metadata
language: en
license: apache-2.0
datasets:
- tweets
widget:
- text: COVID-19 vaccines are safe and effective.
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Vaccinating COVID tweets
- A part of MDLD for DS class at SNU
Fine-tuned model on English language using a masked language modeling (MLM) objective from BERTweet in this repository for the classification task for false/misleading information about COVID-19 vaccines.
Model description
- Baseline model: BERTweet14,15
- trained based on the RoBERTa pre-training procedure
- 850M General English Tweets (Jan 2012 ~ Aug 2019)
- 23M COVID-19 English Tweets
- Size of the model: >134M parameters
- Further training
- Training with recent COVID-19 and vaccine tweets
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Intended uses & limitations
How to use
# You can include sample code which will be formatted
Limitations and bias
Provide examples of latent issues and potential remediations.
Training data
1) Pre-training language model
- Tweets with trending #CovidVaccine hashtag 207,000 tweets uploaded across 2020-08-18 ~ 2021-04-20 [3]
- Tweets about all COVID-19 vaccines 78,000 tweets uploaded across 2020-12-20 ~ 2021-05-13 [4]
- Covid-19 Twitter chatter dataset 590,000 tweets uploaded across 2021-03-01 ~ 2021-05-20 [5]
2) Fine-tuning for fact classification
- Statements from Poynter and Snopes with Selenium 14,000 fact-checked statements from 2020-01-14 to 2021-05-13
- Divide original labels within 3 categories False: False, no evidence, manipulated, fake, not true, unproven, unverified Misleading: Misleading, exaggerated, out of context, needs context True: True, correct
Describe the data you used to train the model. If you initialized it with pre-trained weights, add a link to the pre-trained model card or repository with description of the pre-training data.
Training procedure
Preprocessing, hardware used, hyperparameters...
Eval results
BibTeX entry and citation info
@inproceedings{...,
year={2020}
}
Contributors
- Ahn, Hyunju
- An, Jiyong
- An, Seungchan
- Jeong, Seokho
- Kim, Jungmin
- Kim, Sangbeom