---
language: en
license: cc-by-4.0
datasets:
- multi_nli
library_name: transformers
pipeline_tag: text-classification
---

# Model Card for Model COVID-19-CT-tweets-classification

### Model Description

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This is a  DeBERTa-v3-base-tasksource-nli model with an adapter trained on [More Information Needed], which contains X pairs of a tweet and a conspiracy theory along with class labels: support, deny, neutral. The model was finetuned for text classification to predict whether a tweet supports a given conspiracy theory or not. The model was trained on tweets related to six common COVID-19 conspiracy theories.

1. **Vaccines are unsafe.** The coronavirus vaccine is either unsafe or part of a larger plot to control people or reduce the population. 

2. **Governments and politicians spread misinformation.** Politicians or government agencies are intentionally spreading false information, or they have some other motive for the way they are responding to the coronavirus. 

3. **The Chinese intentionally spread the virus.** The Chinese government intentionally created or spread the coronavirus to harm other countries. 

4. **Deliberate strategy to create economic instability or benefit large corporations.** The coronavirus or the government's response to it is a deliberate strategy to create economic instability or to benefit large corporations over small businesses. 

5. **Public was intentionally misled about the true nature of the virus and prevention.** The public is being intentionally misled about the true nature of the Coronavirus, its risks, or the efficacy of certain treatments or prevention methods. 

6. **Human made and bioweapon.** The Coronavirus was created intentionally, made by humans, or as a bioweapon. 


This model is suitable for English only.

- **Developed by:** Webimmunication Team
- **Shared by [optional]:** @ikrysinska
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** EN
- **License:** CC BY 4.0
- **Finetuned from model [optional]:** https://huggingface.co/sileod/deberta-v3-base-tasksource-nli 

### Model Sources

- **Paper:** [More Information Needed]

- ## Uses

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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### Training Procedure 

The adapter was trained for 5 epochs with a batch size of 16. 

#### Preprocessing

The training data was cleaned before the training. All URLs, Twitter user mentions, and non-ASCII characters were removed. 

## Evaluation

The model was evaluated on a sample of the tweets collected during the COVID-19 pandemic. All the tweets were rated against each of the six theories by five annotators. Using sliding scales, they rated each tweets' endorsement likelihood for the respective conspiracy theory from 0% to 100%. The consensus among raters was substantial for every conspiracy theory. Comparisons with human evaluations revealed substantial correlations. The model significantly surpasses the performance of the pre-trained model without the finetuned adapter (see table below). 


| Conspiracy Theory  | Correlations between human raters  | Correlation between human ratings and model without adapter  | Correlation between human ratings and model with finetuned adapter |
|---|---|---|---|
| **Vaccines are unsafe.** | 0.78 | 0.29 | 0.57 |
| **Governments and politicians spread misinformation.**  | 0.58 | 0.32 | 0.72 | 
| **The Chinese intentionally spread the virus.**  |  0.62  | 0.53  |  0.64  |
| **Deliberate strategy to create economic instability or benefit large corporations.** | 0.56 | 0.33 | 0.54 |
| **Public was intentionally misled about the true nature of the virus and prevention.** | 0.66 | 0.37 | 0.68 | 
| **Human made and bioweapon.** | 0.67 | 0.15 | .78 |



## Environmental Impact

Carbon emissions are estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** GPU Tesla V100
- **Hours used:** 40
- **Cloud Provider:** Google Cloud Platform
- **Compute Region:** us-east1
- **Carbon Emitted:** 4.44 kg CO2 eq ([equivalent to: 17.9 km driven by an average ICE car, 2.22 kgs of coal burned, 0.07 tree seedlings sequesting carbon for 10 years](https://www.epa.gov/energy/greenhouse-gases-equivalencies-calculator-calculations-and-references)


## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Model Card Authors

@ikrysinska, @wtomi

## Model Card Contact

izabela.krysinska@doctorate.put.poznan.pl

tomi.wojtowicz@doctorate.put.poznan.pl

mikolaj.morzy@put.poznan.pl