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Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management

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Listed:
  • Abdelghani Ghanem

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Chaimae Asaad

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco
    École Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Mohammed V University, Rabat 10000, Morocco)

  • Hakim Hafidi

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Youness Moukafih

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Bassma Guermah

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Nada Sbihi

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Mehdi Zakroum

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Mounir Ghogho

    (TICLab, College of Engineering & Architecture, International University of Rabat, Rabat 11103, Morocco)

  • Meriem Dairi

    (College of Management, International University of Rabat, Rabat 11103, Morocco)

  • Mariam Cherqaoui

    (University Ibn Tofail, Kenitra 14000, Morocco)

  • Karim Baina

    (École Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Mohammed V University, Rabat 10000, Morocco)

Abstract

The impact of COVID-19 on socio-economic fronts, public health related aspects and human interactions is undeniable. Amidst the social distancing protocols and the stay-at-home regulations imposed in several countries, citizens took to social media to cope with the emotional turmoil of the pandemic and respond to government issued regulations. In order to uncover the collective emotional response of Moroccan citizens to this pandemic and its effects, we use topic modeling to identify the most dominant COVID-19 related topics of interest amongst Moroccan social media users and sentiment/emotion analysis to gain insights into their reactions to various impactful events. The collected data consists of COVID-19 related comments posted on Twitter, Facebook and Youtube and on the websites of two popular online news outlets in Morocco (Hespress and Hibapress) throughout the year 2020. The comments are expressed in Moroccan Dialect (MD) or Modern Standard Arabic (MSA). To perform topic modeling and sentiment classification, we built a first Universal Language Model for the Moroccan Dialect (MD-ULM) using available corpora, which we have fine-tuned using our COVID-19 dataset. We show that our method significantly outperforms classical machine learning classification methods in Topic Modeling, Emotion Recognition and Polar Sentiment Analysis. To provide real-time infoveillance of these sentiments, we developed an online platform to automate the execution of the different processes, and in particular regular data collection. This platform is meant to be a decision-making assistance tool for COVID-19 mitigation and management in Morocco.

Suggested Citation

  • Abdelghani Ghanem & Chaimae Asaad & Hakim Hafidi & Youness Moukafih & Bassma Guermah & Nada Sbihi & Mehdi Zakroum & Mounir Ghogho & Meriem Dairi & Mariam Cherqaoui & Karim Baina, 2021. "Real-Time Infoveillance of Moroccan Social Media Users’ Sentiments towards the COVID-19 Pandemic and Its Management," IJERPH, MDPI, vol. 18(22), pages 1-19, November.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:22:p:12172-:d:683330
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    References listed on IDEAS

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    1. Carlos de las Heras-Pedrosa & Pablo Sánchez-Núñez & José Ignacio Peláez, 2020. "Sentiment Analysis and Emotion Understanding during the COVID-19 Pandemic in Spain and Its Impact on Digital Ecosystems," IJERPH, MDPI, vol. 17(15), pages 1-22, July.
    2. Faheem Aslam & Tahir Mumtaz Awan & Jabir Hussain Syed & Aisha Kashif & Mahwish Parveen, 2020. "Sentiments and emotions evoked by news headlines of coronavirus disease (COVID-19) outbreak," Palgrave Communications, Palgrave Macmillan, vol. 7(1), pages 1-9, December.
    3. H Manjula Bai, 2020. "The Socio-Economic Implications of the Coronavirus Pandemic (COVID-19): A Review," ComFin Research, Shanlax Journals, vol. 8(4), pages 8-17, October.
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    1. Abdennour Boulesnane & Souham Meshoul & Khaoula Aouissi, 2022. "Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network," Mathematics, MDPI, vol. 10(21), pages 1-22, November.

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