Exploring Social Media for Early Detection of Depression in COVID-19 Patients

The COVID-19 pandemic has caused substantial damage to global health. Even though three years have passed, the world continues to struggle with the virus. Concerns are growing about the impact of COVID-19 on the mental health of infected individuals, who are more likely to experience depression, which can have long-lasting consequences for both the affected individuals and the world. Detection and intervention at an early stage can reduce the risk of depression in COVID-19 patients. In this paper, we investigated the relationship between COVID-19 infection and depression through social media analysis. Firstly, we managed a dataset of COVID-19 patients that contains information about their social media activity both before and after infection. Secondly, We conducted an extensive analysis of this dataset to investigate the characteristic of COVID-19 patients with a higher risk of depression. Thirdly, we proposed a deep neural network for early prediction of depression risk. This model considers daily mood swings as a psychiatric signal and incorporates textual and emotional characteristics via knowledge distillation. Experimental results demonstrate that our proposed framework outperforms baselines in detecting depression risk, with an AUROC of 0.9317 and an AUPRC of 0.8116. Our model has the potential to enable public health organizations to initiate prompt intervention with high-risk patients. © 2023 ACM.

Three dimensions of COVID-19 risk perceptions and their socioeconomic correlates in the United States: A social media analysis.

Social media analysis provides an alternate approach to monitoring and understanding risk perceptions regarding COVID-19 over time. Our current understandings of risk perceptions regarding COVID-19 do not disentangle the three dimensions of risk perceptions (perceived susceptibility, perceived severity, and negative emotion) as the pandemic has evolved. Data are also limited regarding the impact of social determinants of health (SDOH) on COVID-19-related risk perceptions over time. To address these knowledge gaps, we extracted tweets regarding COVID-19-related risk perceptions and developed indicators for the three dimensions of risk perceptions based on over 502 million geotagged tweets posted by over 4.9 million Twitter users from January 2020 to December 2021 in the United States. We examined correlations between risk perception indicator scores and county-level SDOH. The three dimensions of risk perceptions demonstrate different trajectories. Perceived severity maintained a high level throughout the study period. Perceived susceptibility and negative emotion peaked on March 11, 2020 (COVID-19 declared global pandemic by WHO) and then declined and remained stable at lower levels until increasing once again with the Omicron period. Relative frequency of tweet posts on risk perceptions did not closely follow epidemic trends of COVID-19 (cases, deaths). Users from socioeconomically vulnerable counties showed lower attention to perceived severity and susceptibility of COVID-19 than those from wealthier counties. Examining trends in tweets regarding the multiple dimensions of risk perceptions throughout the COVID-19 pandemic can help policymakers frame in-time, tailored, and appropriate responses to prevent viral spread and encourage preventive behavior uptake in the United States.

The European Contestation of the Beijing 2022 Winter Olympics: A Media Perspective

Although the Olympics were meant to play a crucial role in pushing forward the growth of winter sports in the People's Republic of China (PRC) and bolster domestic and international support for its management of COVID-19, they also served as an arena for contesting the selection of Beijing as an Olympic host. The media analysis of the European coverage of the Games suggests that the United States (US) diplomatic boycott and the contestation of Beijing's approach toward human rights penetrated the European narrative of the 2022 Olympics. However, the decline in the coverage of these issues during sports performances shows that this contestation had a short lifespan.

Impact of COVID-19 outbreak and vaccination on ride-sharing services: a social media analysis

The global COVID-19 pandemic produced several changes in nearly every aspect of our lives. Ride-sharing platforms such as Uber and Lyft must adapt their strategies and aims to stay afloat. The analysis in this study is based on 216,120 tweets in the U.S. between January 1, 2019, and December 30, 2021, about Uber. It includes four separate analyses: Popularity and Usage Analytics, Sentimental Analytics, Voice Analytics, and Topic Mining Analytics. The result shows that usage and popularity of Uber on Twitter negatively affect Covid and death cases. In contrast, vaccination helps mitigate the shock of Covid. Additionally, ' ‘Uber's policy and business model was beneficial in improving its positive image during the pandemic;On the early breakout of Covid in the U.S. Uber had a jump on the positive sentiment, mainly because Uber provided safer service than public transportation. [ FROM AUTHOR] Copyright of Transportation Letters is the property of Taylor & Francis Ltd 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.)

Socio-Economic Sustainability Monitoring Based on Intelligent Analysis of Social Media

The paper presents a formal model and based on its software solution for monitoring and analysis of state social-economic sustainability parameters. Initial data is taken from open sources available on the Internet. The model provides a basis for statistical analysis of the mutual influence of factors and hidden patterns. Social well-being is calculated based on the use of neural network models of message processing. The proposed model is implemented as a software package with a graphical interface for practical application in assessing the effects of ongoing or planned activities of national and regional projects. Practical results of its probation and testing were received in the regional health monitoring and management. Initial data included the morbidity growth statistics concerned with coronavirus infection circulation in 2020 and 2021 taken from online open data sources. Monitoring and simulation results allowed generating recommendations of the regional health care system development and provide reasonable decision-making support. The proposed solution can be utilized as a component of an analytical system for social-economic sustainability monitoring and development. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

The victims, villains and heroes of 'panic buying': News media attribution of responsibility for COVID-19 stockpiling.

Societies often respond to a crisis by attributing blame to some groups while constructing others as victims and heroes. While it has received scant sociological attention, 'panic buying' is a critical indicator of such public sentiment at the onset of a crisis, and thus a crucial site for analysis. This article traces dynamics of blame in news media representations of an extreme period of panic buying during COVID-19 in Australia. Analysis reveals that lower socio-economic and ethnically diverse consumers were blamed disproportionately. Unlike wealthier consumers who bulk-bought online, shoppers filling trollies in-store were depicted as selfish and shameful, described using dehumanising language, and portrayed as 'villains' who threatened social order. Supermarkets were cast simultaneously as 'victims' of consumer aggression and 'heroes' for their moral leadership, trustworthiness and problem-solving. This portrayal misunderstands the socio-emotional drivers of panic buying, exacerbates stigma towards already disadvantaged groups, and veils the corporate profiteering that encourages stockpiling.

Sentiment analysis and counselling for COVID-19 pandemic based on social media

As COVID-19 emerged and prolonged, various changes have occurred in our lives. For example, as restrictions on daily life are lengthening, the number of people complaining of depression is increasing. In this paper, we conduct a sentiment analysis by modelling public emotions and issues through social media. Text data written on Twitter is collected by dividing it into the early and late stages of COVID-19, and emotional analysis is performed to reclassify it into positive and negative tweets. Therefore, subject modelling is performed with a total of four datasets to review the results and evaluate the modelling results. Furthermore, topic modelling results are visualised using dimensional reduction, and public opinions on COVID-19 are intuitively confirmed by generating representative words consisting of each topic in the word cloud. Additionally, we implement a COVID-chatbot that provides a question-and-answer service on COVID-19 and verifies the performance in our experiments.

What the COVID-19 crisis reveals about the relationship to space: The case of the closure of the French-German border in Alsace

Introduction. - The COVID-19 crisis of 2020 has led authorities to reestablish measures at the French-German border. The media refer to a "closure" of the border. This constitutes a rapid and brutal event in terms of the cross -border practices and mobility of local inhabitants. Objective. - By considering the closure period as a socio-spatial crisis, we question, first, the thematic structure of media discourse during the period of border closure, and second, the psychological continuity of the crisis discourse, by comparing it with pre -crisis interviews. Method. - A thematic analysis of the discourse is done on a corpus of 407 local press articles, and on 12 semi-structured interviews with young, local inhabitants. Results. - The analysis identified five themes which support the discursive media structure, and which organize and enable the debate. The international comparison and the use of historical and memorial content in the discourse enable actors to take a position on the border closure. The analysis of the links with the interviews shows that the relationship to the border during the crisis is structured on representational dimensions already present in the pre-crisis discourse of the inhabitants. Conclusion. - Results show a psychological continuity in pre and post-crisis discourse: the media discourse reveals preexisting representations of the border, which act as generators of opinions on its closure. Additionally, we discuss the results by focusing on the place of identity-based feelings in the representational relationship to the border: this phenomenon is analysed here on a group and positional level. (c) 2022 Societe Francaise de Psychologie. Published by Elsevier Masson SAS. All rights reserved.

Uncoupling Inequality: Reflections on the Ethics of Benchmarks for Digital Media

Our collaboration seeks to demonstrate shared interrogation by exploring the ethics of machine learning benchmarks from a socio-technical management perspective with insight from public health and ethnic studies. Benchmarks, such as ImageNet, are annotated open data sets for training algorithms. The COVID-19 pandemic reinforced the practical need for ethical information infrastructures to analyze digital and social media, especially related to medicine and race. Social media analysis that obscures Black teen mental health and ignores anti-Asian hate fails as information infrastructure. Despite inadequately handling non-dominant voices, machine learning benchmarks are the basis for analysis in operational systems. Turning to the management literature, we interrogate cross-cutting problems of benchmarks through the lens of coupling, or mutual interdependence between people, technologies, and environments. Uncoupling inequality from machine learning benchmarks may require conceptualizing the social dependencies that build structural barriers to inclusion. © 2022 IEEE Computer Society. All rights reserved.

The Perspectives of Individuals with Comorbidities Towards COVID-19 Booster Vaccine Shots in Twitter: A Social Media Analysis Using Natural Language Processing, Sentiment Analysis and Topic Modeling

Individuals with comorbidities (i.e., Diabetes Mellitus, hypertension, heart diseases) are more likely to develop a more severe form of coronavirus disease 2019 (COVID-19), thus, they should take necessary precautions to avoid infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and its emerging variants and subvariants by getting COVID-19 vaccination and booster doses. In this regard, we used text analytics techniques, specifically Natural Language Processing (NLP), to understand the perception of Twitter users having comorbidities (diabetes, hypertension, and heart diseases) towards the COVID-19 vaccine booster doses. Understanding and identifying Twitter users' perceptions and perspectives will help the members of medical fraternities, governments, and policymakers to frame and implement a suitable public health policy for promoting the uptake of booster shots by such vulnerable people. A total of 176,540 tweets were identified through the scrapping process to understand the perception of individuals with the mentioned comorbidities regarding the COVID-19 booster dose. From sentiment analysis, it was revealed that 57.6% out of 176,540 tweets expressed negative sentiments about the COVID-19 vaccine booster doses. The reasons for negative expressions have been found using the topic modeling approach (i.e., risk factors, fear of myocardial fibrosis, stroke, or death, and using vaccines as bio-weapons). Of note, enhancing the COVID-19 vaccination drive by administering its booster doses to more and more people is of paramount importance for rendering higher protective immunity under the current threats of recently emerging newer Omicron subvariants which are presently causing a rise in cases in a few countries, such as China and others, and might lead to a feasible new wave of the pandemic with the surge in cases at the global level. Copyright © The Author(s) 2023.

Investigating information dissemination and citizen engagement through government social media during the COVID-19 crisis

Purpose: This study explores how effectively the Indian government utilized social media to communicate emergency information and promote citizen engagement and awareness during the first wave of COVID-19 crisis. Design/methodology/approach: This research investigates the tweets scraped from the official Twitter handle "CovidnewsbyMIB" of the Ministry of Information and Broadcasting Government of India;the authors unearthed patterns in the communications between the government and its citizens by adopting various social media analysis techniques. Further, the authors also tried to examine the influence of media richness and dialogic loop on citizen engagement through government social media (CEGSM) using multivariate analysis method. Findings: The results highlighted clusters of words/terms present in the tweets related to COVID-19 combating strategies, guidelines, and updates. The authors also found that media richness has a significant positive relationship with CEGSM, but dialogic loop has an insignificant relationship with CEGSM. Originality/value: This study provides suggestions to government agencies about ways to improve CEGSM by enhancing media richness and dialogic loop elements such as surveys, polls, and responses in the crisis communication. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0307. [ABSTRACT FROM AUTHOR] Copyright of Online Information Review is the property of Emerald Publishing Limited 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Social Media's Role in Achieving Marketing Goals in Iran during the COVID-19 Pandemic

This article explores the impact of social media (SM) on the marketing goals of organizations in Iran during the COVID-19 pandemic. We examine the extent to which firms utilize social media marketing to promote their products in Iran compared to the pre-COVID-19 era. The validity and reliability of the 279 survey results are confirmed using internal and external validity and Cronbach's alpha. The results show that there is a significant positive relationship between the use of SM and the distraction level. Moreover, the gender of the marketer has an impact on the perceived usefulness and application of SM. Finally, a positive effect of working hours per day on the SM usage and the marketing performance is observed. Despite a negative distraction effect, there is no evidence of reduced marketing performance. This research could help organizations to influence the purchasing processes of customers more effectively and at a lower cost. © 2022 by the authors.

Approaches to Assessing the Safety of Medicines during the COVID-19 Pandemic Using the Example of Azithromycin.

Most of the medicines used to treat the novel coronavirus infection (COVID-19) are either approved under an accelerated procedure or not approved for the indication. Consequently, their safety requires special attention. The aim of the study was to review methodological approaches to collecting data on the safety of medicines, using COVID-19 treatment regimens involving azithromycin as a case study. Material(s) and Method(s): PubMed (MEDLINE), Scopus, eLIBRARY, and Cyberleninka databases were searched for publications on azithromycin as part of combination therapy for COVID-19 in 2020-2021. Search queries included names of the medicinal product or its pharmacotherapeutic group and words describing adverse drug reactions (ADRs) during treatment. Result(s): the analysis included 7 publications presenting the results of studies covering the use of azithromycin as part of COVID-19 combination therapy in more than 4000 patients. Most commonly, the patients receiving COVID-19 therapy including azithromycin developed cardiovascular ADRs (up to 30% of azithromycin prescription cases). In 3 of the analysed publications, safety information was collected through spontaneous reporting and active identification based on the findings of laboratory and instrumental investigations performed during the clinical studies;in other 3, only spontaneous reports were used;and in the last one, ADR database information was studied. Conclusion(s): currently, information on ADRs associated with the use of medicines is mainly gathered via spontaneous reporting. Direct sourcing of information on personal experiences with a certain product from patients, among other means through social media analysis, opens a promising direction towards the improvement of existing approaches to collecting safety data.Copyright © 2022 Obstetrics, Gynecology and Reproduction. All rights reserved.

A Comparative Survey of Multimodal Multilabel Sentiment Analysis and Its Applications Initiated Due to the Impact of COVID-19

This study presents a detailed survey of different works related to sentiment analysis. The COVID-19 pandemic and its impact on people's mental health act as the driving force behind this survey. The survey can help study sentiment analysis and approaches taken in many studies to detect human emotions via advanced technology. It can also help in improving present systems by finding loopholes and increasing their accuracy. Various lexicon and ML-based systems and models like Word2Vec and LSTM were studied in the surveyed papers. Some of the current and future directions highlighted were Twitter sentiment analysis, review-based market analysis, determining changing behavior and emotions in a given time period, and detecting the mental health of employees, and students. This survey provides details related to trends and topics in sentiment analysis and an in-depth understanding of various technologies used in different studies. It also gives an insight into the wide variety of applications related to sentiment analysis. © 2022 IEEE.

Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry

This study investigates customer satisfaction through aspect-level sentiment analysis and visual analytics. We collected and examined the flight reviews on TripAdvisor from January 2016 to August 2020 to gauge the impact of COVID-19 on passenger travel sentiment in several aspects. Till now, information systems, management, and tourism research have paid little attention to the use of deep learning and word embedding techniques, such as bidirectional encoder representations from transformers, especially for aspect-level sentiment analysis. This paper aims to identify perceived aspect-based sentiments and predict unrated sentiments for various categories to address this research gap. Ultimately, this study complements existing sentiment analysis methods and extends the use of data-driven and visual analytics approaches to better understand customer satisfaction in the airline industry and within the context of the COVID-19. Our proposed method outperforms baseline comparisons and therefore contributes to the theoretical and managerial literature. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

A Citizen Science Approach for Analyzing Social Media With Crowdsourcing

Social media have the potential to provide timely information about emergency situations and sudden events. However, finding relevant information among the millions of posts being added every day can be difficult, and in current approaches developing an automatic data analysis project requires time and technical skills. This work presents a new approach for the analysis of social media posts, based on configurable automatic classification combined with Citizen Science methodologies. The process is facilitated by a set of flexible, automatic and open-source data processing tools called the Citizen Science Solution Kit. The kit provides a comprehensive set of tools that can be used and personalized in different situations, particularly during natural emergencies, starting from images and text contained in the posts. The tools can be employed by citizen scientists for filtering, classifying, and geolocating the content with a human-in-the-loop approach to support the data analyst, including feedback and suggestions on how to configure the automated tools, and techniques to gather inputs from citizens. Using flooding scenario as a guiding example, this paper illustrates the structure and functioning of the different tools proposed to support citizens scientists in their projects, and a methodological approach to their use. The process is then validated by discussing three case studies based on the Albania earthquake of 2019, the Covid-19 pandemic, and the Thailand floods of 2021. The results suggest that a flexible approach to tools composition and configuration can support a timely setup of an analysis project by citizen scientists, especially in case of emergencies in unexpected locations. © 2013 IEEE.

Prevalence and Propagation of Fake News

In recent years, scholars have raised concerns on the effects that unreliable news, or "fake news,” has on our political sphere, and our democracy as a whole. For example, the propagation of fake news on social media is widely believed to have influenced the outcome of national elections, including the 2016 U.S. Presidential Election, and the 2020 COVID-19 pandemic. What drives the propagation of fake news on an individual level, and which interventions could effectively reduce the propagation rate? Our model disentangles bias from truthfulness of an article and examines the relationship between these two parameters and a reader's own beliefs. Using the model, we create policy recommendations for both social media platforms and individual social media users to reduce the spread of untruthful or highly biased news. We recommend that platforms sponsor unbiased truthful news, focus fact-checking efforts on mild to moderately biased news, recommend friend suggestions across the political spectrum, and provide users with reports about the political alignment of their feed. We recommend that individual social media users fact check news that strongly aligns with their political belief and read articles of opposing political bias. [ FROM AUTHOR] Copyright of Statistics & Public Policy is the property of Taylor & Francis Ltd 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.)

Social Media Analysis using Sentiment Analysis on COVID-19 from Twitter

The development of the internet is getting faster, participating in encouraging the emergence of new and innovative information. In filtering the various information that appears, we need a recommended system to perform well for users in today's internet era. A well-performing recommendation system in question is a reliable recommendation algorithm. This algorithm is fundamental to analyzing various information, such as responses on social media based on user behavior data related to the topic of COVID. This data is crawled from tweets on social media Twitter. The data analysis algorithm obtained uses Python, which is then visualized in the form of a diagram. The processed data is user comments on Twitter, and the text data is analyzed using Python, using more than 60000 data sets taken to form visualizations and conclusions. From sentiment analysis, polarity and subjectivity data are obtained to be analyzed, which are negative, neutral, or positive. The result is show positive tweets with 29.2%, negative tweets is 13%, and 57.8% neutral tweets. Lastly, sentiment analysis can help people effectively infer vast and complex data from social media like Twitter. © 2022 IEEE.

Cause of death: femicide

Labelled ‘the shadow pandemic' by UN Women, violence against women received considerable global public attention during 2020–21. Underpinning this moment of public concern, there lies a substantial history of efforts to document the nature of, and campaign against, the extent of violence against women globally. This is also the case in relation to femicide. Whilst we recognise that this is a contested term, for the purposes of this paper we use femicide to refer to the killing of women and girls because they are female by male violence. Femicide, as a death to be specifically counted in law only exists in a small number of jurisdictions. Where it is so recognised, primarily in South American countries as feminicidio, such deaths represent only the tip of the iceberg of such killings globally. This paper, in drawing on empirical data from a range of different sources (including administrative data, media analysis, and Femicide Observatory data) gathered throughout 2020, considers: what it means to call a death femicide, what implications might follow if all the deaths of women at the hands of men were counted as femicide, and the extent to which extraordinary times have any bearing on this kind of ordinary death. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

College television at German higher education institutions in the wake of the COVID-19 pandemic

Higher education institutions (HEIs) create a range of media products. Among them are college media produced by students. Even though this heterogeneous media form exists throughout Germany and is therefore part of HEIs' public visibility, it remains unnoticed in the field of higher education communication. This study aims to examine the specific type of college television (CTV) in terms of organizational and editorial structures and altered workflows due to the COVID-19 pandemic. The study combines a two-wave online survey among all operating German CTV stations in 2017 and 2021 with a qualitative social media analysis of twelve stations. In 2017, intra-curricular CTV operations rated a higher satisfaction level than extra-curricular cases, whereby the explicit support and cooperation with the HEI scores better. The data shows that CTV operations with an intra-curricular linkage to the respective HEI enjoyed a more stable continuity than extra-curricular operations, some of which were forced to cease production over the course of the COVID-19 pandemic. The pandemic has limited the CTV operations' workflows in terms of access to equipment and social exchange but has also stimulated a shift in topic selection and distribution strategy. © 2022, the author.