ArticleSocial network analysis and mining2022
Public reactions towards Covid-19 vaccination through twitter before and after second wave in India.
Article in Social network analysis and mining, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
4 citing papers in PubMed.
- A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets.Healthcare analytics (New York, N.Y.) · 2023Article
- Capturing Emerging Experiential Knowledge for Vaccination Guidelines Through Natural Language Processing: Proof-of-Concept Study.Journal of medical Internet research · 2023Article
- Automatically detecting and understanding the perception of COVID-19 vaccination: a middle east case study.Social network analysis and mining · 2022Article
- On the development of an information system for monitoring user opinion and its role for the public.Journal of big data · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Social media have a significant impact on opinion building in public. Vaccination in India started in January 2021. We have seen many opinions towards vaccination of the people, as vaccination is one of the most crucial steps toward the fight against COVID-19. In this paper, we have compared the public's sentiments towards COVID vaccination in India before the second wave and after the second wave. We worked by extracting tweets regarding vaccination in India, building our datasets. We extracted 5977 tweets before the second wave and 42,936 tweets after the second wave. We annotated the collected tweets into four categories, namely Provaccine, Antivaccine, Hesitant and Cognizant. We built a baseline model for sentiment analysis and have used multiple classification techniques among which Random Forest using the TF-IDF vectorization technique gave the best accuracy of 69% using max-features and n-estimators as parameters.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.