# SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis

arXiv: https://arxiv.org/abs/2210.01108 · HTML: https://mlsanity.com/paper/2210.01108/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 51 references that are in the map.
- First posted on arXiv 3 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [FastText.zip: Compressing text classification models](https://mlsanity.com/paper/1612.03651/) (2016)
- [Quantifying Intimacy in Language](https://mlsanity.com/paper/2011.03020/) (2020)

## Built on by (most cited first, 14 of 14)

- [POTATO: The Portable Text Annotation Tool](https://mlsanity.com/paper/2212.08620/) (2022)
- [Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://mlsanity.com/paper/2502.20897/) (2025)
- [SuperTweetEval: A Challenging, Unified and Heterogeneous Benchmark for Social Media NLP Research](https://mlsanity.com/paper/2310.14757/) (2023)
- [UZH_CLyp at SemEval-2023 Task 9: Head-First Fine-Tuning and ChatGPT Data Generation for Cross-Lingual Learning in Tweet Intimacy Prediction](https://mlsanity.com/paper/2303.01194/) (2023)
- [Towards Style Alignment in Cross-Cultural Translation](https://mlsanity.com/paper/2507.00216/) (2025)
- [FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation](https://mlsanity.com/paper/2504.17311/) (2025)
- [Targeted Distillation for Sentiment Analysis](https://mlsanity.com/paper/2503.03225/) (2025)
- [HULAT at SemEval-2023 Task 9: Data augmentation for pre-trained transformers applied to Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2302.12794/) (2023)
- [WADER at SemEval-2023 Task 9: A Weak-labelling framework for Data augmentation in tExt Regression Tasks](https://mlsanity.com/paper/2303.02758/) (2023)
- [tmn at SemEval-2023 Task 9: Multilingual Tweet Intimacy Detection using XLM-T, Google Translate, and Ensemble Learning](https://mlsanity.com/paper/2304.04054/) (2023)
- [OPI at SemEval 2023 Task 9: A Simple But Effective Approach to Multilingual Tweet Intimacy Analysis](https://mlsanity.com/paper/2304.07130/) (2023)
- [Text Intimacy Analysis using Ensembles of Multilingual Transformers](https://mlsanity.com/paper/2312.02590/) (2023)
- [LLM-as-a-Demographic: Whom Sociodemographic Prompting Helps, and Whom It Hurts](https://mlsanity.com/paper/2609.00222/) (2026)
- [FineWeb-CLaR: Culture, Language, and Region Annotations for Benchmark-Aligned Corpus Auditing](https://mlsanity.com/paper/2609.25298/) (2026)

## Abstract (arXiv)

We propose MINT, a new Multilingual INTimacy analysis dataset covering 13,372 tweets in 10 languages including English, French, Spanish, Italian, Portuguese, Korean, Dutch, Chinese, Hindi, and Arabic. We benchmarked a list of popular multilingual pre-trained language models. The dataset is released along with the SemEval 2023 Task 9: Multilingual Tweet Intimacy Analysis (https://sites.google.com/umich.edu/semeval-2023-tweet-intimacy).