# Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media

arXiv: https://arxiv.org/abs/2005.11424 · HTML: https://mlsanity.com/paper/2005.11424/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [KoCoSa: Korean Context-aware Sarcasm Detection Dataset](https://mlsanity.com/paper/2402.14428/) (2024)
- [Optimism, Expectation, or Sarcasm? Multi-Class Hope Speech Detection in Spanish and English](https://mlsanity.com/paper/2504.17974/) (2025)
- ["When Words Fail, Emojis Prevail": Generating Sarcastic Utterances with Emoji Using Valence Reversal and Semantic Incongruity](https://mlsanity.com/paper/2305.04105/) (2023)
- [Sarcasm Detection in Twitter -- Performance Impact while using Data Augmentation: Word Embeddings](https://mlsanity.com/paper/2108.09924/) (2021)
- [Researchers eye-view of sarcasm detection in social media textual content](https://mlsanity.com/paper/2304.08582/) (2023)
- [Token-free Models for Sarcasm Detection](https://mlsanity.com/paper/2505.01006/) (2025)

## Abstract (arXiv)

We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions. Our model uses deep transformer layers to perform multi-head attentions among the target utterance and the relevant context in the thread. The context-aware models are evaluated on two datasets from social media, Twitter and Reddit, and show 3.1% and 7.0% improvements over their baselines. Our best models give the F1-scores of 79.0% and 75.0% for the Twitter and Reddit datasets respectively, becoming one of the highest performing systems among 36 participants in this shared task.