Semantic Scholar Open Access 2022 9 sitasi

Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining

Haomin Lin P. Nalluri Lantian Li Yifan Sun Yongjun Zhang

Lihat Sumber DOI

Abstrak

COVID-19 has disproportionately threatened minority communities in the U.S, not only in health but also in societal impact. However, social scientists and policymakers lack critical data to capture the dynamics of the anti-Asian hate trend and to evaluate its scale and scope. We introduce new datasets from Twitter related to anti-Asian hate sentiment before and during the pandemic. Relying on Twitter’s academic API, we retrieve hateful and counter-hate tweets from the Twitter Historical Database. To build contextual understanding and collect related racial cues, we also collect instances of heated arguments, often political, but not necessarily hateful, discussing Chinese issues. We then use the state-of-the-art hate speech classifiers to discern whether these tweets express hatred. These datasets can be used to study hate speech, general anti-Asian or Chinese sentiment, and hate linguistics by social scientists as well as to evaluate and build hate speech or sentiment analysis classifiers by computational scholars.

Topik & Kata Kunci

Computer Science

Penulis (5)

Haomin Lin

P. Nalluri

Lantian Li

Yifan Sun

Yongjun Zhang

Format Sitasi

APA MLA BibTeX

Lin, H., Nalluri, P., Li, L., Sun, Y., Zhang, Y. (2022). Multiplex Anti-Asian Sentiment before and during the Pandemic: Introducing New Datasets from Twitter Mining. https://doi.org/10.18653/v1/2022.wassa-1.2

Akses Cepat

Lihat di Sumber doi.org/10.18653/v1/2022.wassa-1.2

Informasi Jurnal

Tahun Terbit: 2022
Bahasa: en
Total Sitasi: 9×
Sumber Database: Semantic Scholar
DOI: 10.18653/v1/2022.wassa-1.2
Akses: Open Access ✓