Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection

Martínez, Rubén Yáñez; Blanco, Guillermo; Lourenço, Anália

doi:10.1016/j.ipm.2023.103294

Cited by 10 publications

(8 citation statements)

References 36 publications

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“…We observed a notable class imbalance among the three identified stances or classes (negative, neutral and positive), similar to what has been found in previous studies [30,31]. The majority were neutral, followed by positive, with negative being the least common stance.…”

Section: Discussionsupporting

confidence: 88%

Use of large language models as a scalable approach to understanding public health discourse

Espinosa,

Salathé

2024

Preprint

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Online public health discourse is becoming more and more important in shaping public health dynamics. Large Language Models (LLMs) offer a scalable solution for analysing the vast amounts of unstructured text found on online platforms. Here, we explore the effectiveness of Large Language Models (LLMs), including GPT models and open-source alternatives, for extracting public stances towards vaccination from social media posts. Using an expert-annotated dataset of social media posts related to vaccination, we applied various LLMs and a rule-based sentiment analysis tool to classify the stance towards vaccination. We assessed the accuracy of these methods through comparisons with expert annotations and annotations obtained through crowdsourcing. Our results demonstrate that few-shot prompting of best-in-class LLMs are the best performing methods, and that all alternatives have significant risks of substantial misclassification. The study highlights the potential of LLMs as a scalable tool for public health professionals to quickly gauge public opinion on health policies and interventions, offering an efficient alternative to traditional data analysis methods. With the continuous advancement in LLM development, the integration of these models into public health surveillance systems could substantially improve our ability to monitor and respond to changing public health attitudes.

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Section: Discussionsupporting

confidence: 88%

Use of large language models as a scalable approach to understanding public health discourse

Espinosa,

Salathé

2024

Preprint

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“…Martínez et al. [27] introduce a newly annotated corpus of 2801 tweets related to COVID-19 vaccination, which was annotated by three native Spanish speakers as being in favor (904), against (674), or neutral (1223) with a Fleiss’ kappa score of 0.725.…”

Section: Related Workmentioning

confidence: 99%

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Canaparo¹,

Ronchieri²,

Scarso³

2023

Healthcare Analytics

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“…Therefore, efforts have been made to compile multilingual corpora [42,43] as well as language-specific datasets such as Portuguese [44,45], Arabic [46,47], French [48], among others [49][50][51]. For the Spanish language, there are annotated tweet datasets for tasks such as hate speech detection [52], aggression detection [53], LGBT-phobia detection [54], and automatic stance detection [55], among others. However, to our knowledge, there is no manually annotated public corpus for the sentiment polarity of COVID-19-related tweets in Spanish.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

Gomez-Adorno,

Bel-Enguix,

Sierra

et al. 2024

Informatics

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This article presents a comprehensive evaluation of traditional machine learning and deep learning models in analyzing sentiment trends within the SENT-COVID Twitter corpus, curated during the COVID-19 pandemic. The corpus, filtered by COVID-19 related keywords and manually annotated for polarity, is a pivotal resource for conducting sentiment analysis experiments. Our study investigates various approaches, including classic vector-based systems such as word2vec, doc2vec, and diverse phrase modeling techniques, alongside Spanish pre-trained BERT models. We assess the performance of readily available sentiment analysis libraries for Python users, including TextBlob, VADER, and Pysentimiento. Additionally, we implement and evaluate traditional classification algorithms such as Logistic Regression, Naive Bayes, Support Vector Machines, and simple neural networks like Multilayer Perceptron. Throughout the research, we explore different dimensionality reduction techniques. This methodology enables a precise comparison among classification methods, with BETO-uncased achieving the highest accuracy of 0.73 on the test set. Our findings underscore the efficacy and applicability of traditional machine learning and deep learning models in analyzing sentiment trends within the context of low-resource Spanish language scenarios and emerging topics like COVID-19.

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Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection

Cited by 10 publications

References 36 publications

Use of large language models as a scalable approach to understanding public health discourse

Use of large language models as a scalable approach to understanding public health discourse

A natural language processing approach for analyzing COVID-19 vaccination response in multi-language and geo-localized tweets

Machine Learning and Deep Learning Sentiment Analysis Models: Case Study on the SENT-COVID Corpus of Tweets in Mexican Spanish

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