scholarly journals Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0253300
Author(s):  
Md Shoaib Ahmed ◽  
Tanjim Taharat Aurpa ◽  
Md Musfique Anwar
Keyword(s):  
Social Media ◽  
Public Perceptions ◽  
Intervention Strategies ◽  
User Generated Content ◽  
Time Intervals ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Significant Public Health ◽  
Trending Topics ◽  
Twitter Users

COVID-19 caused a significant public health crisis worldwide and triggered some other issues such as economic crisis, job cuts, mental anxiety, etc. This pandemic plies across the world and involves many people not only through the infection but also agitation, stress, fret, fear, repugnance, and poignancy. During this time, social media involvement and interaction increase dynamically and share one’s viewpoint and aspects under those mentioned health crises. From user-generated content on social media, we can analyze the public’s thoughts and sentiments on health status, concerns, panic, and awareness related to COVID-19, which can ultimately assist in developing health intervention strategies and design effective campaigns based on public perceptions. In this work, we scrutinize the users’ sentiment in different time intervals to assist in trending topics in Twitter on the COVID-19 tweets dataset. We also find out the sentimental clusters from the sentiment categories. With the help of comprehensive sentiment dynamics, we investigate different experimental results that exhibit different multifariousness in social media engagement and communication in the pandemic period.

scholarly journals Twitter, Telepractice, and the COVID-19 Pandemic: A Social Media Content Analysis

2021 ◽  
pp. 1-11
Author(s):  
Kristen Weidner ◽  
Joneen Lowman ◽  
Anne Fleischer ◽  
Kyle Kosik ◽  
Peyton Goodbread ◽  
...  
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Technology Implementation ◽  
Scale Up ◽  
Media Content Analysis ◽  
Data Set ◽  
Implementation Framework ◽  
Health Crisis ◽  
Wide Scale ◽  
Twitter Users

Purpose Telepractice was extensively utilized during the COVID-19 pandemic. Little is known about issues experienced during the wide-scale rollout of a service delivery model that was novel to many. Social media research is a way to unobtrusively analyze public communication, including during a health crisis. We investigated the characteristics of tweets about telepractice through the lens of an established health technology implementation framework. Results can help guide efforts to support and sustain telehealth beyond the pandemic context. Method We retrieved a historical Twitter data set containing tweets about telepractice from the early months of the pandemic. Tweets were analyzed using a concurrent mixed-methods content analysis design informed by the nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework. Results Approximately 2,200 Twitter posts were retrieved, and 820 original tweets were analyzed qualitatively. Volume of tweets about telepractice increased in the early months of the pandemic. The largest group of Twitter users tweeting about telepractice was a group of clinical professionals. Tweet content reflected many, but not all, domains of the NASSS framework. Conclusions Twitter posting about telepractice increased during the pandemic. Although many tweets represented topics expected in technology implementation, some represented phenomena were potentially unique to speech-language pathology. Certain technology implementation topics, notably sustainability, were not found in the data. Implications for future telepractice implementation and further research are discussed.

scholarly journals Spam Detection Framework for Twitter using ML

2020 ◽  
Vol 9 (6) ◽  
pp. 216-219
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Bayes Theorem ◽  
Spam Detection ◽  
Bayes Classifier ◽  
Common Mode ◽  
Basic Framework ◽  
Trending Topics ◽  
Twitter Users ◽  
Promotional Messages

Spam has become one of the growing issues in social media websites. Some of the users in these websites creates spam news. Coming to twitter, Users inject tweets in trending topics and replies with promotional messages providing links. A large amount of spam has been noticied in twitter. It is necessary to identify these spams tweets in a twitter stream. Now a days ,a big part of people rely on content available in social media in their decisions, so detecting and deleting these spam details is very important. A basic framework is suggested to detect malicious account holders in twitter..At present to detect these spam users or accounts there are methods which are based on content based features, Graph based features. The system which is going to be created works on machine learning based algorithms. These algorithms help to give accurate results. In this system algorithm named Naïve Bayes classifier algorithm is going to be used. This algorithm is said to be combination of many other principles relyingupon “Bayes theorem” wherein the methods share a common mode of working.

scholarly journals Air Pollution Disasters: Liability Issues in Negligence Associated With the Provision of Personal Protective Interventions (Facemasks)

2020 ◽  
pp. 1-7
Author(s):  
Fiona McDonald ◽  
Claire J Horwell
Keyword(s):  
Public Health ◽  
Air Pollution ◽  
Health Risks ◽  
Duty Of Care ◽  
Protective Mechanism ◽  
Health Issues ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Significant Public Health ◽  
Legal Duty

ABSTRACT Disasters may impact air quality through the generation of high levels of potentially pathogenic particulate matter (PM), for example, in a volcanic eruption. Depending on the concentrations of particles in the air, their size and composition, and the duration of exposure, high levels of PM can create significant public health issues. It has been argued that air pollution, in and of itself, is a public health crisis. One possible intervention to reduce exposure to high levels of PM during an air pollution disaster (APD) is using facemasks. However, agencies may be reluctant to recommend or distribute facemasks for community use during APDs for a variety of reasons, including concerns about liability. There has been no analysis of these concerns. This paper analyzes whether agencies may have a legal duty of care in negligence to provide warnings about the health risks associated with APDs and/or to recommend facemasks as a protective mechanism for community use to reduce exposure to PM. It is also the first to examine the potential for liability in negligence, when a decision is made to distribute facemasks for community use during an APD and the receiver alleges that they sustained a personal injury and seeks compensation.

How to Quarantine False Information: What the U.S. Government May Do as False Information Spread on Social Media during a Public Health Crisis Becomes a Biosecurity Threat

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sonam James
Keyword(s):  
Public Health ◽  
Social Media ◽  
Hate Speech ◽  
Health And Safety ◽  
False Information ◽  
Safety Measures ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Global Spread ◽  
Public Health And Safety

Abstract With the global spread of the COVID-19 pandemic, misinformation about the pandemic spread prolifically on social media. False or harmful information about the coronavirus pandemic spread on social media included hate-speech, vaccine misinformation, and misinformation about public health and safety measures. In the midst of a serious public health crisis, where public cooperation for mandated health and safety measures hinges on trust in government and facts, false information rapidly spread through social media becomes a biosecurity threat. This article explores whether false or harmful information can be regulated during a serious public health emergency.

scholarly journals Impact of Unreliable Content on Social Media Users during COVID-19 and Stance Detection System

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 5
Author(s):  
Mudasir Ahmad Wani ◽  
Nancy Agarwal ◽  
Patrick Bours
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Detection System ◽  
Online Social Network ◽  
Negative Attitude ◽  
Health Crisis ◽  
Online Groups ◽  
Detection Model ◽  
Twitter Users ◽  
The Impact

The abundant dissemination of misinformation regarding coronavirus disease 2019 (COVID-19) presents another unprecedented issue to the world, along with the health crisis. Online social network (OSN) platforms intensify this problem by allowing their users to easily distort and fabricate the information and disseminate it farther and rapidly. In this paper, we study the impact of misinformation associated with a religious inflection on the psychology and behavior of the OSN users. The article presents a detailed study to understand the reaction of social media users when exposed to unverified content related to the Islamic community during the COVID-19 lockdown period in India. The analysis was carried out on Twitter users where the data were collected using three scraping packages, Tweepy, Selenium, and Beautiful Soup, to cover more users affected by this misinformation. A labeled dataset is prepared where each tweet is assigned one of the four reaction polarities, namely, E (endorse), D (deny), Q (question), and N (neutral). Analysis of collected data was carried out in five phases where we investigate the engagement of E, D, Q, and N users, tone of the tweets, and the consequence upon repeated exposure of such information. The evidence demonstrates that the circulation of such content during the pandemic and lockdown phase had made people more vulnerable in perceiving the unreliable tweets as fact. It was also observed that people absorbed the negativity of the online content, which induced a feeling of hatred, anger, distress, and fear among them. People with similar mindset form online groups and express their negative attitude to other groups based on their opinions, indicating the strong signals of social unrest and public tensions in society. The paper also presents a deep learning-based stance detection model as one of the automated mechanisms for tracking the news on Twitter as being potentially false. Stance classifier aims to predict the attitude of a tweet towards a news headline and thereby assists in determining the veracity of news by monitoring the distribution of different reactions of the users towards it. The proposed model, employing deep learning (convolutional neural network(CNN)) and sentence embedding (bidirectional encoder representations from transformers(BERT)) techniques, outperforms the existing systems. The performance is evaluated on the benchmark SemEval stance dataset. Furthermore, a newly annotated dataset is prepared and released with this study to help the research of this domain.

scholarly journals Arginase, a Possible Therapeutic Target in Prostate Cancer

2021 ◽  
pp. 202-207
Author(s):  
Subodhini Abhang ◽  
Prachi Shelgikar ◽  
Shruti Mulgund
Keyword(s):  
Prostate Cancer ◽  
Therapeutic Target ◽  
Substantial Reduction ◽  
Serum Levels ◽  
The Body ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Promising Therapeutic Target ◽  
Significant Public Health ◽  
Severity Of The Disease

With increasing populations, the prevalence of prostate cancer increases. In the future, a significant public health crisis can be recognized in the present incidence of prostate cancer. In order to counter this, markers should be established for the advanced diagnosis and treatment of the illness prognosis. The cells dominate our immune system and grow into a detectable tumour, causing cancer. At this stage in the body, several processes are dominated, governed and deregulated by the tumour. In most cases, immune response undertakes measures by limiting the availability of Arginine. In this context it is fascinating to examine how the levels of Arginine fluctuate with the severity of the disease and the levels of Arginase and NO. Substances and methods: In 25 beginning phases and 25 advanced stage of the prostate cancer patients and compared to 25 healthy controls, 5 ml of the blood were taken and tested for serum levels of Arginina, and nitric oxide. A substantial reduction in arginine (p<0.001) found was detected. In Arginase and levels a substantial increase (p<0.001) was detected. Conclusion: Increased Arginase levels are linked to the illness progression and the result lowers as Arginase uses most phases. Therefore, Arginase inhibition can be promising therapeutic target in prostate cancer.

COVID-19 and the Media in Nigeria

Glimpse ◽  
2021 ◽  
Vol 22 (2) ◽  
pp. 83-98
Author(s):  
Obiageli Pauline Ohiagu ◽  
Keyword(s):  
Public Health ◽  
Social Media ◽  
The Other ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Other Hand ◽  
The Media ◽  
The Impact

This chapter provides a Nigerian perspective to the global COVID-19 public health crisis that began in 2019. Two approaches were used to explain the impact of COVID-19 on the media in Nigeria and the effect of the latter on the spread/containment of the virus. The pandemic directly limited the operations of the media in many ways: socially, economically, and otherwise. On the other hand, both mainstream and social media was instrumental in curtailing the spread of COVID-19 through information, education, and infotainment.

Online mental health advocacy groups social support, stigma management and advocacy messaging, and audience reactions to this messaging

2019 ◽  
Author(s):  
◽  
Sarah Smith-Frigerio
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Peer Support ◽  
Advocacy Groups ◽  
Management Communication ◽  
Health Advocacy ◽  
Stigma Management ◽  
Health Crisis ◽  
Public Health Crisis ◽  
Mental Health Advocacy

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Mental health concerns continue to be stigmatized in traditional media, in spite of -- or perhaps contributing to --high prevalence rates of mental health diagnoses globally. This has led to the World Health Organization (WHO) declaring a public health crisis. Given stigmatization in traditional media, mental health communication scholars are investigating how mental health concerns are depicted and discussed in digital and social media spaces, but this area remains underexplored. The WHO has also outlined the importance of grassroots mental health advocacy groups in addressing the public health crisis, and an understanding of such groups' social media content is imperative. Through the theoretical lenses of information and resources, social support, advocacy, and stigma management communication, case studies of two grassroots mental health advocacy groups were conducted. Analysis of 200 social media posts, interviews with 5 content creators, and interviews with 15 users of the groups' social media feeds identified five major themes: providing information and resources through peer support, using stories, encouragement and connection to provide peer support, using peer support to foster advocacy work amongst users, progressing through stigma management communication strategies from accepting to challenging, and the importance of what is left unsaid. The theoretical and practical implications of these themes are discussed in the final chapter.

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