The popularity of contradictory information about COVID-19 vaccine on social media in China
To eliminate the impact of contradictory information on vaccine hesitancy on social media, this research developed a framework to compare the popularity of information expressing contradictory attitudes towards COVID-19 vaccine or vaccination, mine the similarities and differences among contradictor...
Main Authors: | , |
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Format: | Article |
Language: | English |
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Elsevier Ltd
2022
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Online Access: | View Fulltext in Publisher |
LEADER | 02597nam a2200373Ia 4500 | ||
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001 | 10.1016-j.chb.2022.107320 | ||
008 | 220517s2022 CNT 000 0 und d | ||
020 | |a 07475632 (ISSN) | ||
245 | 1 | 0 | |a The popularity of contradictory information about COVID-19 vaccine on social media in China |
260 | 0 | |b Elsevier Ltd |c 2022 | |
856 | |z View Fulltext in Publisher |u https://doi.org/10.1016/j.chb.2022.107320 | ||
520 | 3 | |a To eliminate the impact of contradictory information on vaccine hesitancy on social media, this research developed a framework to compare the popularity of information expressing contradictory attitudes towards COVID-19 vaccine or vaccination, mine the similarities and differences among contradictory information's characteristics, and determine which factors influenced the popularity mostly. We called Sina Weibo API to collect data. Firstly, to extract multi-dimensional features from original tweets and quantify their popularity, content analysis, sentiment computing and k-medoids clustering were used. Statistical analysis showed that anti-vaccine tweets were more popular than pro-vaccine tweets, but not significant. Then, by visualizing the features' centrality and clustering in information-feature networks, we found that there were differences in text characteristics, information display dimension, topic, sentiment, readability, posters' characteristics of the original tweets expressing different attitudes. Finally, we employed regression models and SHapley Additive exPlanations to explore and explain the relationship between tweets' popularity and content and contextual features. Suggestions for adjusting the organizational strategy of contradictory information to control its popularity from different dimensions, such as poster's influence, activity and identity, tweets' topic, sentiment, readability were proposed, to reduce vaccine hesitancy. © 2022 | |
650 | 0 | 4 | |a Attitude |
650 | 0 | 4 | |a Attitude |
650 | 0 | 4 | |a Content analysis |
650 | 0 | 4 | |a Content feature |
650 | 0 | 4 | |a Content feature |
650 | 0 | 4 | |a Contextual feature |
650 | 0 | 4 | |a Contextual feature |
650 | 0 | 4 | |a COVID-19 vaccine |
650 | 0 | 4 | |a COVID-19 vaccine |
650 | 0 | 4 | |a Information popularity |
650 | 0 | 4 | |a Information popularity |
650 | 0 | 4 | |a K-medoids clustering |
650 | 0 | 4 | |a Multi dimensional |
650 | 0 | 4 | |a Regression analysis |
650 | 0 | 4 | |a Sina-weibo |
650 | 0 | 4 | |a Social media |
650 | 0 | 4 | |a Social networking (online) |
650 | 0 | 4 | |a Vaccines |
650 | 0 | 4 | |a Weibo |
700 | 1 | |a Wang, D. |e author | |
700 | 1 | |a Zhou, Y. |e author | |
773 | |t Computers in Human Behavior |