HEALTH DEBATES ON SOCIAL MEDIA: LINKING DIGITAL HEALTH TO COMMUNICATION AND AI STUDIES

2020 ◽  
Author(s):  
Martina Skrubbeltrang Mahnke ◽  
Katrine Meldgaard Kjær
Keyword(s):  
Social Media ◽  
Communication Theory ◽  
Digital Health ◽  
Theoretical Perspective ◽  
Health Data ◽  
Digital Data ◽  
Analytical Strategies ◽  
Health Studies ◽  
Theoretical Question ◽  
Media Data

Drawing on the examples of three current health debates on Twitter revolving around the hashtags #medicalcannabis, #covid19 and #vaccinationervirker (in English: vaccinations work), this paper explores the broader theoretical question how we may expand the notion of ‘health data’ to include health debates and discussions on social media, and further how these can be linked to concepts of digital health data assemblages and communicative others. By combing insights from AI and communication studies and STS, as well as insights into the human-data relationship from digital health studies, the paper theoretically links digital data assemblages with communication theory which provides tools to think about health data as relational and communicative. With this, social media data becomes relevant in a new light, not only for media scientists, but also for understanding health practices in a digital age more generally. The paper discusses issues this theoretical perspective raises for researchers of social media and online health engagement; what challenges and possibilities this provides in relation to studying social media discussions on health; and finally, an overview of analytical strategies and empirical fields from which these perspectives may be studied.

scholarly journals How to use relevant data for maximal benefit with minimal risk: digital health data governance to protect vulnerable populations in low-income and middle-income countries

2019 ◽  
Vol 4 (2) ◽  
pp. e001395 ◽  
Author(s):  
Nicki Tiffin ◽  
Asha George ◽  
Amnesty Elizabeth LeFevre
Keyword(s):  
Vulnerable Populations ◽  
Low Income ◽  
Digital Health ◽  
Health Data ◽  
Digital Data ◽  
Data Governance ◽  
Data Breach ◽  
Middle Income ◽  
Middle Income Countries ◽  
Health Programmes

Globally, the volume of private and personal digital data has massively increased, accompanied by rapid expansion in the generation and use of digital health data. These technological advances promise increased opportunity for data-driven and evidence-based health programme design, management and assessment; but also increased risk to individuals of data misuse or data breach of their sensitive personal data, especially given how easily digital data can be accessed, copied and transferred on electronic platforms if the appropriate controls are not implemented. This is particularly pertinent in low-income and middle-income countries (LMICs), where vulnerable populations are more likely to be at a disadvantage in negotiating digital privacy and confidentiality given the intersectional nature of the digital divide. The potential benefits of strengthening health systems and improving health outcomes through the digital health environment thus come with a concomitant need to implement strong data governance structures and ensure the ethical use and reuse of individuals’ data collected through digital health programmes. We present a framework for data governance to reduce the risks of health data breach or misuse in digital health programmes in LMICS. We define and describe four key domains for data governance and appropriate data stewardship, covering ethical oversight and informed consent processes, data protection through data access controls, sustainability of ethical data use and application of relevant legislation. We discuss key components of each domain with a focus on their relevance to vulnerable populations in LMICs and examples of data governance issues arising within the LMIC context.

scholarly journals Invisible Data: A Framework for Understanding Visibility Processes in Social Media Data

2021 ◽  
Vol 7 (1) ◽  
pp. 205630512098447
Author(s):  
Christina Neumayer ◽  
Luca Rossi ◽  
David M. Struthers
Keyword(s):  
Social Media ◽  
Conceptual Framework ◽  
Human Interaction ◽  
Digital Data ◽  
Social Phenomena ◽  
Social Media Data ◽  
Four Dimensions ◽  
Social Visibility ◽  
Point Of Departure ◽  
Media Data

Social media data are increasingly used to study a variety of social phenomena. This development is based on the assumption that digital traces left on social media can provide insights into the nature of human interaction. In this research, we turn our attention to what remains invisible in research based on social media data. Using Andrea Brighenti’s work on “social visibility” as a point of departure, we unpack data invisibilities, as they are created within four dimensions: people and intentionality, technologies and tools, accessibility and form, and meaning and imaginaries. We introduce the notion of quasi-visible data as an intermediary between visible and invisible data highlighting the processual character of data invisibilities. With this conceptual framework, we contribute to developing a more reflective and ethical field of research into the study of social phenomena based on social media data. We conclude by arguing that distancing ourselves from the assumption that all social media data are visible and focusing on the invisible will enhance our understanding of digital data.

scholarly journals Epistemology, methods and theories of communication in the Big Data Era: a critical panorama of social media research

2018 ◽  
Vol 33 ◽  
pp. 167-181
Author(s):  
Ana Thereza Nogueira Soares
Keyword(s):  
Social Media ◽  
Big Data ◽  
Communication Theory ◽  
Point Of View ◽  
Analytical Models ◽  
Social Media Data ◽  
Data Collection And Analysis ◽  
Evolution Of Technology ◽  
Media Research ◽  
Media Data

This paper proposes a critical reflection on the epistemological, methodological and theoretical implications of the researches based on Big Data – especially on social media data – for the scientific field of communication. From an epistemological point of view, it reveals the unsustainability of analytical models based on static frameworks of communication, claiming that the sociasl processes that emerge with the influence of the internet are unequivocally presented in fluid and contingent formats. In this context, it highlights that the evolution of technology itself has the potential to boost the construction of data collection and analysis tools capable of grasping the communication movements, justifying the need for alignment between ontology, epistemology and methodology in scientific research. The text, also, poses questions about communication theory and its concepts. It is believed that the relevance acquired by data in recent years should not point to a domain of the empirical over the theoretical. Effectively, the strengthening of the communication science demands precision and care with the use of terms, models and theoretical references historically consolidated in the problematization and explanation of the contemporary.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

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