Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

Poddar, Soham; Mondal, Mainack; Misra, Janardan; Ganguly, Niloy; Ghosh, Saptarshi

doi:10.1609/icwsm.v16i1.19334

Cited by 24 publications

(13 citation statements)

References 48 publications

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“…With the onset of the COVID-19 pandemic, extensive work was done in the area of COVID-19 vaccinations. Some studies focused on the demographics of the vaccination-hesitant population [16,17], whereas others tried to understand what leads to this hesitancy [18,19], finding, similar to our study, that common concerns are potential side effects and distrust toward the pharmaceutical industry. Melton et al [20] used an approach similar to that of ours but focused on the social media platform Reddit (Reddit Inc).…”

Section: Vaccination Discourse On Social Mediasupporting

confidence: 80%

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Lindelöf¹,

Aledavood²,

Keller³

2023

J Med Internet Res

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Background Since the onset of the COVID-19 pandemic, vaccines have been an important topic in public discourse. The discussions around vaccines are polarized, as some see them as an important measure to end the pandemic, and others are hesitant or find them harmful. A substantial portion of these discussions occurs openly on social media platforms. This allows us to closely monitor the opinions of different groups and their changes over time. Objective This study investigated posts related to COVID-19 vaccines on Twitter (Twitter Inc) and focused on those that had a negative stance toward vaccines. It examined the evolution of the percentage of negative tweets over time. It also examined the different topics discussed in these tweets to understand the concerns and discussion points of those holding a negative stance toward the vaccines. Methods A data set of 16,713,238 English tweets related to COVID-19 vaccines was collected, covering the period from March 1, 2020, to July 31, 2021. We used the scikit-learn Python library to apply a support vector machine classifier to identify the tweets with a negative stance toward COVID-19 vaccines. A total of 5163 tweets were used to train the classifier, of which a subset of 2484 tweets was manually annotated by us and made publicly available along with this paper. We used the BERTopic model to extract the topics discussed within the negative tweets and investigate them, including how they changed over time. Results We showed that the negativity with respect to COVID-19 vaccines has decreased over time along with the vaccine rollouts. We identified 37 topics of discussion and presented their respective importance over time. We showed that popular topics not only consisted of conspiratorial discussions, such as 5G towers and microchips, but also contained legitimate concerns around vaccination safety and side effects as well as concerns about policies. The most prevalent topic among vaccine-hesitant tweets was related to the use of messenger RNA and fears about its speculated negative effects on our DNA. Conclusions Hesitancy toward vaccines existed before the COVID-19 pandemic. However, given the dimension of and circumstances surrounding the COVID-19 pandemic, some new areas of hesitancy and negativity toward COVID-19 vaccines have arisen, for example, whether there has been enough time for them to be properly tested. There is also an unprecedented number of conspiracy theories associated with them. Our study shows that even unpopular opinions or conspiracy theories can become widespread when paired with a widely popular discussion topic such as COVID-19 vaccines. Understanding the concerns, the discussed topics, and how they change over time is essential for policy makers and public health authorities to provide better in-time information and policies to facilitate the vaccination of the population in future similar crises.

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Section: Vaccination Discourse On Social Mediasupporting

confidence: 80%

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Lindelöf¹,

Aledavood²,

Keller³

2023

J Med Internet Res

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“…Trust and safety-related topics, for example, side effects, rushed vaccine, featured prominently in the antivax tweets, which supports other work that identified safety and trust (in institutions and governments) as a key hurdle in addressing vaccine hesitancy [18,20,23]. Our stance detection approach also enabled the definitive identification of a set of dual-stance users who contributed a significant volume to both antivax and provax tweets, supporting existing findings [4,26,[119][120][121]133].…”

Section: Comparison With Prior Worksupporting

confidence: 81%

“…A growing body of literature has analyzed social media posts, particularly tweets related to COVID-19 vaccines. Here, we compared our work with 29 significant Twitter studies [2][3][4][5][6][7]18,20,21,[23][24][25][26][119][120][121][122][123][124][125][126][127][128][129][130][131][132][133][134]. Unsupervised methods, such as sentiment analysis and topic modeling, are the most popular methods used to classify and analyze tweets; 11 studies used some combination of sentiment analysis, emotion analysis, and topic modeling [3,6,20,21,24,124,126,128,130,131,134].…”

Section: Comparison With Prior Workmentioning

confidence: 99%

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

Zaidi¹,

Ye²,

Samon³

et al. 2023

J Med Internet Res

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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.

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“…• News classification: Ag's News [61], SemEval-19 [29], Sarcasm news headline dataset [42], and Clickbait news headline dataset [8]. • Stance detection: SemEval-16 [43], P-Stance [34], Vax-Stance [47],…”

Section: Task Domains and Datasetsmentioning

confidence: 99%

APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT

Zhu,

Yin,

Tyson

et al. 2024

Proceedings of the ACM Web Conference 2024

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Recent research has highlighted the potential of LLMs, like Chat-GPT, for performing label annotation on social computing text. However, it is already well known that performance hinges on the quality of the input prompts. To address this, there has been a flurry of research into prompt tuning -techniques and guidelines that attempt to improve the quality of prompts. Yet these largely rely on manual effort and prior knowledge of the dataset being annotated. To address this limitation, we propose APT-Pipe, an automated prompt-tuning pipeline. APT-Pipe aims to automatically tune prompts to enhance ChatGPT's text classification performance on any given dataset. We implement APT-Pipe and test it across twelve distinct text classification datasets. We find that prompts tuned by APT-Pipe help ChatGPT achieve higher weighted F1-score on nine out of twelve experimented datasets, with an improvement of 7.01% on average. We further highlight APT-Pipe's flexibility as a framework by showing how it can be extended to support additional tuning mechanisms. CCS CONCEPTS• Computing methodologies → Natural language processing.

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Winds of Change: Impact of COVID-19 on Vaccine-Related Opinions of Twitter Users

Cited by 24 publications

References 48 publications

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Dynamics of the Negative Discourse Toward COVID-19 Vaccines: Topic Modeling Study and an Annotated Data Set of Twitter Posts

Topics in Antivax and Provax Discourse: Yearlong Synoptic Study of COVID-19 Vaccine Tweets

APT-Pipe: A Prompt-Tuning Tool for Social Data Annotation using ChatGPT

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