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

Ghanem, Abdelghani; Asaad, Chaimae; Hafidi, Hakim; Moukafih, Youness; Guermah, Bassma; Sbihi, Nada; Zakroum, Mehdi; Ghogho, Mounir; Dairi, Meriem; Cherqaoui, Mariam; Bäına, Karim

doi:10.3390/ijerph182212172

Cited by 5 publications

(4 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another similar study [43] intends to build a health monitoring system in order to discover concerns associated with the COVID-19 epidemic and to assess the sentiments of Moroccan users on Facebook, Twitter, YouTube, and other popular websites. In addition to the Arabic language, the researchers focused on the Moroccan dialect and developed MD-ULM, the first Universal Language Model for the Moroccan dialect.…”

Section: Background and Related Workmentioning

confidence: 99%

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Boulesnane¹,

Meshoul

Aouissi³

2022

Mathematics

View full text Add to dashboard Cite

The recent large outbreak of infectious diseases, such as influenza-like illnesses and COVID-19, has resulted in a flood of health-related posts on the Internet in general and on social media in particular, in a wide range of languages and dialects around the world. The obvious relationship between the number of infectious disease cases and the number of social media posts prompted us to consider how we can leverage such health-related content to detect the emergence of diseases, particularly influenza-like illnesses, and foster disease surveillance systems. We used Algerian Arabic posts as a case study in our research. From data collection to content classification, a complete workflow was implemented. The main contributions of this work are the creation of a large corpus of Arabic Facebook posts based on Algerian dialect and the proposal of a new classification model based on sentiment analysis and one-dimensional convolutional neural networks. The proposed model categorizes Facebook posts based on the users’ feelings. To counteract data imbalance, two techniques have been considered, namely, SMOTE and random oversampling (ROS). Using a 5-fold cross-validation, the proposed model outperformed other baseline and state-of-the-art models such as SVM, LSTM, GRU, and BiLTSM in terms of several performance metrics.

show abstract

Section: Background and Related Workmentioning

confidence: 99%

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Boulesnane¹,

Meshoul

Aouissi³

2022

Mathematics

View full text Add to dashboard Cite

show abstract

“…In this context, considerable efforts have been made to create datasets based primarily on tweets from Twitter. For example, to gauge Moroccans' emotional response to the pandemic and government decisions, Ghanem et al [9] developed a real-time infoveillance platform and collected comments related to Covid-19 from the most popular social media platforms in Morocco, including Twitter, Facebook, and Youtube, as well as two popular news sites. The dataset contained over 747K comments expressed in Moroccan dialect and modern standard Arabic throughout 2020.…”

Section: Related Workmentioning

confidence: 99%

Evidence-Aware Multilingual Fake News Detection

Hammouchi

Ghogho

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

Due to the rapid growth of the Internet and the subsequent rise of social media users, sharing information has become more flexible than ever before. This unrestricted freedom has also led to an increase in fake news. During the Covid-19 outbreak, fake news spread globally, negatively affecting authorities' decisions and the health of individuals. As a result, governments, media agencies, and academics have established factchecking units and developed automatic detection systems. Research approaches focused largely on writing styles, propagation patterns, and building knowledge bases that serve as a reference for fact checking. However, little work has been done to assess the credibility of the source of the claim to be checked. This paper proposes a general framework for detecting fake news that uses external evidence to verify the veracity of online news in a multilingual setting. Search results from Google are used as evidence, and the claim is cross-checked with the top five search results. Additionally, we associate a vector of credibility scores with each evidence source based on the domain name and website reputation metrics. All of these components are combined to derive better predictions of the veracity of claims. Further, we analyze the claim-evidence entailment relationship and select supporting and refuting evidence to cross-check with the claim. The selection free approach yields better detection performance. In this work, we consider as a case study Covid-19 related news. Our framework achieves an F1-score of 0.85 and 0.97 in distinguishing fake from true news on XFact and Constraint datasets respectively. With the achieved results, the proposed framework present a promising automatic fact checker for both early and late detection.

show abstract

“…A pre-trained GPT (Generative pre-trained transformer) model was customized with Covid-19 tweets was applied to reveal insights into the biases and opinions of the users [109]. A universal language model for the Moroccan dialect was built in [110] and fine-tuned using a collected Covid-19 dataset to perform topic modeling, emotion recognition and polar sentiment analysis, and understand the Moroccan population's feelings towards the pandemic and the government's response to it. LSTM (Long Short Term Memory), BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Language Representation with Informative Entities) were used in [111] to analyze the evolution of sentiments in the face of Covid-19's public health crisis.…”

Section: Classification Covid-19mentioning

confidence: 99%

“…Twitter [97][98][99][100][101]103] Reddit [101] Multiple Epidemics Weibo [102] ML Classification Covid19 Twitter [104,105,112] Weibo [106] Zika Twitter [107] DL Classification Covid19 Twitter [108][109][110] Weibo [111] Topic Modeling Covid19 Twitter [110,[113][114][115][116][117] Weibo [118,119] 4.2.2. RQ2: Can social media be used for misinformation management during epidemics?…”

Section: Covid19mentioning

confidence: 99%

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Asaad¹,

Khaouja²,

Ghogho³

et al. 2022

Preprint

View full text Add to dashboard Cite

BACKGROUND Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media sites offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. OBJECTIVE The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. METHODS Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. RESULTS Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. CONCLUSIONS Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment.

show abstract

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

Cited by 5 publications

References 28 publications

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Influenza-like Illness Detection from Arabic Facebook Posts Based on Sentiment Analysis and 1D Convolutional Neural Network

Evidence-Aware Multilingual Fake News Detection

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Contact Info

Product

Resources

About