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

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.

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Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

Journal Of Big Data ◽

10.1186/s40537-021-00488-w ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Yahya Albalawi ◽

Jim Buckley ◽

Nikola S. Nikolov

Keyword(s):

Social Media ◽

Deep Learning ◽

Comprehensive Evaluation ◽

Classification Problem ◽

Data Sets ◽

Word Embeddings ◽

Data Set ◽

Lower Accuracy ◽

Health Related ◽

The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

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DAViS: a unified solution for data collection, analyzation, and visualization in real-time stock market prediction

Financial Innovation ◽

10.1186/s40854-021-00269-7 ◽

2021 ◽

Vol 7 (1) ◽

Author(s):

Suppawong Tuarob ◽

Poom Wettayakorn ◽

Ponpat Phetchai ◽

Siripong Traivijitkhun ◽

Sunghoon Lim ◽

...

Keyword(s):

Machine Learning ◽

Social Media ◽

Data Collection ◽

Stock Market ◽

Real Time ◽

Stock Prices ◽

Contextual Information ◽

Online News ◽

Stock Prediction ◽

Stock Market Prediction

AbstractThe explosion of online information with the recent advent of digital technology in information processing, information storing, information sharing, natural language processing, and text mining techniques has enabled stock investors to uncover market movement and volatility from heterogeneous content. For example, a typical stock market investor reads the news, explores market sentiment, and analyzes technical details in order to make a sound decision prior to purchasing or selling a particular company’s stock. However, capturing a dynamic stock market trend is challenging owing to high fluctuation and the non-stationary nature of the stock market. Although existing studies have attempted to enhance stock prediction, few have provided a complete decision-support system for investors to retrieve real-time data from multiple sources and extract insightful information for sound decision-making. To address the above challenge, we propose a unified solution for data collection, analysis, and visualization in real-time stock market prediction to retrieve and process relevant financial data from news articles, social media, and company technical information. We aim to provide not only useful information for stock investors but also meaningful visualization that enables investors to effectively interpret storyline events affecting stock prices. Specifically, we utilize an ensemble stacking of diversified machine-learning-based estimators and innovative contextual feature engineering to predict the next day’s stock prices. Experiment results show that our proposed stock forecasting method outperforms a traditional baseline with an average mean absolute percentage error of 0.93. Our findings confirm that leveraging an ensemble scheme of machine learning methods with contextual information improves stock prediction performance. Finally, our study could be further extended to a wide variety of innovative financial applications that seek to incorporate external insight from contextual information such as large-scale online news articles and social media data.

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Imported feminism: problematizing the Westernized image of the feminist in Arab social media

Journal of Humanities and Applied Social Sciences ◽

10.1108/jhass-10-2020-0181 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ramy Magdy ◽

Maries Mikhael ◽

Yassmine G. Hussein

Keyword(s):

Social Media ◽

New Media ◽

Real Time ◽

Arab Women ◽

Content Type ◽

Second Wave Feminism ◽

Women Experiences ◽

Facebook Pages ◽

The Impact ◽

Second Wave

Purpose This paper aims to analyze the discourse of Arab feminism social media pages as a form of real-time new media. This is to be conducted culturally to understand the Westernized character these pages tend to propagate and the politico-cultural significations of such a propagation. Design/methodology/approach Using visual and content analysis the paper analyzes both the written and visual contents of two popular Arab feminist Facebook pages, “Thory” and “Feminist doodles” to explore its culture relevance/Westernization via the categories of “re-employing the binary second wave feminism, the historical relevance and the Westernized tone of both pages. Findings The pages showed a tendency toward second wave, Westernized, anti-orient feminism. Such importation of feminism made the pages’ message not only a bit irrelevant but also conceptually violent to a large extent. Starting from alien contexts, the two pages dislocate the Arab women experiences of their situation for the sake of comprehending and adapting to heavily Westernized images. Originality/value The paper contributes to the ongoing debate over the gender issue in the Arab context after 2011, what it originally offers is discussing the cultural relevance of popular feminist Facebook pages claiming to represent the everyday struggles of the Arab women. In addition, it shows the impact of real-time media on identity formulation.

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Impact of Social Media Visuals on People’s Visual Communication during Social Movements in Bangladesh

Global Journal of Human-Social Science ◽

10.34257/gjhssavol19is11pg9 ◽

2019 ◽

pp. 9-18

Author(s):

MD Saiful Alam Chowdhury ◽

Monira Begum ◽

Shaolin Shaon

Keyword(s):

Social Media ◽

Social Movements ◽

Social Networking ◽

Social Networking Sites ◽

Visual Communication ◽

Online News ◽

Substantial Role ◽

The Past ◽

Popular View ◽

The Impact

The past decade has seen an armorial growth of the influence of social media on many aspects of people’s lives. Social networking sites, especially Facebook, play a substantial role in framing popular view through its contents. This article explores the impact of visuals, especially photos and videos, published in social media during social movements. Importantly that some visuals received attention in social media during agitations which later got featured or become news in print, electronic and online news portal media as well. Some of the visuals later proved to be edited or fabricated contents which created confusion among participants in this research and beyond. The confusion has contributed to the acceleration or shrinkage of the movement in question in many cases. The center of this article is to examine how social media visuals influence people’s visual communication during social movements. Additionally, it digs out the user’s activity on social media during movements.

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Using Social Media Listening to measure the impact of diseases and treatments on patients’ Health-Related Quality of Life (Preprint)

10.2196/preprints.31528 ◽

2021 ◽

Author(s):

Simon Renner ◽

Tom Marty ◽

Mickaïl Khadhar ◽

Pierre Foulquié ◽

Paméla Voillot ◽

...

Keyword(s):

Quality Of Life ◽

Social Media ◽

Health Related Quality ◽

Web Crawler ◽

Real World Data ◽

Patient Centered ◽

Related Quality ◽

Health Related ◽

The Impact

BACKGROUND Monitoring social media has been shown to be a useful mean to capture patients’ opinions and feelings about medical issues, ranging from diseases to treatments. Health-related quality of life is a useful indicator of overall patients’ health that can be captured online. OBJECTIVE This study aims to describe a Social Media Listening system which is able to detect any impact of diseases or treatments on health-related quality of life as reported in social media and forum messages written by patients. METHODS Using a web crawler, 19 health-related forums in France were harvested and messages relating a patient’s experience with a disease or a treatment were specifically collected. The algorithm was based on the two clinically validated questionnaires SF-36 and EQ-5D. Models were trained using cross-validation (a machine learning technique which obtains the best combination between different data samples) and hyperparameter optimization. Over-sampling was used to increase the infrequent dimension: after annotation, SMOTE was used to balance the proportion of the dimension among messages. RESULTS The training set was composed of 1400 messages, randomly taken from a 20 000 batch of health-related messages coming from forums. The algorithm was able to detect a general impact on health-related quality of life (sensitivity of 0.83 and specificity of 0.74), a physical impact (0.67 and 0.76), a psychic impact (0.82 and 0.60), an activity-related impact (0.73 and 0.78), a relational impact (0.73 and 0.70) and a financial impact (0.79 and 0.74). CONCLUSIONS Real-time assessment of patients’ health-related quality of life through the use of Social Media Listening is useful to a patient-centered medical care. Social media as a source of Real World Data are a complementary point of vue to understand patients’ concerns, unmet needs and how diseases and treatments can be a burden in their daily lives. Trial Registration: Not applicable (not a trial)

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The Impact of Social vs. Nonsocial Referring Channels on Online News Consumption

Management Science ◽

10.1287/mnsc.2020.3637 ◽

2020 ◽

Author(s):

Sagit Bar-Gill ◽

Yael Inbar ◽

Shachar Reichman

Keyword(s):

Social Media ◽

Large Scale ◽

Online News ◽

Clickstream Data ◽

News Consumption ◽

Online Newspaper ◽

News Website ◽

The Impact ◽

News Sharing ◽

Sharing Behavior

The digitization of news markets has created a key role for online referring channels. This research combines field and laboratory experiments and analysis of large-scale clickstream data to study the effects of social versus nonsocial referral sources on news consumption in a referred news website visit. We theorize that referrer-specific browsing modes and referrer-induced news consumption thresholds interact to impact news consumption in referred visits to an online newspaper and that news sharing motivations invoked by the referral source impact sharing behavior in these referred visits. We find that social media referrals promote directed news consumption—visits with fewer articles, shorter durations, yet higher reading completion rates—compared with nonsocial referrals. Furthermore, social referrals invoke weaker informational sharing motivations relative to nonsocial referrals, thus leading to a lower news sharing propensity relative to nonsocial referrals. The results highlight how news consumption changes when an increasing amount of traffic is referred by social media, provide insights applicable to news outlets’ strategies, and speak to ongoing debates regarding biases arising from social media’s growing importance as an avenue for news consumption. This paper was accepted by Anandhi Bharadwaj, information systems.

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Round table: Online anti-vaccination movements: the role of social media in public health communications

European Journal of Public Health ◽

10.1093/eurpub/ckz185.681 ◽

2019 ◽

Vol 29 (Supplement_4) ◽

Author(s):

Keyword(s):

Public Health ◽

Social Networks ◽

Social Media ◽

Real Time ◽

Hpv Vaccine ◽

Digital Health ◽

World Health ◽

Swine Flu ◽

Potential Impact ◽

The Impact

Abstract Digital health has revolutionised healthcare, with implications for understanding public reaction to health emergencies and interventions. Social media provides a space where like-minded people can share interests and concerns in real-time, regardless of their location. This can be a force for good, as platforms like Twitter can spread correct information about outbreaks, for example in the 2009 swine flu pandemic. However, social media can also disseminate incorrect information or deliberately spread misinformation leading to adverse public health sentiment and outcomes. The current issues around trust in vaccines is the best-known example. Vaccine hesitancy, traditionally linked to issues of trust, misinformation and prior beliefs, has been increasingly fueled by influential groups on social media and the Internet. Ultimately, anti-vaccination movements have the potential to lead to outbreaks of vaccine-preventable diseases, especially if refusal is concentrated locally, creating vulnerable populations. For example, 2018-19 saw a large increase in incidence of measles in the US and Europe (where cases tripled from 2017), two regions where the disease was already or almost eliminated. In 2019, the World Health Organisation listed anti-vaccination movements as one of the top 10 threats to global public health. HPV vaccination is another example of the impact of anti-vaccination movements. As viral videos originating on YouTube spread across social networks, uptake has tumbled in a number of countries, with Japan, Denmark, Colombia and Ireland being badly hit. In Japan, the government came under sufficient pressure that they de-recommended HPV vaccine, seeing an 80% uptake rate fall below 1% in 2014. There have been reports of successful interventions by national governments. A recent campaign run by the HPV Alliance (a coalition of some 35 private companies, charities and public institutions) in Ireland has seen rates below 40% back up to a national average of 75%. A combination of hard-hitting personal testimonials, social media and traditional media promoted the HPV vaccine. Despite this, systematic engagement and supranational strategies are still in the early stages of being formulated. As misleading information spread through social media and digital networks has undesirable impact on attitudes to vaccination (and uptake rates), urgent actions are required. Analysis and visualisation techniques mining data streams from social media platforms, such as Twitter, Youtube enable real-time understanding of vaccine sentiments and information flows. Through identification of key influencers and flashpoints in articles about vaccination going viral, targeted public health responses could be developed. This roundtable discussion will showcase different ways in which media and social networks, accessible in real-time provide an opportunity for detecting a change in public confidence in vaccines, for identifying users and rumors and for assessing potential impact in order to know how to best respond. Key messages Social media has significantly enhanced our understanding of anti-vaccination movements and potential impact on public health attitudes and behaviors regarding vaccination. Innovative methods of analysing social media data, from digital health, data science and computer science, have an important role in developing health promotions to counter anti-vaccination movements.

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Comparison of Traditional Citation Metrics and Altmetrics Among Dermatology Journals: Content and Correlational Analysis Study (Preprint)

10.2196/preprints.15643 ◽

2019 ◽

Author(s):

Gregg Murray ◽

Rebecca Hellen ◽

James Ralph ◽

Siona Ni Raghallaigh

Keyword(s):

Social Media ◽

Citation Count ◽

Research Impact ◽

Online News ◽

Positive Correlation ◽

News Sources ◽

Journal Citation Reports ◽

Social Media Platforms ◽

The Impact ◽

Article Citation

BACKGROUND Research impact has traditionally been measured using citation count and impact factor (IF). Academics have long relied heavily on this form of metric system to measure a publication’s impact. A higher number of citations is viewed as an indicator of the importance of the research and a marker for the impact of the publishing journal. Recently, social media and online news sources have become important avenues for dissemination of research, resulting in the emergence of an alternative metric system known as altmetrics. OBJECTIVE We assessed the correlation between altmetric attention score (AAS) and traditional scientific impact markers, namely journal IF and article citation count, for all the dermatology journal and published articles of 2017. METHODS We identified dermatology journals and their associated IFs available in 2017 using InCites Journal Citation Reports. We entered all 64 official dermatology journals into Altmetric Explorer, a Web-based platform that enables users to browse and report on all attention data for every piece of scholarly content for which Altmetric Explorer has found attention. RESULTS For the 64 dermatology journals, there was a moderate positive correlation between journal IF and journal AAS (rs=.513, P<.001). In 2017, 6323 articles were published in the 64 dermatology journals. Our data show that there was a weak positive correlation between the traditional article citation count and AAS (rs=.257, P<.001). CONCLUSIONS Our data show a weak correlation between article citation count and AAS. Temporal factors may explain this weak association. Newer articles may receive increased online attention after publication, while it may take longer for scientific citation counts to accumulate. Stories that are at times deemed newsworthy and then disseminated across the media and social media platforms border on sensationalism and may not be truly academic in nature. The opposite can also be true.

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The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

Information Systems Frontiers ◽

10.1007/s10796-021-10191-z ◽

2021 ◽

Author(s):

Rui Liu ◽

Suraksha Gupta ◽

Parth Patel

Keyword(s):

Social Media ◽

Health Care Professionals ◽

Digital Health ◽

Social Media Marketing ◽

Ethical Concerns ◽

Related Information ◽

Health Related ◽

Research Propositions ◽

The Impact ◽

Depth Interviews

AbstractSocial media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.

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