README.md

---
configs:
- config_name: default
data_files:
- split: train_en
  path: "dataset/en/en_train.jsonl"
language:
  - en
  - ja
  - el
  - es
license:
  - other
multilinguality:
  - monolingual
size_categories:
  - 1K<n<10K
task_categories:
  - text-classification
pretty_name: xtopic
---

# Dataset Card for "cardiffnlp/tweet_topic_multilingual"

## Dataset Description
- **Dataset:** X-Topic
- **Domain:** X (Twitter)
- **Number of Class:** 19


### Dataset Summary
This is the official repository of X-Topic ([Multilingual Topic Classification in X: Dataset and Analysis](https://arxiv.org/abs/2410.03075), EMNLP 2024), a topic classification dataset based on X (formerly Twitter), featuring 19 topic labels.

The classification task is multi-label, with tweets available in four languages: English, Japanese, Spanish, and Greek.

The dataset comprises 4,000 tweets (1,000 per language), collected between September 2021 and August 2022.

The dataset uses the same taxonomy as [TweetTopic](https://huggingface.co/datasets/cardiffnlp/tweet_topic_multi). 


## Dataset Structure

### Data Splits

The dataset includes the following splits:

- **en**: English
- **es**: Spanish
- **ja**: Japanese
- **gr**: Greek
- **en_2022**: English data from 2022 (TweetTopic)
- **mix**: Mixed-language data
- **mix_2022**: Mixed-language data including (TweetTopic) from 2022
- **Cross-validation splits:**
  - **en_cross_validation_0** to **en_cross_validation_4**: English cross-validation splits
  - **es_cross_validation_0** to **es_cross_validation_4**: Spanish cross-validation splits
  - **ja_cross_validation_0** to **ja_cross_validation_4**: Japanese cross-validation splits
  - **gr_cross_validation_0** to **gr_cross_validation_4**: Greek cross-validation splits


### Data Instances
An example of `train` looks as follows.

```python
{
  "id": 1470030676816797696,
  "text": "made a matcha latte, black tea and green juice until i break my fast at 1!! my body and skin are thanking me",
  "label": [0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
  "label_name": ["Diaries & Daily Life", "Fitness & Health", "Food & Dining"],
  "label_name_flatten": "Diaries & Daily Life, Fitness & Health, Food & Dining"
}
```

### Labels
| <span style="font-weight:normal">0: arts_&_culture</span> | <span style="font-weight:normal">5: fashion_&_style</span> | <span style="font-weight:normal">10: learning_&_educational</span>  | <span style="font-weight:normal">15: science_&_technology</span>  |
|-----------------------------|---------------------|----------------------------|--------------------------|
| 1: business_&_entrepreneurs | 6: film_tv_&_video  | 11: music              	| 16: sports           	|
| 2: celebrity_&_pop_culture  | 7: fitness_&_health | 12: news_&_social_concern  | 17: travel_&_adventure   |
| 3: diaries_&_daily_life 	| 8: food_&_dining	| 13: other_hobbies      	| 18: youth_&_student_life |
| 4: family               	| 9: gaming       	| 14: relationships      	|                      	|

Annotation instructions for English can be found [here](https://docs.google.com/document/d/1IaIXZYof3iCLLxyBdu_koNmjy--zqsuOmxQ2vOxYd_g/edit?usp=sharing).



## Citation Information
```
@inproceedings{antypas-etal-2024-multilingual,
    title = "Multilingual Topic Classification in {X}: Dataset and Analysis",
    author = "Antypas, Dimosthenis  and
      Ushio, Asahi  and
      Barbieri, Francesco  and
      Camacho-Collados, Jose",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.1123",
    pages = "20136--20152",
    abstract = "In the dynamic realm of social media, diverse topics are discussed daily, transcending linguistic boundaries. However, the complexities of understanding and categorising this content across various languages remain an important challenge with traditional techniques like topic modelling often struggling to accommodate this multilingual diversity. In this paper, we introduce X-Topic, a multilingual dataset featuring content in four distinct languages (English, Spanish, Japanese, and Greek), crafted for the purpose of tweet topic classification. Our dataset includes a wide range of topics, tailored for social media content, making it a valuable resource for scientists and professionals working on cross-linguistic analysis, the development of robust multilingual models, and computational scientists studying online dialogue. Finally, we leverage X-Topic to perform a comprehensive cross-linguistic and multilingual analysis, and compare the capabilities of current general- and domain-specific language models.",
}
```