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

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.

BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration.

The COVID-19 pandemic has introduced new norms, such as social distancing, face masks, quarantine, lockdowns, travel restrictions, work/study from home, and business closures, to name a few. The pandemic's seriousness has made people vocal on social media, especially on microblogs such as Twitter. Since the early days of the outbreak, researchers have been collecting and sharing large-scale datasets of COVID-19 tweets. However, the existing datasets carry issues related to proportion and redundancy. We report that more than 500 million tweet identifiers point to deleted or protected tweets. To address these issues, this paper introduces an enriched global billion-scale English-language COVID-19 tweets dataset, BillionCOV, which contains 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. Importantly, BillionCOV facilitates researchers to filter tweet identifiers for efficient hydration. We anticipate that the dataset of this scale with global scope and extended temporal coverage will aid in obtaining a thorough understanding of the pandemic's conversational dynamics.

Efficient Unsupervised Parameter Estimation for One-Class Support Vector Machines.

One-class support vector machines (OCSVMs) are very effective for semisupervised anomaly detection. However, their performance strongly depends on the settings of their hyperparameters, which has not been well studied. Moreover, unavailability of a clean training set that only comprises normal data in many real-life problems has given rise to the application of OCSVMs in an unsupervised manner. However, it has been shown that if the training set includes anomalies, the normal boundary created by OCSVMs is prone to skew toward the anomalies. This problem decreases the detection rate of anomalies and results in poor performance of the classifier. In this paper, we propose a new technique to set the hyperparameters and clean suspected anomalies from unlabelled training sets. The proposed method removes suspected anomalies based on a $K$ -nearest neighbors technique, which is then used to directly estimate the hyperparameters. We examine several benchmark data sets with diverse distributions and dimensionality. Our findings suggest that on the examined data sets, the proposed technique is roughly 70 times faster than supervised parameter estimation via grid-search and cross validation, and one to three orders of magnitude faster than broadly used semisupervised and unsupervised parameter estimation methods for OCSVMs. Moreover, our method statistically outperforms those semisupervised and unsupervised methods and its accuracy is comparable to supervised grid-search and cross validation.