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Author Spotlight: Advancements in Multiplex Detection of Respiratory Viruses

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Development of a COVID-19-Related Anti-Asian Tweet Data Set: Quantitative Study.

Maryam Mokhberi¹, Ahana Biswas², Zarif Masud¹

¹Department of Computer Science, University of Toronto, Toronto, ON, Canada.

JMIR Formative Research

|January 24, 2023

Summary

This study introduces a manually labeled dataset of tweets to combat anti-Asian stigma during COVID-19. The dataset aids in developing algorithms to detect and reduce online hate speech against marginalized groups.

Keywords:

Asian BERT COVID-19 Sinophobia Twitter annotation classification community data data set discrimination hate speech online pandemic research stigma

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Area of Science:

Computational Social Science
Natural Language Processing
Data Science

Background:

COVID-19 pandemic fueled anti-Asian stigma and hate speech on social media platforms like Twitter.
High-quality datasets are crucial for developing effective detection techniques for online discrimination.
Individuals of Asian descent have been disproportionately targeted with unfair attacks online.

Purpose of the Study:

To introduce a manually labeled dataset of tweets containing anti-Asian stigmatizing content.
To provide a benchmark for research on online stigma and hate speech.
To facilitate the development of algorithms for detecting and mitigating anti-Asian sentiment.

Main Methods:

Sampled over 668 million tweets from January to July 2020.
Employed a 3-stage, algorithm-driven data selection process.
Utilized manual annotation by volunteers to create high-quality labeled datasets (v3.0 and v3.1).

Main Results:

Developed two datasets: v3.0 (11,263 tweets) and v3.1 (4,998 tweets) with detailed labels and subtopics.
Preliminary experiments showed Bidirectional Encoder Representations from Transformers (BERT) achieved 79% accuracy in stigma detection.
Support vector machine (SVM) achieved 73% accuracy, demonstrating the dataset's utility for model benchmarking.

Conclusions:

The dataset serves as a vital benchmark for qualitative and quantitative research on anti-Asian stigma.
Findings reaffirm the prevalence of discrimination against Asian populations worldwide.
The dataset will assist researchers in analyzing the roots of anti-Asian stigma and developing interventions to reduce hate speech during crises.