Hydrating Large-Scale Coronavirus Pandemic Tweets: A Review of Software for Transportation Research

Arafat, Mahmoud; Morshed, Syed Ahnaf; Ali, MD Sultan; Raihan, Md. Abu

doi:10.1061/9780784483541.033

Cited by 2 publications

(1 citation statement)

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“…We collect all publicly available Twitter posts in Brazil, Indonesia, and Nigeria that contain vaccinerelated terms from January 2020 to December 2022 (N = 18,809,231). We use the hydration process (Arafat et al, 2021) where we use Brandwatch API to scrape IDs of tweets that contained predefined vaccine-related terms, then apply Twitter API to retrieve tweet contents.…”

Section: Datasetmentioning

confidence: 99%

COVID-19 Vaccine Misinformation in Middle Income Countries

Kim,

Bak,

Agrawal

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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This paper introduces a multilingual dataset of COVID-19 vaccine misinformation, consisting of annotated tweets from three middle-income countries: Brazil, Indonesia, and Nigeria. The expertly curated dataset includes annotations for 5,952 tweets, assessing their relevance to COVID-19 vaccines, presence of misinformation, and the themes of the misinformation. To address challenges posed by domain specificity, the low-resource setting, and data imbalance, we adopt two approaches for developing COVID-19 vaccine misinformation detection models: domain-specific pre-training and text augmentation using a large language model. Our best misinformation detection models demonstrate improvements ranging from 2.7 to 15.9 percentage points in macro F1-score compared to the baseline models. Additionally, we apply our misinformation detection models in a large-scale study of 19 million unlabeled tweets from the three countries between 2020 and 2022, showcasing the practical application of our dataset and models for detecting and analyzing vaccine misinformation in multiple countries and languages. Our analysis indicates that percentage changes in the number of new COVID-19 cases are positively associated with COVID-19 vaccine misinformation rates in a staggered manner for Brazil and Indonesia, and there are significant positive associations between the misinformation rates across the three countries.

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Section: Datasetmentioning

confidence: 99%