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GleanEthiopian
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Gambella
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į‹µįˆ
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TigrayGenocide
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įˆ°į‰ įˆ­_į‹œįŠ“
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RejectHR6600
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FakeNewsAlert
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ColorsOfUnity
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FreeJawarMohammed
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share
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įŠ įˆœįˆŖįŠ«
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ItsMyDam
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įŠ¢į‰µį‹®įŒµį‹«
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į‰ įŠ¢į‰µį‹®įŒµį‹«
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įˆ°į‰ įˆ­_į‹œįŠ“
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įŠ¢į‰µį‹®įŒµį‹«_įŠ įˆøįŠ•į‹įˆˆį‰½
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no
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įŠ¦į‹«į‹«
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EthiopiaCheck
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TplfmustGo
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įŠ į‰¦įˆ
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UnityForEthio
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EPRDF
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OromoProtests
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įˆįˆ­įŒ«
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Ethiopia
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TigrayIsPrevailing
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įŠ įˆØįŠ•įŒ“į‹“įŠ įˆ»įˆ«
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į‰„įˆįŒ½įŒįŠ“
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į‰µįŒįˆ«į‹­
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NoNegotiationWithTplf
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StopTigraySiege
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įŠ¢į‰µį‹®įŒµį‹«į‹Š
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į‰ įŠ įˆ›įˆ«
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AmharaGenocide
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įŠ į‹²įˆµįŠ į‰ į‰£
1,520,100,516,927,357,000
Prosperity
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į‹ØįŠ¢į‰µį‹®įŒµį‹«įŠ•
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UnityForEthiopia
1,416,059,879,341,580,300
TigrayMassArrest
1,436,705,959,619,940,400
į‰µįŒįˆ«į‹­į‰µįˆµį‹•įˆ­
1,471,798,099,622,436,900
Tigray
1,465,425,624,827,318,300
LearnifyEdTech
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addisababa
1,431,948,415,466,885,000
EthiopiaPrevails
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TplfIsWarCriminal
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į‹ˆį‹«įŠ”
1,385,202,135,013,503,000
įŠ¦į‹«į‹«įˆ˜įˆį‰²įˆšį‹²į‹«
1,247,948,550,408,933,400
COVID19Ethiopia
1,511,746,011,089,318,000
RejectHR6600
1,512,242,800,481,419,300
SupportHR6600
821,438,982,609,367,000
EthiopianRevolution
1,384,372,438,327,234,600
AmharaGenocide
1,396,878,938,190,880,800
EritreaShinesAt30
901,090,343,977,975,800
VOAAmharic
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itsmydam

Hashtag Prediction Dataset from paper TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations

PRs Welcome arXiv Github

This repo contains the Hashtag prediction dataset from our paper TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations.
[arXiv] [HuggingFace Models] [Github repo]

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Download

Use the hashtag-classification-id.zip in this repo. Link.

Check the first-author's GitHub repo for any supplemental dataset material or code. Link

Dataset Description

The hashtag prediction dataset is a multilingual classification dataset. Separate datasets are given for different languages. We first select 500 (or all available) popular hashtags of each language and then sample 10k (or all available) popular Tweets that contain these hashtags. We make sure each Tweet will have exactly one of the selected hashtags.

The evaluation task is a multiclass classification task, with hashtags as labels. We remove the hashtag from the Tweet, and let the model predict the removed hashtag.

We provide Tweet ID and raw text hashtag labels in tsv files. For each language, we provide train, development, and test splits.

To use the dataset, you must hydrate the Tweet text with Twitter API, and remove the hashtag used for label from each Tweet .

The data format is displayed below.

ID label
1 hashtag
2 another hashtag

Citation

If you use our dataset in your work, please cite the following:

@article{zhang2022twhin,
  title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
  author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
  journal={arXiv preprint arXiv:2209.07562},
  year={2022}
}
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