Paper Example. Social Media Surveillance

What is the impact and efficacy of determining individuals' nature and health status using social media surveillance sites such as Facebook? Before exploring the nature and health status of individuals using media social surveillance, it is crucial to learn to define the same term, social media surveillance to understand the impact it creates at individuals' levels as much as health status is concerned. Social media surveillance is defined as an approach that involves collections of personal data pulled or extracted from numerous social media platforms, including Facebook and Twitter, and many more, using automated technology in most cases to allow real-time aggregation, organization, and analysis of metadata and content to conclude. Social media surveillance is not only a refreshing approach used by research to demonstrate and obtain individuals' health status alone but also the nature and lifestyles of people, including organizational workers if the need arises. The assignment broadly analyses developed research questions that seek to understand the impact and efficacy of social determining individuals' nature and health status using social media surveillance sites by summarizing five entry sources with interest in the selected topic.

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Rezaallah, B., Lewis, D. J., Pierce, C., Zeilhofer, H. F., & Berg, B. I. (2019). Social media surveillance of multiple sclerosis medications used during pregnancy and breastfeeding: a content analysis. Journal of medical Internet research, 21(8), e13003. https://www.jmir.org/2019/8/e13003/Rezaallah et al. (2019) examine social media surveillance of multiple sclerosis medications used during pregnancy and breastfeeding. The article focuses on a given sample of individuals, women of childbearing age. Additionally, social media surveillance is made viable in this section as the article demonstrates the health status of women of childbearing age in the context of multiple sclerosis. It is divided into three essential parts: objectives, methods, and results of social media surveillance. In the first section, the article's goal, the study analyzed the content of various posts about pregnancy and the use of medicines in different online forums (Rezaallah et al., 2019). These discussions shared within online forums with intentions of learning medications at the pregnancy level explains the nature and impact of social media surveillance in determining individuals' health status. Since such a study aimed to explore a thorough understanding of patients' experience, especially those with multiple sclerosis, it demonstrates or suggests the impact social media surveillance creates mostly in the context of individuals' nature and health status.

The second part of the article reviews methods used in specified social media surveillance study. Twenty-one different posts from numerous discussion posts were used or collected. Mentioned posts were identified within a specified period, 2015 and 2016, and content analyzed using the content analysis technique (Rezaallah et al., 2019). The third part or section of the article focuses on results. Within this section of the shared article, six posts relating to multiple sclerosis were identified. Eighty percent of participants acknowledged having personal experiences with multiple sclerosis. It demonstrates the efficacy of determining individuals' experiences and health status using social media surveillance, among many results shared within the article presented.

Amir, S., Dredze, M., & Ayers, J. W. (2019, June). Mental health surveillance over social media with digital cohorts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology (pp. 114-120). https://www.aclweb.org/anthology/W19-3013.pdfAmir et al. (2019) demonstrate mental health surveillance over social media with a digital cohort. The article shared that relates to social media surveillance presents three essential sections within the papers, including mental classifiers used in the context of identified mental condition, analysis, and discussion, all suggesting or showing the impact and efficacy of determining individuals' nature and health status in the context of social media surveillance. In the context of mental classifiers, the study employed a supervised model for mental health inference over social media, twitter being one of the social media identified. The diseases examined in the study include depression and post-traumatic stress disorders and classified using the self-reported datasets. It determines an integral part of the research study that explains or demonstrates the impact and efficacy in determining the nature and health status, especially the mental state of individuals using social media surveillance. As initially introduced, the surveillance was specified on a given social media site which is twitter. There were 327 users with a diagnosed history of depression and 246 users with post-traumatic stress disorders (Amir et al., 2019). It discloses the role social media surveillance play in determining individuals' nature and status of health.

In the context of analysis, the study using Twitter users and the aforementioned demographic interference pipeline processed the developed cohort through the mental-health classifiers to show depression of depression and post-traumatic stress disorder (Amir et al., 2019). It is a crucial part of the article and study to demonstrate the prevalence of the mental condition among twitter users as it explains the impact of social media prevalence in determining or describing individuals' nature and mental status. In the result shared, about 30.25 percent of members within the shared cohort had the highest level of developing or suffering from depression, all managed or derived from medial social surveillance (Amir et al., 2019).

Shah, Z., Surian, D., Dyda, A., Coiera, E., Mandl, K. D., & Dunn, A. G. (2019). Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study. Journal of medical Internet research, 21(11), e14007. https://www.jmir.org/2019/11/e14007Shah et al. (2019) examine automatically appraising the credibility of vaccine-related web pages shared on social media in the context of social media surveillance. It explains or demonstrates the impact of social media surveillance at this time; it focuses on the crucial part of social media pages that appraise the credibility of the vaccine-related webpage. It is organized into four sections, three of which are a vital part. Some of the main notable sections include objective, methods, and results working hand in hand in demonstrating the impact and efficacy of social media surveillance (Shah et al., 2019). In the context of objective or goal shared within the article, the study estimating proportions of vaccines related to twitter posts and relation shared posts has a low credibility webpage. It is a crucial section of the survey exploited by the social media surveillance approach that has the potential of determining an individual’s nature of health based on information they obtain from shared webpages (Shah et al., 2019). Such an approach associated with social media surveillance is crucial individuals’ health hence determines or demonstrate its impact. In the case of results shared, the study indicates that a low credibility page in most cases tends to reach fewer individuals or users compared to higher credibility sources. It covers an integral part of social media surveillance as it defines the essential part of vaccines.

Müller, M. M., & Salathé, M. (2019). Crowd-breaks: Tracking health trends using public social media data and crowdsourcing. Frontiers in public health, 7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476276/Muller & Salathe (2019) examines crowd-breaks, tracking health trends using public social media data and crowdsourcing. It is an essential part and one of the social media surveillance approaches that create or demonstrate the impact of tracking health trends using public social media. It is divided into four sections, including methods and tools, streaming pipeline, user interface, sentiment analysis, results and discussion, all demonstrating the essentials of social media surveillance as connected health trends (Muller & Salathe 2019). In the case of discussion, among other essential sections shared within the article explain how social media surveillance enables the detection of the health trends, especially in the case where individuals’ habits such as the boycott of vaccination programs may lead to disease outbreaks (Muller & Salathe 2019). It explains the impact of social media surveillance as connected to individuals’ health and disease.

Byrne, J., Kirwan, G., & Mc Guckin, C. (2019). Social Media Surveillance in Social Work: Practice Realities and Ethical Implications. Journal of Technology in Human Services, 37(2-3), 142-158. http://mural.maynoothuniversity.ie/11188/1/GK_Social_2019.pdf The article Byrne et al. (2019) shares the impact of social media surveillance in social work, practice realities and ethical implications. It is divided into numerous essential sections, which include ethical dilemmas, methodology, findings where social pressure as one of the crucial elements is determined, and discussion, among other vital parts. Generally, the article exploits critical sections of social media practice in the context of workers certifying the use of social media surveillance in the context of developing essential findings that broadly involve people, including organizational workers (Byrne et al., 2019). In the context of ethical dilemmas, the report indicates or outlines various conduct requirements in line with the use of social media. It includes responsibly using social media by adopting professional standards and considering the possible impact on service users, among other outlines (Byrne et al., 2019). Each of the shared conduct requirements demonstrates the impact of social media surveillance in the context of communications and individuals’ ethical standards.

References

Amir, S., Dredze, M., & Ayers, J. W. (2019, June). Mental health surveillance over social media with digital cohorts. In Proceedings of the Sixth Workshop on Computational Linguistics and Clinical Psychology (pp. 114-120). https://www.aclweb.org/anthology/W19-3013.pdf

Byrne, J., Kirwan, G., & Mc Guckin, C. (2019). Social Media Surveillance in Social Work: Practice Realities and Ethical Implications. Journal of Technology in Human Services, 37(2-3), 142-158. http://mural.maynoothuniversity.ie/11188/1/GK_Social_2019.pdf

Müller, M. M., & Salathé, M. (2019). Crowd-breaks: Tracking health trends using public social media data and crowdsourcing. Frontiers in public health, 7. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6476276/

Rezaallah, B., Lewis, D. J., Pierce, C., Zeilhofer, H. F., & Berg, B. I. (2019). Social media surveillance of multiple sclerosis medications used during pregnancy and breastfeeding: a content analysis. Journal of medical Internet research, 21(8), e13003. https://www.jmir.org/2019/8/e13003/

Shah, Z., Surian, D., Dyda, A., Coiera, E., Mandl, K. D., & Dunn, A. G. (2019). Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study. Journal of medical Internet research, 21(11), e14007. https://www.jmir.org/2019/11/e14007