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.
Abstract. Real-world things are increasingly becoming fully qualified members of the Web. From, pacemakers and medical records to children's toys and sneakers, things are connected over the Web and publish information that is available for the whole world to see. It is crucial that there is secure access to this Web of Things (WoT) and to the related information published by things on the Web. In this paper, we introduce an architecture that encompasses Web-enabled things in a secure and scalable manner. Our architecture utilizes the features of the well-known role-based access control (RBAC) to specify the access control policies to the WoT, and we use cryptographic keys to enforce such policies. This approach enables prescribers to WoT services to control who can access what things and how access can continue or should terminate, thereby enabling privacy and security of large amount of data that these things are poised to flood the future Web with.
Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.
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