ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination

Mubarak, Hamdy; Hassan, Sabit; Chowdhury, Shammur Absar; Alam, Firoj

doi:10.48550/arxiv.2201.06496

Cited by 2 publications

(4 citation statements)

References 25 publications

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“…ArCovidVac [40] is a manually labeled Arabic Twitter dataset for the COVID-19 vaccine campaign, including numerous Arab nations. defining their opinion on vaccination and the immunization procedure.…”

Section: Arcovidvacmentioning

confidence: 99%

Stacked-CNN-BiLSTM-COVID: An Effective Stacked Ensemble Deep Learning Framework for Sentiment Analysis of Arabic COVID-19 Tweets

Abdelhady

Soliman

Farghally

2022

Preprint

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Social networks are popular for advertising, idea sharing, and opinion formation. Due to COVID-19, coronavirus information disseminated on social media affects people's lives directly. Individuals sometimes managed it well, but it often hampered daily activities. As a result, analyzing people's feelings is important. Sentiment analysis identifies opinions or sentiments from text. In this paper, we present an effective model that leverages the benefits of Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) to categorize Arabic tweets using a stacked ensemble learning model. First, the tweets are represented as vectors using a word embedding model, then the text feature is extracted by CNN, and finally the context information of the text is acquired by BiLSTM. Aravec, FastText, and ArWordVec are employed separately to assess the impact of the word embedding on the our model. We also compare the proposed method to various deep learning models: CNN, LSTM, and BiLSTM. Experiments are performed on three different Arabic datasets related to COVID-19 and vaccines. Empirical findings show that the proposed model outperformed the other models' results by achieving F-measures of 76.76\%, 87.\%, and 80.5\% on the SenWave, AraCOVID19-SSD, and ArCovidVac datasets, respectively.

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Section: Arcovidvacmentioning

confidence: 99%

Stacked-CNN-BiLSTM-COVID: An Effective Stacked Ensemble Deep Learning Framework for Sentiment Analysis of Arabic COVID-19 Tweets

Abdelhady

Soliman

Farghally

2022

Preprint

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“…ArCovidVac is the first Arabic twitter dataset designed to gather tweets concerning people reviews after vaccine constituting about 10k tweet [5]. They used a set of keywords (vaccine, vaccination, ‫تطعيم‬ and ‫)لقاح‬ to collect tweets and manually annotated COVID-19 vaccine infodemic from Jan 5 th to Feb 3 rd , 2021 using twarc search API.…”

Section: Datamentioning

confidence: 99%

“…The collected tweets covering different Arabic countries and labeled with one of the predefined set of the following classes: "Info-news", "Celebrity", "Plan", "Requests", "Rumors", "Advice", "Restrictions", "Personal", "Advice", "Restrictions", "Personal", "Unrelated" and "others". Tweets are classified in two stances positive (pr-vaccination), and negative (against vaccination), we focused on the main task of our research which is misinformation classification tasks including fake tweets from noninformative ones, and real tweets from Info-news, trusted organizations [5].…”

Section: Datamentioning

confidence: 99%

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Misinformation Detection in Arabic Tweets: A Case Study about COVID-19 Vaccination

Ashraf

Nayel

Taha

2022

Benha Journal of Applied Sciences

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Misinformation about COVID-19 overwhelmed our lives due to the tremendous usage of social media, especially Twitter. Spreading misinformation caused fear and panic among people affecting the national economic security of many countries. Vaccination is the crucial key to limiting the pandemic spread of COVID-19. Therefore, researchers start to detect and fight against the spread of misinformation taking it as a new challenge. This paper illustrates a model for misinformation detection in Arabic tweets using Natural Language Processing (NLP) techniques. A machine learning-based system has been developed regarding COVID-19 vaccination tweets. Term Frequency-Inverse Document Frequency (TF-IDF) has been used as vector space model for feature extraction. Support Vector Machines classification algorithm has been used for implementation the proposed system. Evaluation of the system, using different metrics, has been implemented on Arcov-19Vac, a dataset of Arabic tweets related to COVID-19 vaccination. The results reported by the illustrated model show that the performance of our model is promising.

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ArCovidVac: Analyzing Arabic Tweets About COVID-19 Vaccination

Cited by 2 publications

References 25 publications

Stacked-CNN-BiLSTM-COVID: An Effective Stacked Ensemble Deep Learning Framework for Sentiment Analysis of Arabic COVID-19 Tweets

Stacked-CNN-BiLSTM-COVID: An Effective Stacked Ensemble Deep Learning Framework for Sentiment Analysis of Arabic COVID-19 Tweets

Misinformation Detection in Arabic Tweets: A Case Study about COVID-19 Vaccination

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