---
title: "Perceived and Intended Sarcasm Detection with Graph Attention"
description: "The authors build a heterogeneous graph over the SPIRS Twitter corpus. 84.2% F1. 48 references, cited by 6 papers here."
authors: ["Joan Plepi", "Lucie Flek"]
year: 2021
venue: "Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)"
source: "https://aclanthology.org/2021.wnut-1.12/"
doi: "10.18653/v1/2021.wnut-1.12"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/plepi-2021-perceived-intended-sarcasm-detection-graph/"
---
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# Perceived and Intended Sarcasm Detection with Graph Attention Networks

**Authors:** [Joan Plepi](https://md.research.elamur.ai/free/en/authors/joan-plepi/) and [Lucie Flek](https://md.research.elamur.ai/free/en/authors/lucie-flek/) · **Year:** 2021 · **Venue:** [Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)](https://md.research.elamur.ai/free/en/venues/wnut/) ([Workshop on Noisy User-generated Text 2021](https://md.research.elamur.ai/free/en/venues/wnut/2021/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.wnut-1.12/) · [PDF](https://aclanthology.org/2021.wnut-1.12.pdf) · [DOI 10.18653/v1/2021.wnut-1.12](https://doi.org/10.18653/v1/2021.wnut-1.12) · [OpenAlex](https://openalex.org/W3211868628)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/plepi-2021-perceived-intended-sarcasm-detection-graph/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper argues that sarcasm detection should look beyond the post itself. The relationship between a writer and their audience, and the writer's own habits, also shape how sarcasm is used and read. The authors build a heterogeneous graph over the SPIRS Twitter corpus. Users and tweets are nodes, linked by three edge types: user-to-user interactions, tweet-to-tweet links inside a conversation thread, and tweet-to-author links. Tweets are initialised with SentenceBERT vectors. Users are initialised with user2vec vectors learned from more than 10M historical tweets that the authors collected. A Graph Attention Network (GAT) refines these vectors, and a small feed-forward classifier labels the target tweet from its own vector joined with its author's vector. The full user+tweet GAT reaches 84.2% F1, against 69.9% for fine-tuned BERT, while using far fewer trainable parameters. A second experiment separates sarcasm the author intended from sarcasm only readers perceived. The second kind proves much harder, which suggests that author-centred context helps mainly with intent.

## Key points
- The data is SPIRS (Shmueli et al., 2020): about 30K labeled tweets from roughly 19k users, with sarcastic, non-sarcastic, oblivious, elicit and cue tweets. The authors added over 10M unlabeled past tweets for user context. The resulting graph has 108K nodes, density 0.00002 and only 32% homophily.
- F1 scores (mean of 10 seeds):
  - BERT: 69.9
  - BERT + user2vec: 73.4
  - BERT + tweet-tweet GAT: 70.1 (the graph was too sparse to help)
  - BERT + user-only GAT: 76.1
  - User+tweet GAT: 84.2, with about 500K trainable parameters versus 110M for BERT
- Cue tweets reveal the label, so they were left out of the main graph. Putting them back inflates F1 to 94.5. Dropping elicit tweets lowers F1 to 82.0, and dropping oblivious tweets lowers it to 81.4.
- On intended vs perceived sarcasm, the GAT scores 71.2 F1 against 69.0 for BERT. Perceived sarcasm reaches only about 56 F1, while self-reported sarcasm reaches about 84. Perceived cases are mostly misclassified as the more frequent self-reported class.
- The attention analysis shows that tweet–author edges carry the most weight, while oblivious and elicit edges get little. Thread context matters mainly when the user-level prior is weak or inconsistent. Early GCN experiments gave worse and less interpretable results than GAT.

## Interesting ideas
- Using typed nodes and typed edges (person, utterance, authorship, interaction, same-thread) lets a single model separate who said something from what was said and where it was said, and attention weights then show which link drove a decision.
- When a label-revealing context element is added back, the score jumps to near-perfect, which makes it a ready-made leakage check for any context-enriched model.
- Treating the writer's intended sarcasm and the reader's perceived sarcasm as separate targets shows that speaker-side evidence explains intent far better than reception.
- The attention patterns suggest a simple fallback order: rely on the speaker's habitual behaviour first, and consult the surrounding discourse only when that prior is missing or contradictory.
- The ablation design removes one category of context at a time (elicit posts, oblivious replies) to measure how much each kind of neighbour contributes.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (world): The world of a text could store speaker–utterance–addressee links as explicit typed edges. Each decision could then cite the specific edge (for example "this character usually mocks") it relied on.
- **I2** (learn): Evaluation could include a negative control that deliberately feeds in or removes answer-revealing context. A suspiciously large gain would then flag that a rule or model is reading the answer rather than reasoning.
- **I3** (frames): The irony component in prag could record both whose interpretation a non-literal reading belongs to and how a given listener character takes it. This would let fables model characters who miss or misread a sarcastic remark.
- **I4** (frames): Irony and implicature detection could first check a character's established stance in the text. It would escalate to discourse cues only when that stance is absent or conflicts with the utterance, keeping each step explainable.
- **I5** (learn): The same remove-one-source ablation could quantify how much each witness parser or each knowledge source contributes to silver-data agreement and to final answers.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Philipps University of Marburg](https://md.research.elamur.ai/free/en/universities/philipps-university-of-marburg/) — University, Marburg, Germany · 4 works · 225 citations

📚 Shelves (4): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 9,085 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 8,888 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 9,296 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 9,206 works

🧭 Topics (1): [😊 Sentiment & emotion](https://md.research.elamur.ai/free/en/topics/sentiment-emotion/) · 609 works

## Cite

Cite as: Joan Plepi and Lucie Flek. 2021. Perceived and Intended Sarcasm Detection with Graph Attention Networks. Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{plepi2021perceived,
  title = {{Perceived and Intended Sarcasm Detection with Graph Attention Networks}},
  author = {Joan Plepi and Lucie Flek},
  booktitle = {Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021)},
  year = {2021},
  doi = {10.18653/v1/2021.wnut-1.12},
  url = {https://doi.org/10.18653/v1/2021.wnut-1.12}
}
```

- [← previous Learned Construction Grammars Converge Across Registers Given Increased Exposure](https://md.research.elamur.ai/free/en/papers/dunn-2021-learned-construction-grammars-converge-across/)
- [next → “Laughing at you or with you”: The Role of Sarcasm in Shaping the Disagreement Space](https://md.research.elamur.ai/free/en/papers/ghosh-2021-laughing-you-or-you-role/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/6/)

## References

48 references, 18 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 24 October 2026*