (2017)
- [Reasoning with Sarcasm by Reading In-between](https://mlsanity.com/paper/1805.02856/) (2018)
- [A Deeper Look into Sarcastic Tweets Using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1610.08815/) (2016)
- [R3: Reverse, Retrieve, and Rank for Sarcasm Generation with Commonsense Knowledge](https://mlsanity.com/paper/2004.13248/) (2020)
- [emoji2vec: Learning Emoji Representations from their Description](https://mlsanity.com/paper/1609.08359/) (2016)
- [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)
- [CASCADE: Contextual Sarcasm Detection in Online Discussion Forums](https://mlsanity.com/paper/1805.06413/) (2018)
- [NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification](https://mlsanity.com/paper/1811.05014/) (2018)
- [A Report on the 2020 Sarcasm Detection Shared Task](https://mlsanity.com/paper/2005.05814/) (2020)
- [Sarcasm Detection using Hybrid Neural Network](https://mlsanity.com/paper/1908.07414/) (2019)
- [Sentiment and Sarcasm Classification with Multitask Learning](https://mlsanity.com/paper/1901.08014/) (2019)
- [Deep contextualized word representations for detecting sarcasm and irony](https://mlsanity.com/paper/1809.09795/) (2018)
- [Sarcasm Analysis using Conversation Context](https://mlsanity.com/paper/1808.07531/) (2018)
- [The Role of Conversation Context for Sarcasm Detection in Online Interactions](https://mlsanity.com/paper/1707.06226/) (2017)
- [Are Word Embedding-based Features Useful for Sarcasm Detection?](https://mlsanity.com/paper/1610.00883/) (2016)
- [A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict](https://mlsanity.com/paper/2109.03587/) (2021)

## Built on by (most cited first, 2 of 2)

- [CAF-I: A Collaborative Multi-Agent Framework for Enhanced Irony Detection with Large Language Models](https://mlsanity.com/paper/2506.08430/) (2025)
- [EM2LDL: A Multilingual Speech Corpus for Mixed Emotion Recognition through Label Distribution Learning](https://mlsanity.com/paper/2511.20106/) (2025)

## Abstract (arXiv)

Sarcasm can be defined as saying or writing the opposite of what one truly wants to express, usually to insult, irritate, or amuse someone. Because of the obscure nature of sarcasm in textual data, detecting it is difficult and of great interest to the sentiment analysis research community. Though the research in sarcasm detection spans more than a decade, some significant advancements have been made recently, including employing unsupervised pre-trained transformers in multimodal environments and integrating context to identify sarcasm. In this study, we aim to provide a brief overview of recent advancements and trends in computational sarcasm research for the English language. We describe relevant datasets, methodologies, trends, issues, challenges, and tasks relating to sarcasm that are beyond detection. Our study provides well-summarized tables of sarcasm datasets, sarcastic features and their extraction methods, and performance analysis of various approaches which can help researchers in related domains understand current state-of-the-art practices in sarcasm detection.