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vaccinating-covid-tweets

Text Classification
Transformers
PyTorch
English
roberta
Inference Endpoints
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vaccinating-covid-tweets / README.md
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metadata
language: en
license: apache-2.0
datasets:
  - tweets
widget:
  - text: COVID-19 vaccines are safe and effective.

Disclaimer: This page is under maintenance. Please DO NOT refer to the information on this page to make any decision yet.

Vaccinating COVID tweets

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.

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: \\\\t\\\\tFalse, no evidence, manipulated, fake, not true, unproven, unverified Misleading: \\\\tMisleading, exaggerated, out of context, needs context True:\\\\t\\\\tTrue, 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

  • Baseline model: BERTweet
    • 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

Eval results

Contributors

  • This page is a part of final team project from MLDL for DS class at SNU
    • Team BIBI - Vaccinating COVID-NineTweets
    • Team members: Ahn, Hyunju; An, Jiyong; An, Seungchan; Jeong, Seokho; Kim, Jungmin; Kim, Sangbeom
    • Advisor: Prof. Wen-Syan Li

GSDS