Cartooning COVID-19 on facebook

Mike Asukwo's brightly textured cartoons captured the colorless realities of the Covid pandemic in Nigeria and circulated along with global pandemic discourses as local visual archives of Nigeria's postcolonial disenchantment. Social media is particularly central to the aesthetic value of Asukwo's political cartoons in producing and constraining the expression of civic agency among Nigerians. His cartoons demonstrate how everyday media practices such as the decoding and reproduction of popular culture texts online can challenge hierarchical systems of control. This chapter examines the conditions under which cultural netizens like Asukwo and his online audience make sense of the state's response to the Covid-19 pandemic, highlighting how Nigeria's crisis of infrastructure manifests in cartoons to accentuate the messiness of political leadership. The chapter concludes on the ambivalent valences of the social web and the digital public sphere it fosters, underscoring how social media documents the pandemic perspectives of members of the digital class in Nigeria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

The effects of cognitive- and affective-based trust on students' knowledge sharing and learning performance during the Covid-19 pandemic

This study examined the effects of cognitive and affective-based trust on knowledge sharing among students, which influences learning performance during the COVID-19 pandemic. A survey was conducted with 730 participants, and analysis was carried out using structural equation modeling (SEM) based on the uses and gratifications (U&G) theory. The results showed that cognitive and affective trust significantly affects students' knowledge sharing behavior on Facebook, which further influences learning performance. This study also showed that social media had become a tool for social interaction and learning, which is crucial to students during the COVID-19 pandemic.

Forecasting Residential Energy Consumption: A Case Study for Greece

Residential energy consumption forecasting has immense value in energy efficiency and sustainability. In the current work we tried to forecast energy consumption on residences in Athens, Greece. As a proof of concept, smart sensors were installed into two residences that recorded energy consumption, as well as indoors environmental variables (humidity and temperature). It should be noted that the data set was collected during the COVID-19 pandemic. Moreover, we integrated weather data from a public weather site. A dashboard was designed to facilitate monitoring of the sensors' data. We addressed various issues related to data quality and then we tried different models to forecast daily energy consumption. In particular, LSTM neural networks, ARIMA, SARIMA, SARIMAX and Facebook (FB) Prophet were tested. Overall SARIMA and FB Prophet had the best performance. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

The Impact of Socioeconomic and Demographic Factors on COVID-19 Forecasting Model

Background: COVID-19 has become a primary public health issue in various countries across the world. The main difficulty in managing outbreaks of infectious diseases is due to the difference in geographical, demographic, economic inequalities and people's behavior in each region. The spread of disease acts like a series of diverse regional outbreaks;each part has its disease transmission pattern. Objective: This study aims to assess the association of socioeconomic and demographic factors to COVID-19 cases through cluster analysis and forecast the daily cases of COVID-19 in each cluster using a predictive modeling technique. Methods: This study applies a hierarchical clustering approach to group regencies and cities based on their socioeconomic and demographic similarities. After that, a time-series forecasting model, Facebook Prophet, is developed in each cluster to assess the transmissibility risk of COVID-19 over a short period of time. Results: A high incidence of COVID-19 was found in clusters with better socioeconomic conditions and densely populated. The Prophet model forecasted the daily cases of COVID-19 in each cluster, with Mean Absolute Percentage Error (MAPE) of 0.0869;0.1513;and 0.1040, respectively, for cluster 1, cluster 2, and cluster 3. Conclusion: Socioeconomic and demographic factors were associated with different COVID-19 waves in a region. From the study, we found that considering socioeconomic and demographic factors to forecast COVID-19 cases played a crucial role in determining the risk in that area. © 2023 The Authors. Published by Universitas Airlangga.

Moral Narratives Around the Vaccination Debate on Facebook

Vaccine hesitancy is a complex issue with psychological, cultural, and even societal factors entangled in the decision-making process. The narrative around this process is captured in our everyday interactions;social media data offer a direct and spontaneous view of peoples' argumentation. Here, we analysed more than 500,000 public posts and comments from Facebook Pages dedicated to the topic of vaccination to study the role of moral values and, in particular, the understudied role of the Liberty moral foundation from the actual user-generated text. We operationalise morality by employing the Moral Foundations Theory, while our proposed framework is based on recurrent neural network classifiers with a short memory and entity linking information. Our findings show that the principal moral narratives around the vaccination debate focus on the values of Liberty, Care, and Authority. Vaccine advocates urge compliance with the authorities as prosocial behaviour to protect society. On the other hand, vaccine sceptics mainly build their narrative around the value of Liberty, advocating for the right to choose freely whether to adhere or not to the vaccination. We contribute to the automatic understanding of vaccine hesitancy drivers emerging from user-generated text, providing concrete insights into the moral framing around vaccination decision-making. Especially in emergencies such as the Covid-19 pandemic, contrary to traditional surveys, these insights can be provided contemporary to the event, helping policymakers craft communication campaigns that adequately address the concerns of the hesitant population. © 2023 ACM.

(Re)framing the emerging mobility regime at the U.S.-Mexico borderlands: Covid-19, temporality, and racial capitalism

In this paper, we examine transborder commuters' experiences (i.e. individuals who commute between U.S. and Mexican border cities frequently) during the Covid-19 pandemic, with keen attention to the links between racial capitalism and temporality. We address two interrelated issues: first, we unpack how the United States framed the pandemic through the metaphor of war and the production of the categories of 'essential work(er)' and 'essential travel' to ensure racial capitalism's surplus labor and continuation. These categories function like a double-edged sword, tying racialized populations to racial capitalism's temporality to exploit them while excluding privileged others. We argue that Covid-19's temporality conflicts with racial capitalism's temporality. While the former relies on the deceleration of everyday life, the latter depends on constant acceleration driven by profit-seeking. Using queer and feminist theoretical lenses, we then demonstrate how U.S. Covid-19 border restrictions at land ports of entry exacerbated transborder commuters' cross-border travels and privileged some based on legal status. As a result, they used public Facebook groups to navigate and comprehend new commuting conditions, disidentifying with the United States' official pandemic framing and producing their own. This shared experience catalyzed 'digital transborder kinships' or temporally-bound socialities rooted in relational care, advocacy, and knowledge production. [ FROM AUTHOR] Copyright of Mobilities is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Stacked Bi-directional Long Short Term Memory model for Multi-Class Arabic Sentiment Analysis on Covid-19

The world has been affected by the Covid-19 epidemic during the last three years. During that period, most people tended to use social networks, where by searching for topics related to Covid-19, information could be provided to manage decisions by organizations or governments about public health. With the importance of the Arabic language, despite the lack of research targeting it, using Arabic language as a source of data and analyzing it due to the large number of users on social networks gives an impetus to understand people's feelings about the Covid-19 pandemic. One of the challenges facing sentiment analysis in Arabic is the use of dialects. The most common and existing methods used have been quite ineffective as they are oblivious to contextual information and cannot handle long-distance word dependencies. The Iraqi Arabic dialect is one of the Arabic dialects that still suffers from a lack of research in sentiment analysis. In this study, the official page of the Iraqi Ministry of Health on Facebook was used to collect and analysis comments. Word2vec model is incorporated to extract words semantic characteristics. To capture contextual features, Stacked Bi-directional Long Short Term Memory model (Stacked Bi-LSTM) utilizes sequential word vectors derived from the Continuous Bag of Words model. When compared to most common and existing approaches, the proposed method performed well. © 2022 IEEE.

Examining the Growth of Online Entrepreneurship Effects on Family Income in Jordan during the Corona Virus

According to the significant impacts of social media and the internet on all facets of our lives in general and the business world in particular, many business owners and entrepreneurs who are looking to expand their clientele or start their own ventures have moved to the virtual world, particularly when they want to advance their careers. For the reasons mentioned above, this article aims to ascertain how social media networks impact business operations, with a focus on how they affect entrepreneurship growth. Facebook and Instagram are the two most useful social media platforms. The findings indicate that Facebook and Instagram have a significant impact on increasing entrepreneurship and household income in Jordan. © 2023 IEEE.

A Machine Learning Approach for COVID-19 Tweet Classification

Vaccine-related information is awash on social media platforms like Twitter and Facebook. One party supports vaccination, while the other opposes vaccination and promotes misconceptions and misleading information about the risks of vaccination. The analysis of social media posts can give significant information into public opinion on vaccines, which can help government authorities in decision-making.This paper describes the dataset used in the shared task, and compares the performance of different classification that are provax, antivax and last neutral for identifying effective tweets related to Covid vaccines.We experimented with a classification-based approach. Our experiment shows that SVM classification performs well in order to effiective post.We're going to do this because vaccination is an important step for Covid19 so people can easily fix the news about the vaccine and grab their own slot and symptom detection is also playing a important part to arrest the spread of disease. © 2022 Copyright for this paper by its authors.

Identification of Facebook posts on nephrology-related terms that benefit patient outreach

Objective: The impact of social media on public health has been examined in various studies. However, none have explored user engagement based on the type of Facebook posts related to renal disease. Therefore, the present study sought to determine which type of nephrology-related posts have greater user engagement. Setting(s): Facebook pages. Method(s): The posts on a specific Facebook page curated by a team of nephrologists in Malaysia were examined in this cross-sectional study. The type of post, likes, comments, shares of a post and reach of a post were used for data analysis. Analysis of variance was used to quantify the relative contribution of each independent variable to the odds of the post being highly liked or shared. The Kruskal-Wallis test was used to compare links, photos, shared videos, status and videos for parameters such as reach, the number of times a specific piece of content has been displayed on a screen (impressions), and user engagement. Result(s): Shared videos and photos received the highest median reach of 5,862 and 5,880, respectively. People who 'liked' the page in 2019, 2020 and 2021 numbered 193, 4,196 and 2,835, respectively. Among the types of content on the Facebook page, photos and shared videos received the highest median lifetime reach of the post compared to links, status and videos in terms of 'people who liked the page'. Conclusion(s): The study findings suggest that posting a video or photo maximises the chance of engagement and meaningfully impacts public health outcomes.Copyright © The Author(s) 2023.

The use of social media and virtual opportunities by integrated vascular surgery residency programs during the COVID-19 pandemic.

OBJECTIVES: The COVID-19 pandemic has significantly affected the 2021 match application cycle as in person sub-internships and interviews have been halted. Given the abrupt change, we aimed to characterize the utilization of social media and virtual open house platforms by integrated vascular surgery residency programs for outreach and networking during the pandemic for the 2021 cycle. METHODS: A list of accredited integrated vascular surgery residency programs was compiled using the Electronic Residency Application Service (ERAS) website provided by the Academic Medical Colleges (AMC). The social media platforms Twitter, Instagram, and Facebook were queried for accounts associated with the training programs or their associated institutional vascular surgery divisions. Each discovered account was surveyed for date of creation as well as posts outlining virtual interactive events such as open houses, meet-and-greets, and virtual sub-internship opportunities. Slopes of the curves representing total account numbers and account numbers on each platform were compared from pre-COVID to current day using linear regression and t-statistics. RESULTS: There were 64 integrated vascular surgery residency programs participating in the 2021 match cycle. 70.3% (N = 45) of programs had a social media presence on at least one of the three platforms. 54.7% (N = 35) of programs had an associated Twitter account. 43.9% (N = 28) of programs had an associated Instagram account. Six (9.4%) programs were found on Facebook. The number of social media accounts significantly increased from March 2020 (37 vs 69, p < .001) to March 2021. CONCLUSIONS: Vascular surgery residency programs have significantly increased use of social media platforms over a 12-month period beginning in March 2020, indicating adaptation to the restrictions prompted by the pandemic.

Does social media usage ameliorate loneliness in rural youth? A cross sectional pilot study.

AIM: To investigate the relationship between social media use and loneliness and psychological wellbeing of youth in rural New South Wales. DESIGN: This was a web-based cross-sectional survey. METHODS: The survey consisted of 33 items including demography (12 items), participants' social media use (9 items), mood and anxiety (6 items), perceived loneliness (6 items), the impact of COVID-19 on social media usage or perceived loneliness (2 items). The participants' mood and anxiety were evaluated using the psychological distress tool (K6), while loneliness was measured using the De Jong Gierveld 6-item scale. Total loneliness and psychological distress scores were compared between demographic variables. RESULTS: A total of 47 participants, aged 16-24 years took part in the study. The majority were women (68%) and many had K6 score that was indicative of psychological distress (68%). About half of the participants indicated that Facebook (FB) was their most used social media platform and two in five participants were on social media within 10 min of waking up each day, about 30% spent more than 20 h per week on social media, and more than two-third sent private messages, images, or videos, multiple times a day. The mean loneliness score was 2.89 (range, 0 to 6), with 0 being 'not lonely' and 6 being 'intense social loneliness'. One-way ANOVA and χ2 test results showed that those who used FB most frequently had significantly higher mean scores for loneliness compared to those that used other social media platforms (p = 0.015). Linear regression analysis revealed that those who commonly used FB were more likely to report higher loneliness scores (coefficient = -1.45, 95%CI -2.63, -0.28, p = 0.017), while gender (p = 0.039), age (p = 0.048), household composition (p = 0.023), and education level (p = 0.014) were associated with severe psychological distress. CONCLUSIONS: The study found that social media usage, particularly FB, as measured by time used and active or passive engagement with the medium, was significantly linked to loneliness, with some impact on psychological distress. Social media use within ten minutes of waking increased the likelihood of psychological distress. However, neither loneliness nor psychological distress were associated with rurality among the rural youth in this study.

Managing Pandemic Communication Online: Turkish Ministry of Health's Digital Communication Strategies During COVID-19

Health ministries around the world have used online communication, specifically social media platforms, to provide information, communicate warnings to the public, and influence behavior according to recommended health precautions due to the COVID-19 pandemic. Grounded in agenda-setting theory, this study analyzes Turkey's Ministry of Health's (MoH) social media communication strategies and practices during COVID-19 through a content analysis of the content shared via its official Twitter, Facebook, and Instagram accounts from February to June 2020, focusing on the first 120 days of the pandemic, when it was at its height. Findings reveal that the MoH's social media activity was mainly driven by Twitter, and the minister of health has become the face of the fight against the pandemic. Results reveal that the government's efforts to fight against the virus and its prevention measures are among the most popular themes in online communication. The MoH's social media communication has shown only limited success in community building and network expansion due to inconsistent and ineffective hashtag use, among other weaknesses in the ministry's use of social media conventions.

Comparing the Effects of Traditional Media and Social Media Use on General Trust in China During the COVID-19 Pandemic

Although previous studies have indicated a generally positive association between social media use and general trust, the differential impacts of traditional media and social media on general trust and their underlying mechanisms have yet to be fully elaborated. Drawing on the three-dimensional definition of social capital (i.e., personal networks, social norms, and interpersonal trust) as its theoretical framework, this study comparatively examines the impacts of traditional media and social media on general trust, focusing on the mediating role of social capital. Analyses of survey data from China (N = 1,519) during the first stage of COVID-19 demonstrate that social media has a positive relationship with general trust. In addition, social media has two opposing indirect effects on general trust through increased interpersonal trust and personal networks. In contrast, traditional media use has no positive relationship with general trust, either directly or indirectly, although it has a positive relationship with social norms. The differential consequences of using traditional media and social media on general trust are discussed from the perspective of social capital.

Teaching presence in students' WhatsApp groups: Affordances for language learning

With the recent COVID-19 pandemic and disruption of campus-based education, the use of mobile social networking applications to supplement formal education has attracted a great deal of attention. Teachers do have opportunities to join students' online groups to share, clarify, and exchange housekeeping information and course-related content with them. Teachers can, in particular, provide English as a foreign language (EFL) students with more sources of linguistic input, interaction, and feedback. Research investigating this potential, however, is still scarce in such contexts. The current study explores the likely affordances of teaching presence in students' WhatsApp groups for designing, facilitating, and guiding cognitive and social processes conducive to their language learning. A mixed-method design was employed to collect both quantitative and qualitative data and information from English-major undergraduates (N = 111) and faculty teachers (N = 8) who joined the same WhatsApp groups for one academic semester at a major university in Oman. Descriptive and thematic analyses of data from a survey with both closed-ended and open-ended questions and semi-structured interviews indicate that the shared WhatsApp groups functioned as small close-knit communities where students were able to constantly access teachers for their assistance, feedback, and clarification of content. Despite these merits, however, the participating faculty believed that the presence of teachers in WhatsApp groups might have consequences for students' tolerance of ambiguity, scaffolding, and autonomous language learning. The paper concludes by discussing several pedagogical implications and directions for future research.

Arabic fake news detection in social media Based on AraBERT

Social media has become a source of information for many people because of its freedom of use. As a result, fake news spread quickly and easily, regardless of its credibility, especially over the past decade. The vast amount of information being shared has fraudulent practices that negatively affect readers' cognitive abilities and mental health. In this study, we aim to introduce a new Arabic COVID-19 dataset for fake news related to COVID-19 from Twitter and Facebook. Afterward, we applied two pre-Trained models of classification AraBERT and BERT base Arabic. As a result, AraBERT models obtained better accuracy than BERT base Arabic in two datasets. © 2022 IEEE.

Temporal Dynamics of User Engagement with U.S. News Sources on Facebook

Recently, researchers have modeled how reliability and political bias of news may affect Facebook users' engagement, as measured using interaction metrics such as the number of shares, likes, etc. However, the temporal dynamics of Facebook users' engagement with news of varying degrees of bias and reliability is less studied. In light of the COVID-19 pandemic, it is also important to quantify how the pandemic changed user engagement with various news. This paper presents the first temporal study of Facebook users' interaction dynamics, accounting for both the bias and reliability of the publishers. We consider a dataset of 992 U.S. publishers, and the study spans the period from Jan. 2018 to July 2022. This allows us to accurately assess the effect of the covid outbreak on the temporal dynamics of Facebook users' interactions with different classes of news. Our study examines these two parameters' effect on Facebook user engagement using both per-publisher and aggregated statistics. Several findings are revealed by our analysis, including that publishers in different bias and reliability classes experienced significantly different levels of engagement dynamics during and following the covid outbreak. For example, we show that the least reliable news exhibited the most considerable growth of followers during the covid period and the most reliable news sources exhibited the greatest growth rate of followers during the post-covid period. We also show that the interaction rate (number of interactions normalized over the number of followers) with Facebook news posts during the post-covid period is smaller than it was even before the outbreak. Furthermore, we demonstrate how the COVID-19 outbreak caused statistically significant structural breaks in the temporal dynamics of engagement with several types of news, and quantify this effect. With social media becoming a popular news source during crises, the observed temporal dynamics provide important insights into how information was consumed over the recent years, benefiting both researchers and public sectors. © 2022 IEEE.

The increasing role of electronic media in headache.

Most individuals with access to the internet use social media platforms. These platforms represent an excellent opportunity to disseminate knowledge about management and treatment to the benefit of patients. The International Headache Society, The European Headache Federation, and The American Headache Society have electronic media committees to promote and highlight the organizations' expertise and disseminate research findings. A growing mistrust in science has made dealing with infodemics (i.e., sudden access to excessive unvetted information) an increasing part of clinical management. An increasing role of these committees will be to address this challenge. As an example, recent studies have demonstrated that the most popular online content on migraine management is not evidence-based and is disseminated by for-profit organizations. As healthcare professionals and members of professional headache organizations, we are obliged to prioritize knowledge dissemination. A progressive social media strategy is associated not only with increased online visibility and outreach, but also with a higher scientific interest. To identify gaps and barriers, future research should assess the range of available information on headache disorders in electronic media, characterize direct and indirect consequences on clinical management, and recognize best practice and strategies to improve our communication on internet-based communication platforms. In turn, these efforts will reduce the burden of headache disorders by facilitating improved education of both patients and providers.

Analyzing the Evolution of Disinformation Content on Facebook - a Pilot Study

Disinformation spread on social media generates a truly massive amount of content on a daily basis, much of it not quite duplicated but repetitive and related. In this paper, we present an approach for clustering social media posts based on topic modeling in order to identify and formalize an underlying structure in all the noise. This would be of great benefit for tracking evolving trends, analyzing large-scale campaigns, and focusing efforts on debunking or community outreach. The steps we took in particular include harvesting through CrowdTangle huge collection of Facebook posts explicitly identified as containing disinformation by debunking experts, following those links back to the people, pages and groups where they were shared then collecting all posts shared on those channels over an extended period of time. This generated a very large textual dataset which was used in the topic modeling experiments attempting to identify the larger trends in the available data. Finally, the results were transformed and collected in a Knowledge Graph for further study and analysis. Our main goal is to investigate different trends and common patterns in disinformation campaigns, and whether there exist some correlations between some of them. For instance, for some of the most recent social media posts related to COVID-19 and political situation in Ukraine. © 2022 Copyright for this paper by its authors.

Fishing for Validation: Understanding Promises and Challenges of a Private Social Media Group for COVID-19 Long-Hauler Patients

The COVID-19 pandemic has affected more than 301 million people worldwide so far. Many communities (such as minority communities) suffered disproportionately more difficulties throughout the pandemic. In this paper, we would like to focus on one such community: COVID-19 long-haulers community. Long-hauler community consists of people affected by Coronavirus, but their symptoms do not cure in a couple of weeks;instead, they experience lingering symptoms for months. The concerns of this community were initially ignored by health care providers primarily because of limited information. In this paper, we have analyzed the social media discussion of a private Facebook group dedicated to the long-hauler community. In addition, we interviewed the community members to investigate their motivations for joining the group and how the group has impacted their lives as long-hauler patients. Our analyses revealed the primary discussion topics of this community. It also showed how a minority community could stand by each other using social media groups during a crisis. We concluded the paper with long-term implications of our findings for health care systems, policies, and existing literature on cooperative AI. © 2023 ACM.