---
title: "The Effect of Sociocultural Variables on Sarcasm Communication Online (2020)"
description: "Sarcasm on social networks says one thing and means another, yet most detectors look only at cues inside the… 57 references, cited by 15 papers here."
authors: ["Silviu Vlad Oprea", "Walid Magdy"]
year: 2020
venue: "arXiv:2004.04945"
source: "https://arxiv.org/abs/2004.04945"
doi: "10.48550/arxiv.2004.04945"
arxiv: "2004.04945"
licence: "arXiv non-exclusive distribution licence (read only, no redistribution)"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/oprea-2020-effect-sociocultural-variables-sarcasm-communication/"
---
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# The Effect of Sociocultural Variables on Sarcasm Communication Online

**Authors:** [Silviu Vlad Oprea](https://md.research.elamur.ai/free/en/authors/silviu-oprea/) and [Walid Magdy](https://md.research.elamur.ai/free/en/authors/walid-magdy/) · **Year:** 2020 · **Venue:** [arXiv:2004.04945](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-human-computer-interaction/) ([Proceedings of the ACM on Human-Computer Interaction 2020](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-acm-on-human-computer-interaction/2020/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [arXiv non-exclusive distribution licence (read only, no redistribution)](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2004.04945) · [PDF](https://arxiv.org/pdf/2004.04945) · [DOI 10.48550/arxiv.2004.04945](https://doi.org/10.48550/arxiv.2004.04945) · [arXiv 2004.04945](https://arxiv.org/abs/2004.04945) · [OpenAlex](https://openalex.org/W3015878272)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [15](https://md.research.elamur.ai/free/en/papers/oprea-2020-effect-sociocultural-variables-sarcasm-communication/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Sarcasm on social networks says one thing and means another, yet most detectors look only at cues inside the message. Psycholinguistics suggests that the backgrounds of speaker and listener also matter, but there was little quantitative evidence for online settings. The authors used a crowdsourcing platform to collect tweets from the people who wrote them, so each tweet carries its author's own "intended" sarcasm label. They then formed annotator groups that differ in gender, age, country (UK vs US) and English nativeness, and had each group label the same tweets ("perceived" labels) in two conditions. In the first, annotators saw only the tweet text. In the second, they followed a link and could use the surrounding tweets and the profile. Intended and perceived labels were compared with f-score plus significance testing. Age and English nativeness clearly shaped whether sarcasm was understood, and gender and country also showed effects in parts of the paper. Context weakened most effects, but age stayed influential. The authors argue that sarcasm and sentiment tools should model the interlocutors, not only the text.

## Key points
- Four sociocultural variables were studied, chosen from psycholinguistic studies and linguistic theories of sarcasm: gender, age, country (UK, US) and English language nativeness.
- Gold labels come from the tweet authors (speakers), not from outside annotators. This lets the study measure communicative success rather than just agreement between annotators, which is how it differs from earlier work by Joshi et al.
- There are two settings: text only, and link with timeline and profile context. Context reduces the influence of nativeness and gender, but not of age.
- Only two speaker backgrounds were covered, F-25-34-UK and M-25-34-US. The authors add some observations that are not statistically tested:
  - UK females seem to use more overt sarcasm.
  - Communication across genders works better in the UK.
  - Communication across ages works better in the US.
- The survey web apps and the scripts for aggregation and significance testing are offered as open science. The paper published at CSCW 2020.

## Interesting ideas
- The ground truth for an intent-laden phenomenon is collected from the producer, so "correct" means "understood as meant" rather than "what most readers think". This could apply to any non-literal language.
- Running the same items with and without wider context shows how much a judgment depends on surrounding information versus the bare utterance.
- Annotators are split into treatment groups by background, and each group's agreement with the gold labels is compared with significance tests. This reveals systematic reader biases instead of averaging them away.
- A social trait could be represented as the joint embedding (e.g. ParagraphVector) of many texts written by people who share that trait.
- Speaker traits act as cues that a listener combines with the utterance to infer non-literal intent.

## For Mova

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

- **I1** (frames): irony and implicature tests in crate prag could be scored against author-declared intent, so that what a typical reader would guess is not treated as truth.
- **I2** (learn): evaluations could include a text-only condition next to a full-document condition, as a control showing whether a pragmatic decision really uses context.
- **I3** (learn): silver data from witness parsers or LLM teacher tiers could be grouped by source, and each group's divergence tested, instead of only being merged by vote.
- **I4** (world): a speaker entity in the world of a text could carry an explicit, inspectable profile attribute (such as age group or nativeness) that the irony component is allowed to consult.
- **I5** (frames): the irony detector could take speaker attributes as named, explainable features beside lexical cues, and record which one triggered a sarcastic reading.

*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

- [University of Edinburgh](https://md.research.elamur.ai/free/en/universities/university-of-edinburgh/) — University, Edinburgh, United Kingdom · 255 works · 68,771 citations

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## Cite

Cite as: Silviu Vlad Oprea and Walid Magdy. 2020. The Effect of Sociocultural Variables on Sarcasm Communication Online. arXiv:2004.04945.

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

```bibtex
@misc{oprea2020effect,
  title = {{The Effect of Sociocultural Variables on Sarcasm Communication Online}},
  author = {Silviu Vlad Oprea and Walid Magdy},
  year = {2020},
  doi = {10.48550/arxiv.2004.04945},
  eprint = {2004.04945},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2004.04945}
}
```

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*Updated 24 October 2026*