Social Media Monitoring of the COVID-19 Pandemic and Influenza Epidemic With Adaptation for Informal Language in Arabic Twitter Data: Qualitative Study

Abstract

Background Twitter is a real-time messaging platform widely used by people and organizations to share information on many topics. Systematic monitoring of social media posts (infodemiology or infoveillance) could be useful to detect misinformation outbreaks as well as to reduce reporting lag time and to provide an independent complementary source of data compared with traditional surveillance approaches. However, such an analysis is currently not possible in the Arabic-speaking world owing to a lack of basic building blocks for research and dialectal variation. Objective We collected around 4000 Arabic tweets related to COVID-19 and influenza. We cleaned and labeled the tweets relative to the Arabic Infectious Diseases Ontology, which includes nonstandard terminology, as well as 11 core concepts and 21 relations. The aim of this study was to analyze Arabic tweets to estimate their usefulness for health surveillance, understand the impact of the informal terms in the analysis, show the effect of deep learning methods in the classification process, and identify the locations where the infection is spreading. Methods We applied the following multilabel classification techniques: binary relevance, classifier chains, label power set, adapted algorithm (multilabel adapted k-nearest neighbors [MLKNN]), support vector machine with naive Bayes features (NBSVM), bidirectional encoder representations from transformers (BERT), and AraBERT (transformer-based model for Arabic language understanding) to identify tweets appearing to be from infected individuals. We also used named entity recognition to predict the place names mentioned in the tweets. Results We achieved an F1 score of up to 88% in the influenza case study and 94% in the COVID-19 one. Adapting for nonstandard terminology and informal language helped to improve accuracy by as much as 15%, with an average improvement of 8%. Deep learning methods achieved an F1 score of up to 94% during the classifying process. Our geolocation detection algorithm had an average accuracy of 54% for predicting the location of users according to tweet content. Conclusions This study identified two Arabic social media data sets for monitoring tweets related to influenza and COVID-19. It demonstrated the importance of including informal terms, which are regularly used by social media users, in the analysis. It also proved that BERT achieves good results when used with new terms in COVID-19 tweets. Finally, the tweet content may contain useful information to determine the location of disease spread.

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

“…Alsudias et al. [29] analyze Arabic tweets that can contain informal terms to assess their usefulness for health surveillance, identifying the locations where the infection is spreading and employing deep learning techniques in the classification process.…”

Exploring Pandemics Events on Twitter by Using Sentiment Analysis and Topic Modelling

“…Alsudias and Rayson [24] monitor the COVID-19 pandemic and influenza epidemic by NLP techniques, including multilabel classification for finding infected people by a set of methods (such as multilabel k-nearest neighbors, and BERT) and predicting location for every infection person by conditional random fields algorithm.…”

Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation

“…It is noteworthy that the use of Twitter data has gained increased popularity with respect to the various alternative data sources and has been shown to substantially improve the predictive performance of ILI models over similar baseline models that only consider epidemiological data such as historical ILI data [ 31 ]. Many studies have analyzed Twitter data in different languages, such as Japanese, Arabic, and English, to support ILI surveillance [ 26 , 30 , 32 - 34 ]. State-of-the art natural language processing (NLP) techniques have been combined in these studies with various machine learning approaches, including decision trees, SVM, and LSTM, for predicting ILI spread.…”

“…More recently, several research endeavors have used Twitter data for the implementation of digital approaches aiding COVID-19 surveillance. Emphasis has been placed on the application of various NLP techniques for topic modeling and sentiment analysis, whereas only a few studies have used Twitter data to predict the evolution of COVID-19 cases [ 34 - 37 ]. It is worth noting that little previous research has focused on the use case of the Greek language within the context of modeling disease spread from Twitter data, whereas NLP resources and methods are less well developed for Greek than for English [ 38 ].…”