Topics in Antivax and Provax Discourse : Yearlong Synoptic Study of COVID-19 Vaccine Tweets
©Zainab Zaidi, Mengbin Ye, Fergus Samon, Abdisalan Jama, Binduja Gopalakrishnan, Chenhao Gu, Shanika Karunasekera, Jamie Evans, Yoshihisa Kashima. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.08.2023..
BACKGROUND: Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines.
OBJECTIVE: This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets.
METHODS: We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques.
RESULTS: Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period.
CONCLUSIONS: This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine-related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
|---|---|
| Enthalten in: |
Journal of medical Internet research - 25(2023) vom: 08. Aug., Seite e45069 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zaidi, Zainab [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Antivax |
|---|
| Anmerkungen: |
Date Completed 09.08.2023 Date Revised 11.08.2023 published: Electronic Citation Status MEDLINE |
|---|
| doi: |
10.2196/45069 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM360521789 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM360521789 | ||
| 003 | DE-627 | ||
| 005 | 20231226083206.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.2196/45069 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1201.xml |
| 035 | |a (DE-627)NLM360521789 | ||
| 035 | |a (NLM)37552535 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zaidi, Zainab |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Topics in Antivax and Provax Discourse |b Yearlong Synoptic Study of COVID-19 Vaccine Tweets |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 09.08.2023 | ||
| 500 | |a Date Revised 11.08.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Zainab Zaidi, Mengbin Ye, Fergus Samon, Abdisalan Jama, Binduja Gopalakrishnan, Chenhao Gu, Shanika Karunasekera, Jamie Evans, Yoshihisa Kashima. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.08.2023. | ||
| 520 | |a BACKGROUND: Developing an understanding of the public discourse on COVID-19 vaccination on social media is important not only for addressing the ongoing COVID-19 pandemic but also for future pathogen outbreaks. There are various research efforts in this domain, although, a need still exists for a comprehensive topic-wise analysis of tweets in favor of and against COVID-19 vaccines | ||
| 520 | |a OBJECTIVE: This study characterizes the discussion points in favor of and against COVID-19 vaccines posted on Twitter during the first year of the pandemic. The aim of this study was primarily to contrast the views expressed by both camps, their respective activity patterns, and their correlation with vaccine-related events. A further aim was to gauge the genuineness of the concerns expressed in antivax tweets | ||
| 520 | |a METHODS: We examined a Twitter data set containing 75 million English tweets discussing the COVID-19 vaccination from March 2020 to March 2021. We trained a stance detection algorithm using natural language processing techniques to classify tweets as antivax or provax and examined the main topics of discourse using topic modeling techniques | ||
| 520 | |a RESULTS: Provax tweets (37 million) far outnumbered antivax tweets (10 million) and focused mostly on vaccine development, whereas antivax tweets covered a wide range of topics, including opposition to vaccine mandate and concerns about safety. Although some antivax tweets included genuine concerns, there was a large amount of falsehood. Both stances discussed many of the same topics from opposite viewpoints. Memes and jokes were among the most retweeted messages. Most tweets from both stances (9,007,481/10,566,679, 85.24% antivax and 24,463,708/37,044,507, 66.03% provax tweets) came from dual-stance users who posted both provax and antivax tweets during the observation period | ||
| 520 | |a CONCLUSIONS: This study is a comprehensive account of COVID-19 vaccine discourse in the English language on Twitter from March 2020 to March 2021. The broad range of discussion points covered almost the entire conversation, and their temporal dynamics revealed a significant correlation with COVID-19 vaccine-related events. We did not find any evidence of polarization and prevalence of antivax discourse over Twitter. However, targeted countering of falsehoods is important because only a small fraction of antivax discourse touched on a genuine issue. Future research should examine the role of memes and humor in driving web-based social media activity | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 vaccine | |
| 650 | 4 | |a antivax | |
| 650 | 4 | |a disinformation | |
| 650 | 4 | |a misinformation | |
| 650 | 4 | |a stance detection | |
| 650 | 4 | |a topic modeling | |
| 650 | 4 | |a vaccine hesitancy | |
| 650 | 7 | |a COVID-19 Vaccines |2 NLM | |
| 650 | 7 | |a Vaccines |2 NLM | |
| 700 | 1 | |a Ye, Mengbin |e verfasserin |4 aut | |
| 700 | 1 | |a Samon, Fergus |e verfasserin |4 aut | |
| 700 | 1 | |a Jama, Abdisalan |e verfasserin |4 aut | |
| 700 | 1 | |a Gopalakrishnan, Binduja |e verfasserin |4 aut | |
| 700 | 1 | |a Gu, Chenhao |e verfasserin |4 aut | |
| 700 | 1 | |a Karunasekera, Shanika |e verfasserin |4 aut | |
| 700 | 1 | |a Evans, Jamie |e verfasserin |4 aut | |
| 700 | 1 | |a Kashima, Yoshihisa |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of medical Internet research |d 1999 |g 25(2023) vom: 08. Aug., Seite e45069 |w (DE-627)NLM116127104 |x 1438-8871 |7 nnns |
| 773 | 1 | 8 | |g volume:25 |g year:2023 |g day:08 |g month:08 |g pages:e45069 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.2196/45069 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 25 |j 2023 |b 08 |c 08 |h e45069 | ||