---
title: "Findings of the Shared Task on Hope Speech Detection for Equality, Diversity — Chakravarthi 2021"
description: "They propose detecting and promoting supportive, encouraging, future-oriented messages instead. 41 references, cited by 82 papers here."
authors: ["Bharathi Raja Chakravarthi", "Vigneshwaran Muralidaran"]
year: 2021
venue: "Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion"
source: "https://aclanthology.org/2021.ltedi-1.8/"
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/chakravarthi-2021-findings-shared-task-hope-speech/"
---
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# Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion

**Authors:** [Bharathi Raja Chakravarthi](https://md.research.elamur.ai/free/en/authors/bharathi-raja-chakravarthi/) and [Vigneshwaran Muralidaran](https://md.research.elamur.ai/free/en/authors/vigneshwaran-muralidaran/) · **Year:** 2021 · **Venue:** Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2021.ltedi-1.8/) · [PDF](https://aclanthology.org/2021.ltedi-1.8.pdf) · [OpenAlex](https://openalex.org/W3154997565)

**Citations:** 87 citations (OpenAlex, 2026-10-14) · 82 in our library, of the 140,884 papers whose bibliographies we read

## Summary
This paper reports on the first shared task on hope speech, held at the LT-EDI-2021 workshop (EACL 2021). The organisers argue that social-media NLP has focused on suppressing harmful content, which can restrict how some groups express themselves. They propose detecting and promoting supportive, encouraging, future-oriented messages instead. Participants received the HopeEDI corpus, which contains YouTube comments in English and in code-mixed Tamil and Malayalam. The comments were gathered around EDI-related topics such as LGBTIQ issues, COVID-19, women in STEM and Black Lives Matter. Each comment had to be labelled as hope speech, non-hope speech, or not in the target language. Systems were ranked by weighted F1 on a held-out test set through CodaLab. About thirty teams submitted per language, and the best entries mostly fine-tuned XLM-RoBERTa (or RoBERTa/BERT for English). Top scores were 0.93 for English, 0.85 for Malayalam and only 0.61 for Tamil. The authors link the Tamil gap to code-mixing and a more balanced, truly three-way label distribution. The task leaves an open benchmark for positive-content detection in under-resourced languages.

## Key points
- HopeEDI has 59,354 comments: 28,451 English, 20,198 Tamil and 10,705 Malayalam. Data were split 80/10/10 into train, development and test sets.
- Each comment was labelled by at least three annotators who were briefed on EDI. Krippendorff's alpha was 0.63 (English), 0.76 (Tamil) and 0.85 (Malayalam).
- Classes are skewed in English and Malayalam:
  - English has 2,484 hope vs 25,940 non-hope, with only 27 other-language comments.
  - Tamil is closer to balanced, with 7,899 hope, 9,816 non-hope and 2,483 not-Tamil comments.
- 137 participants registered, and roughly 30–31 teams per language submitted final runs. The official metric was weighted F1, with sklearn's classification report recommended for checking.
- Score ranges were 0.61–0.93 for English, 0.49–0.85 for Malayalam and 0.37–0.61 for Tamil. Baselines were Tf-Idf features with standard sklearn classifiers (SVM, KNN, decision tree, logistic regression, multinomial Naive Bayes).

## Interesting ideas
- **Positive framing of a moderation problem.** Instead of only flagging harmful content, the task identifies content worth amplifying. The same reframing could work for other moderation or recommendation tasks where pure filtering risks silencing dialects or minority voices.
- **An explicit "not target language" class.** Off-language comments get their own label rather than being silently dropped. This makes language mixing a measurable part of the problem, which is useful for any corpus with heavy code-mixing or script switching.
- **Language ID first, then classification.** Some teams first predicted the language of a comment and only then the content label. This is a simple modular design for mixed-language data, where routing errors can be diagnosed separately from content errors.
- **Synthetic code-mixed training data.** One team generated artificial code-mixed text to pre-train a RoBERTa model from scratch for Tamil. This could help any low-resource setting where natural mixed-language text is scarce.
- **Explaining score gaps through label distribution.** The organisers note that English and Malayalam behave almost like binary tasks, while Tamil is genuinely three-way. Comparing class distributions before comparing scores across languages helps avoid misleading conclusions about model quality.

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

Fields: [Natural language processing](https://md.research.elamur.ai/free/en/shelves/nlp/) · [Society & ethics](https://md.research.elamur.ai/free/en/shelves/society/)

Topics: [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [🛡️ Bias, fairness & safety](https://md.research.elamur.ai/free/en/topics/bias-fairness-safety/)

## Cite

Cite as: Bharathi Raja Chakravarthi and Vigneshwaran Muralidaran. 2021. Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion. Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion.

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

```bibtex
@inproceedings{chakravarthi2021findings,
  title = {{Findings of the Shared Task on Hope Speech Detection for Equality, Diversity, and Inclusion}},
  author = {Bharathi Raja Chakravarthi and Vigneshwaran Muralidaran},
  booktitle = {Proceedings of the First Workshop on Language Technology for Equality, Diversity and Inclusion},
  year = {2021},
  url = {https://aclanthology.org/2021.ltedi-1.8/}
}
```

- [← previous SemEval-2021 Task 5: Toxic Spans Detection](https://md.research.elamur.ai/free/en/papers/pavlopoulos-2021-semeval-2021-task-5-toxic/)
- [next → P-Stance: A Large Dataset for Stance Detection in Political Domain](https://md.research.elamur.ai/free/en/papers/li-2021-p-stance-large-dataset-stance/)
- [↑ back to papers of 2021](https://md.research.elamur.ai/free/en/papers/2021/page/3/)

## References

41 references, 3 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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5. Nikhil Kumar Ghanghor, Parameswari Krishnamurthy, Sajeetha Thavareesan, Ruba Priyadharshini, and Bharathi Raja Chakravarthi. 2021a. IIITK@DravidianLangTech-EACL2021: Offensive Language Identification and Meme Classification in Tamil, Malayalam and Kannada. In Proceedings of the First Workshop on Spe… [find](https://scholar.google.com/scholar?q=Nikhil+Kumar+Ghanghor%2C+Parameswari+Krishnamurthy%2C+Sajeetha+Thavareesan%2C+Ruba+Priyadharshini%2C+and+Bharathi+Raja+Chakravarthi%2E+2021a%2E+IIITK%40DravidianLangTech%2DEACL2021%3A+Offensive+Language+Identification+)
6. Bharathi Raja Chakravarthi. 2020a. HopeEDI: A multilingual hope speech detection dataset for equality, diversity, and inclusion. In Proceedings of the Third Workshop on Computational Modeling of People’s Opinions, Personality, and Emotion’s in Social Media, pages 41–53, Barcelona, Spain (Online). As… [our card](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-hopeedi-multilingual-hope-speech-detection/)
7. Nikhil Kumar Ghanghor, Rahul Ponnusamy, Prasanna Kumar Kumaresan, Ruba Priyadharshini, Sajeetha Thavareesan, and Bharathi Raja Chakravarthi. 2021b. IIITK@LT-EDI-EACL2021: Hope Speech Detection for Equality, Diversity, and Inclusion in Tamil, Malayalam and English. In Proceedings of the First Worksho… [find](https://scholar.google.com/scholar?q=Nikhil+Kumar+Ghanghor%2C+Rahul+Ponnusamy%2C+Prasanna+Kumar+Kumaresan%2C+Ruba+Priyadharshini%2C+Sajeetha+Thavareesan%2C+and+Bharathi+Raja+Chakravarthi%2E+2021b%2E+IIITK%40LT%2DEDI%2DEACL2021%3A+Hope+Speech+Detection+for+Equ)
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9. Bharathi Raja Chakravarthi, Navya Jose, Shardul Suryawanshi, Elizabeth Sherly, and John P Mc-Crae. 2020. A sentiment analysis dataset for code-mixed Malayalam-English. In Proceedings of the 1st Joint Workshop of SLTU (Spoken Language Technologies for Under-resourced languages) and CCURL (Collaborati… [our card](https://md.research.elamur.ai/free/en/papers/chakravarthi-2020-sentiment-analysis-dataset-code-mixed/)
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