News on Twitter: Engagement, Exposure and Estimating Credibility using Machine Learning

ElFadl, Asma Hamid; Shah, Uzair; Rehman, Sadaqat Ur; Ali, Raian; Shah, Zubair

doi:10.1109/besc53957.2021.9635549

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…The analysis of information on Twitter can provide valuable insights into public opinion [2]. Many studies have demonstrated the extraction of valuable information from Twitter data analysis [3], [4]. However, manual evaluation of the information on Twitter is a resource-intensive task as identified by previous studies too [5].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence-Based Models for Predicting Vaccines Critical Tweets: An Experimental Study

Shah

Ali

Alam

et al. 2022

Studies in Health Technology and Informatics

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We studied the suitability of Artificial Intelligence (AI)-based models to predict vaccine-critical tweets on the social media platform Twitter. We manually labeled a sample of 800 tweets as either “vaccine-critical” (i.e, anti-vaccine tweets, mentioned concerns related to vaccine safety and efficacy, and are against vaccine mandates or vaccine passports) or “other” (i.e., tweets that are neutral, report news, or are ambiguous) and used them to train and test AI-based models for automatically predicting vaccine-critical tweets. We fine-tuned two pre-trained deep learning-based language models, BERT and BERTweet, and implemented four classical AI-based models, Random Forest, Logistics Regression, Linear Support Vector Machines, and Multinomial Naïve Bayes. We evaluated these AI-based models using f1 score, accuracy, precision, and recall in three-fold cross-validation. We found that BERTweet outperformed all other models using these measures.

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