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Datasets:
cardiffnlp
/
tweet_topic_multilingual

Tasks:
Text Classification
Languages:
English
Japanese
Greek
Size:
1K<n<10K
ArXiv:
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metadata
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, 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.

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.

{
  "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

0: arts_&_culture 5: fashion_&_style 10: learning_&_educational 15: science_&_technology
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.

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.",
}