scholarly journals Studying the COVID-19 infodemic at scale

2021 ◽  
Vol 8 (1) ◽  
pp. 205395172110211
Author(s):  
Anatoliy Gruzd ◽  
Manlio De Domenico ◽  
Pier Luigi Sacco ◽  
Sylvie Briand
Keyword(s):  
Social Media ◽  
Big Data ◽  
Interdisciplinary Research ◽  
Leading Edge ◽  
Theme Issue ◽  
Misleading Information ◽  
Effective Strategies ◽  
Health Related

This special theme issue of Big Data & Society presents leading-edge, interdisciplinary research that focuses on examining how health-related (mis-)information is circulating on social media. In particular, we are focusing on how computational and Big Data approaches can help to provide a better understanding of the ongoing COVID-19 infodemic (overexposure to both accurate and misleading information on a health topic) and to develop effective strategies to combat it.

scholarly journals SOCIAL MEDIA AND FAKE NEWS ON CORONA VIRUS: A REVIEW OF LITERATURE

2020 ◽  
Vol 1 (2) ◽  
Author(s):  
Sandra Ukwuru ◽  
Prisca Nwankwo
Keyword(s):  
Social Media ◽  
21St Century ◽  
Research Agenda ◽  
Equal Opportunity ◽  
Review Of Literature ◽  
Fake News ◽  
Misleading Information ◽  
Corona Virus ◽  
Natural Home ◽  
Health Related

Social media is the 21st-century media that has given every user an equal opportunity to publish news without passing through any form of gatekeeping, editorial, or professional scrutiny. This means that it has become a natural home for the spread of fake news even on the recent coronavirus with its consequent health implications. The authors deployed available materials and literature to discuss the burning issues surrounding fake news as misleading information on social media, especially how social media has become a natural home for fake news on coronavirus. More so, this paper reviewed the literature on the effects of fake news on coronavirus and then motivations for sharing fake news online as a way to provide a start-off point for an understanding of why social media misinformation on Corona virus has spread.  The authors concluded by presenting a gap in literature, in addition to a research agenda for studies on the spread of health-related disinformation in Nigeria.

scholarly journals Integration of social media with healthcare big data for improved service delivery

2018 ◽  
Vol 20 (1) ◽  
Author(s):  
Sibulela Mgudlwa ◽  
Tiko Iyamu
Keyword(s):  
Social Media ◽  
Big Data ◽  
Service Delivery ◽  
Healthcare Delivery ◽  
Healthcare Services ◽  
Security And Privacy ◽  
Related Services ◽  
Health Related ◽  
Factors Of Influence ◽  
Use Of Social Media

Background: In the last decade, social media users across the world have crossed 1 billion, making it one of the fastest growing sources of big data. Also, people needing healthcare continue to increase in every society. Through accessibility, communication and interaction between health practitioners and patients, this type of ever-growing, social media subscriber–based platform can be of significant use in improving healthcare delivery to society. However, users encounter serious challenges in their attempts to make use of social media and big data for health-related services. The challenges are primarily caused by factors such as integration, complexity, security and privacy. The challenges are mainly owing to the sensitive nature of the healthcare environment, as a result of personalisation and privacy of information. Objectives: The objectives of the study were to examine and gain a better understanding of the complexities that are associated with the use of social media and healthcare big data, through influencing factors, and to develop a framework that can be used to improve health-related services to the patients. Methods: The interpretivist approach was employed, within which qualitative data were collected. This included documents and existing literature in the areas of social media and healthcare big data. To have a good spread of both previous and current state of events within the phenomena being studied, literature published between 2006 and 2016 were gathered. The data were interpretively analysed. Results: Based on the analysis of the data, factors of influence were found, which were used to develop a model. The model illustrates how the factors of influence can enable and at the same time constrain the use of social media for healthcare services. The factors were interpreted from which a framework was developed. The framework is intended to guide integration of social media with healthcare big data through which service delivery to patients can be improved. Conclusion: This study can be used to guide integration of social media with healthcare big data by health facilities in the communities. The study contributes to healthcare workers’ awareness on how social media can possibly be used to improve the services that they provide to the needy. Also, the study will benefit information systems and technologies and academic domains, particularly from the health services’ perspective.

scholarly journals Exploring Big Data Analytic Approaches to Cancer Blog Text Analysis (Preprint)

2018 ◽  
Author(s):  
Viju Raghupathi ◽  
Yilu Zhou ◽  
Wullianallur Raghupathi
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Word Association ◽  
Early Stage ◽  
Big Data Analytics ◽  
Free Text ◽  
Health Related ◽  
Data Analytic ◽  
Insight Into

BACKGROUND In recent years researchers have begun to realize the value of social media as a source for data that helps us understand health-related phenomena. Health blogs in particular are rich with information for decision-making. While there are web crawlers and blog analysis software that generate statistics related to blogs, these are relatively primitive and are not useful computationally to aid with the analysis and understanding of the social networks and medical blogs that are evolving around healthcare. There is a need for sophisticated tools to fill this gap. Furthermore, to our knowledge there are not many big data studies or applications in the text analytics of cancer blogs. This study attempts to fill this specific gap while analyzing cancer blogs. OBJECTIVE In this exploratory research, we examine the potential of applying big data analytic techniques to the analysis of blogs that exist in the cancer domain. Our objective is twofold: to extract from the blogs, patterns and insight about cancer diagnosis, treatment, and management; and to apply advanced computation techniques in processing large amounts of unstructured health data. METHODS We applied the big data analytics architecture of Hadoop MapReduce via the Cloudera platform to the analysis of cancer blog content, in order to extract patterns and insight on cancer diagnoses. We apply a series of algorithms to gain insight into the content and develop a vocabulary and taxonomy of keywords based on existing medical nomenclature. By applying a number of algorithms, we gained insight into the blog content. The study identifies, for instance, the most discussed topics as well as associations that relate to key phenomena RESULTS Using several text analytic algorithms, including word count, word association, clustering, and classification, we were able to identify and analyze the patterns and keywords in cancer blog postings. This gave insight into some of the key issues that are discussed in blogs such as the type of cancer (breast cancer being the dominant topic), diagnosis, treatments, and others. CONCLUSIONS In general, big data analytics has the potential to transform the way practitioners and researchers gain insight from health social media, especially those in free text, unstructured form. Big data analytics and applications in health-related social media are still at an early stage, and rapid acceleration is possible with the advancements in models, tools, and technologies.

scholarly journals Adverse Drug Event Discovery Using Biomedical Literature: A Big Data Neural Network Adventure (Preprint)

2017 ◽  
Author(s):  
Ahmad P Tafti ◽  
Jonathan Badger ◽  
Eric LaRose ◽  
Ehsan Shirzadi ◽  
Andrea Mahnke ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Big Data ◽  
Language Processing ◽  
Adverse Drug Events ◽  
Learning Strategy ◽  
Distributed Database ◽  
Biomedical Literature ◽  
Drug Event ◽  
Health Related

BACKGROUND The study of adverse drug events (ADEs) is a tenured topic in medical literature. In recent years, increasing numbers of scientific articles and health-related social media posts have been generated and shared daily, albeit with very limited use for ADE study and with little known about the content with respect to ADEs. OBJECTIVE The aim of this study was to develop a big data analytics strategy that mines the content of scientific articles and health-related Web-based social media to detect and identify ADEs. METHODS We analyzed the following two data sources: (1) biomedical articles and (2) health-related social media blog posts. We developed an intelligent and scalable text mining solution on big data infrastructures composed of Apache Spark, natural language processing, and machine learning. This was combined with an Elasticsearch No-SQL distributed database to explore and visualize ADEs. RESULTS The accuracy, precision, recall, and area under receiver operating characteristic of the system were 92.7%, 93.6%, 93.0%, and 0.905, respectively, and showed better results in comparison with traditional approaches in the literature. This work not only detected and classified ADE sentences from big data biomedical literature but also scientifically visualized ADE interactions. CONCLUSIONS To the best of our knowledge, this work is the first to investigate a big data machine learning strategy for ADE discovery on massive datasets downloaded from PubMed Central and social media. This contribution illustrates possible capacities in big data biomedical text analysis using advanced computational methods with real-time update from new data published on a daily basis.

Trend of Parental Refusal or Delay of Childhood Vaccinations

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
Meagan Marie Daoust
Keyword(s):  
Public Health ◽  
Social Media ◽  
Healthcare System ◽  
Educational Context ◽  
Health Consequences ◽  
Missed Opportunities ◽  
The Public ◽  
Health Related ◽  
Parental Refusal

The healthcare trend of parental refusal or delay of childhood vaccinations will be investigated through a complex Cynefin Framework component in an economic and educational context, allowing patterns to emerge that suggest recommendations of change for the RN role and healthcare system. As a major contributing factor adding complexity to this trend, social media is heavily used for health related knowledge, making it is difficult to determine which information is most trustworthy. Missed opportunities for immunization can result, leading to economic and health consequences for the healthcare system and population. Through analysis of the powerful impact social media has on this evolving trend and public health, an upstream recommendation for RNs to respond with is to utilize reliable social media to the parents’ advantage within practice. The healthcare system should focus on incorporating vaccine-related education into existing programs and classes offered to parents, and implementing new vaccine classes for the public.

DYNAMIC EXTRACTION AND ANALYTICS OF BIG DATA FROM CLOUD AND SOCIAL MEDIA INTEGRATED PLATFORMS

2020 ◽  
Vol 9 (6) ◽  
pp. 3703-3711
Author(s):  
N. Oberoi ◽  
S. Sachdeva ◽  
P. Garg ◽  
R. Walia
Keyword(s):  
Social Media ◽  
Big Data ◽  
Dynamic Extraction

Measuring Relevant Information in Health Social Network Conversations and Clinical Diagnosis Cases (Preprint)

2018 ◽  
Author(s):  
Albert Moreira ◽  
Raul Alonso-Calvo ◽  
Alberto Muñoz ◽  
Jose Crespo
Keyword(s):  
Social Media ◽  
Social Network ◽  
Relevant Information ◽  
Online Discussions ◽  
Biomedical Knowledge ◽  
Expert Opinions ◽  
Health Related ◽  
Health Social Network ◽  
Discussion System ◽  
Clinical Cases

BACKGROUND Internet and Social media is an enormous source of information. Health Social Networks and online collaborative environments enable users to create shared content that afterwards can be discussed. While social media discussions for health related matters constitute a potential source of knowledge, characterizing the relevance of participations from different users is a challenging task. OBJECTIVE The aim of this paper is to present a methodology designed for quantifying relevant information provided by different participants in clinical online discussions. METHODS A set of key indicators for different aspects of clinical conversations and specific clinical contributions within a discussion have been defined. These indicators make use of biomedical knowledge extraction based on standard terminologies and ontologies. These indicators allow measuring the relevance of information of each participant of the clinical conversation. RESULTS Proposed indicators have been applied to two discussions extracted from PatientsLikeMe, as well as to two real clinical cases from the Sanar collaborative discussion system. Results obtained from indicators in the tested cases have been compared with clinical expert opinions to check indicators validity. CONCLUSIONS The methodology has been successfully used for describing participant interactions in real clinical cases belonging to a collaborative clinical case discussion tool and from a conversation from a Health Social Network.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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