التفاصيل البيبلوغرافية
| العنوان: |
Detecting Reported Side Effects of COVID-19 Vaccines From Arabic Twitter (X) Data |
| المؤلفون: |
Maram K. Alhumayani, Huda N. Alhazmi |
| المصدر: |
IEEE Access, Vol 12, Pp 55367-55388 (2024) |
| بيانات النشر: |
IEEE, 2024. |
| سنة النشر: |
2024 |
| المجموعة: |
LCC:Electrical engineering. Electronics. Nuclear engineering |
| مصطلحات موضوعية: |
Arabic language, biterm topic modeling (BTM), COVID-19 vaccine, machine learning, NLP, side effects, Electrical engineering. Electronics. Nuclear engineering, TK1-9971 |
| الوصف: |
Vaccines might potentially cause side effects as any other drugs, which needs to be investigated and analyzed to identify the public safety concerns. The massive vaccination rollout against COVID-19 provoked discussion among people through social media platforms. Twitter (X), a popular social media platform, plays a significant role in disseminating information about COVID-19 vaccines and monitoring people’s reports regarding vaccination side effects. The aim of this study is to mine Twitter (X) to identify self-reported side effects related to COVID-19 vaccines in Arabic language, compare their distribution among six vaccine types, and construct Arabic lexicon of symptoms. We collected the tweets posts in Arabic language after the distribution of COVID-19 vaccines, then we developed a workflow for identifying self-report symptoms using biterm topic modeling (BTM) and support vector machine (SVM) to extract the symptoms then cluster them in groups based on their co-occurrence. A total of 51 symptoms were extracted from 65,387 tweets that were reported 148,324 times. We performed a more in-depth analysis to investigate the symptoms that tend to occur simultaneously. The results show that the symptoms that more likely to occur together may indicate to a particular connection. The findings suggested that the social media conversation can provide a comprehensive depiction of symptoms that may complement what identified in clinical studies. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
2169-3536 |
| Relation: |
https://ieeexplore.ieee.org/document/10500705/; https://doaj.org/toc/2169-3536 |
| DOI: |
10.1109/ACCESS.2024.3389655 |
| URL الوصول: |
https://doaj.org/article/a9943c40ccbf42ee8a5dbb9e178e9bc8 |
| رقم الانضمام: |
edsdoj.9943c40ccbf42ee8a5dbb9e178e9bc8 |
| قاعدة البيانات: |
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