ANTi-Vax: a novel Twitter dataset for COVID-19 vaccine misinformation detection

Abstract

Objectives COVID-19 (SARS-CoV-2) pandemic has infected hundreds of millions and inflicted millions of deaths around the globe. Fortunately, the introduction of COVID vaccines provided a glimmer of hope and a pathway to recovery. However, due to misinformation being spread on social media and other platforms, there has been a rise in vaccine hesitancy which can lead to a negative impact on vaccine uptake in the population. The goal of this research is to introduce a novel machine learning-based COVID-19 vaccine misinformation detection framework. Study Design We collected and annotated COVID-19 vaccine tweets and trained machine learning algorithms to classify vaccine misinformation. Methods More than 15,000 tweets were annotated as misinformation or general vaccine tweets using reliable sources and validated by medical experts. The classification models explored were XGBoost, LSTM, and BERT transformer model. Results The best classification performance was obtained using BERT, resulting in 0.98 F1-score on the test set. The precision and recall scores were 0.97 and 0.98 respectively. Conclusion Machine learning-based models are effective in detecting misinformation regarding COVID-19 vaccines on social media platforms.

Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context

“…Hayawi et al [ 15 ] presented a unique COVID-19 vaccination misinformation detection framework based on machine learning (ML). They used ML techniques to classify vaccination misinformation after collecting and annotating COVID-19 vaccine tweets.…”

Classifying COVID-19 vaccine narratives

“…As the importance of understanding and tackling COVID-19 vaccination hesitancy grew, increasing efforts have been made to analyse vaccine narratives and discourses, dissemination of false claims and anti-vaccine groups on social media, resulting in the construction of a number of COVID-19 vaccine-focused datasets, without [11,12] or with annotations about veracity (e.g., true or false information) [13], sentiment (e.g., positive, negative or neutral) [14], stance (e.g., pro-or anti-vaccine) [15,16] or topic category (e.g., vaccine development or side effects) [4,17]. The datasets, consequently, can be used to facilitate the research on COVID-19 vaccine-related online information from different aspects, including fact-checking, sentiment analysis, stance detection, and topic analysis.…”

Inevitable-Metaverse: A Novel Twitter Dataset for Public Sentiments on Metaverse

“…It consists of a bidirectional LSTM layer with 1-dimensional global max-pooling operation and two dense layers with recti ed linear unit (ReLU) activation. We also utilized the Glove word embeddings [28] which convert the words into an n-dimensional space to obtain the semantic similarity between words [27]. To prevent over tting, we used the dropout layer [29] with a 0.5 rate after each LSTM and dense layers.…”