# Modelling Context with User Embeddings for Sarcasm Detection in Social Media

arXiv: https://arxiv.org/abs/1607.00976 · HTML: https://mlsanity.com/paper/1607.00976/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2016.

## Built on by (most cited first, 26 of 26)

- [Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification](https://mlsanity.com/paper/2209.13017/) (2022)
- [Large Human Language Models: A Need and the Challenges](https://mlsanity.com/paper/2312.07751/) (2023)
- [Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection](https://mlsanity.com/paper/2104.09261/) (2021)
- [Augmenting emotion features in irony detection with Large language modeling](https://mlsanity.com/paper/2404.12291/) (2024)
- [Can Large Vision-Language Models Understand Multimodal Sarcasm?](https://mlsanity.com/paper/2508.03654/) (2025)
- [AraBERT and Farasa Segmentation Based Approach For Sarcasm and Sentiment Detection in Arabic Tweets](https://mlsanity.com/paper/2103.01679/) (2021)
- [News Headlines Dataset For Sarcasm Detection](https://mlsanity.com/paper/2212.06035/) (2022)
- [InterCLIP-MEP: Interactive CLIP and Memory-Enhanced Predictor for Multi-modal Sarcasm Detection](https://mlsanity.com/paper/2406.16464/) (2024)
- [RCLMuFN: Relational Context Learning and Multiplex Fusion Network for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2412.13008/) (2024)
- [Interest-oriented Universal User Representation via Contrastive Learning](https://mlsanity.com/paper/2109.08865/) (2021)
- [Perceived and Intended Sarcasm Detection with Graph Attention Networks](https://mlsanity.com/paper/2110.04001/) (2021)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [Does Commonsense help in detecting Sarcasm?](https://mlsanity.com/paper/2109.08588/) (2021)
- [Computational Sarcasm Analysis on Social Media: A Systematic Review](https://mlsanity.com/paper/2209.06170/) (2022)
- [X-PuDu at SemEval-2022 Task 6: Multilingual Learning for English and Arabic Sarcasm Detection](https://mlsanity.com/paper/2211.16883/) (2022)
- [BNS-Net: A Dual-channel Sarcasm Detection Method Considering Behavior-level and Sentence-level Conflicts](https://mlsanity.com/paper/2309.03658/) (2023)
- [Modelling Visual Semantics via Image Captioning to extract Enhanced Multi-Level Cross-Modal Semantic Incongruity Representation with Attention for Multimodal Sarcasm Detection](https://mlsanity.com/paper/2408.02595/) (2024)
- [User Factor Adaptation for User Embedding via Multitask Learning](https://mlsanity.com/paper/2102.11103/) (2021)
- ["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 a Disaster Context](https://mlsanity.com/paper/2308.08156/) (2023)
- [Dual Modality-Aware Gated Prompt Tuning for Few-Shot Multimodal Sarcasm Detection](https://mlsanity.com/paper/2507.04468/) (2025)
- [Interpretable Multi-Head Self-Attention model for Sarcasm Detection in social media](https://mlsanity.com/paper/2101.05875/) (2021)
- [Modeling Proficiency with Implicit User Representations](https://mlsanity.com/paper/2110.08011/) (2021)
- [Researchers eye-view of sarcasm detection in social media textual content](https://mlsanity.com/paper/2304.08582/) (2023)
- [When Do "More Contexts" Help with Sarcasm Recognition?](https://mlsanity.com/paper/2403.12469/) (2024)
- [Psychology-Driven Enhancement of Humour Translation](https://mlsanity.com/paper/2507.09259/) (2025)

## Abstract (arXiv)

We introduce a deep neural network for automated sarcasm detection. Recent work has emphasized the need for models to capitalize on contextual features, beyond lexical and syntactic cues present in utterances. For example, different speakers will tend to employ sarcasm regarding different subjects and, thus, sarcasm detection models ought to encode such speaker information. Current methods have achieved this by way of laborious feature engineering. By contrast, we propose to automatically learn and then exploit user embeddings, to be used in concert with lexical signals to recognize sarcasm. Our approach does not require elaborate feature engineering (and concomitant data scraping); fitting user embeddings requires only the text from their previous posts. The experimental results show that our model outperforms a state-of-the-art approach leveraging an extensive set of carefully crafted features.