A Model for Classifying People at Risk of Diabetes Mellitus Using Social Media Analytics

Author(s):  
Soulakshmee D. Nagowah ◽  
Ravesh Joaheer
Diabetes ◽  
2018 ◽  
Vol 67 (Supplement 1) ◽  
pp. 1830-P ◽  
Author(s):  
GIAN PIO SORICE ◽  
ILARIA IMPROTA ◽  
TERESA MEZZA ◽  
SARA GRIONI ◽  
GIOVANNA MASONE IACOBUCCI ◽  
...  

2014 ◽  
Vol 35 (1) ◽  
pp. 7-43 ◽  
Author(s):  
Dick M. Carpenter ◽  
Jenifer Walsh Robertson ◽  
Michele E. Johnson ◽  
Scott Blum

2020 ◽  
Author(s):  
Jay Palmer ◽  
Kyle Revis ◽  
Yves Romain

2020 ◽  
Vol 9 (6) ◽  
pp. 413-422
Author(s):  
Muhammad H Mujammami ◽  
Abdulaziz A Alodhayani ◽  
Mohammad Ibrahim AlJabri ◽  
Ahmad Alhumaidi Alanazi ◽  
Sultan Sayyaf Alanazi ◽  
...  

Background: High prevalence of undiagnosed cases of diabetes mellitus (DM) has increased over the last two decades, most patients with DM only become aware of their condition once they develop a complication. Limited data are available regarding the knowledge and awareness about DM and the associated risk factors, complications and management in Saudi society. Aim: This study aimed to assess knowledge of DM in general Saudi society and among Saudi healthcare workers. Results: Only 37.3% of the participants were aware of the current DM prevalence. Obesity was the most frequently identified risk factor for DM. Most comparisons indicated better awareness among health workers. Conclusion: A significant lack of knowledge about DM in Saudi society was identified. Social media and educational curriculum can improve knowledge and awareness of DM.


2021 ◽  
pp. 089443932110122
Author(s):  
Dennis Assenmacher ◽  
Derek Weber ◽  
Mike Preuss ◽  
André Calero Valdez ◽  
Alison Bradshaw ◽  
...  

Computational social science uses computational and statistical methods in order to evaluate social interaction. The public availability of data sets is thus a necessary precondition for reliable and replicable research. These data allow researchers to benchmark the computational methods they develop, test the generalizability of their findings, and build confidence in their results. When social media data are concerned, data sharing is often restricted for legal or privacy reasons, which makes the comparison of methods and the replicability of research results infeasible. Social media analytics research, consequently, faces an integrity crisis. How is it possible to create trust in computational or statistical analyses, when they cannot be validated by third parties? In this work, we explore this well-known, yet little discussed, problem for social media analytics. We investigate how this problem can be solved by looking at related computational research areas. Moreover, we propose and implement a prototype to address the problem in the form of a new evaluation framework that enables the comparison of algorithms without the need to exchange data directly, while maintaining flexibility for the algorithm design.


Author(s):  
Sebastian Zhi Tao Khoo ◽  
Leong Hock Ho ◽  
Ee Hong Lee ◽  
Danston Kheng Boon Goh ◽  
Zehao Zhang ◽  
...  

2021 ◽  
Vol 9 (3) ◽  
pp. 232596712199005
Author(s):  
Jonathan S. Yu ◽  
James B. Carr ◽  
Jacob Thomas ◽  
Julianna Kostas ◽  
Zhaorui Wang ◽  
...  

Background: Social media posts regarding ulnar collateral ligament (UCL) injuries and reconstruction surgeries have increased in recent years. Purpose: To analyze posts shared on Instagram and Twitter referencing UCL injuries and reconstruction surgeries to evaluate public perception and any trends in perception over the past 3 years. Study Design: Cross-sectional study. Methods: A search of a 3-year period (August 2016 and August 2019) of public Instagram and Twitter posts was performed. We searched for >22 hashtags and search terms, including #TommyJohn, #TommyJohnSurgery, and #tornUCL. A categorical classification system was used to assess the sentiment, media format, perspective, timing, accuracy, and general content of each post. Post popularity was measured by number of likes and comments. Results: A total of 3119 Instagram posts and 267 Twitter posts were included in the analysis. Of the 3119 Instagram posts analyzed, 34% were from patients, and 28% were from providers. Of the 267 Twitter posts analyzed, 42% were from patients, and 16% were from providers. Although the majority of social media posts were of a positive sentiment, over the past 3 years, there was a major surge in negative sentiment posts (97% increase) versus positive sentiment posts (9% increase). Patients were more likely to focus their posts on rehabilitation, return to play, and activities of daily living. Providers tended to focus their posts on education, rehabilitation, and injury prevention. Patient posts declined over the past 3 years (–28%), whereas provider posts increased substantially (110%). Of posts shared by health care providers, 4% of posts contained inaccurate or misleading information. Conclusion: The majority of patients who post about their UCL injury and reconstruction on social media have a positive sentiment when discussing their procedure. However, negative sentiment posts have increased significantly over the past 3 years. Patient content revolves around rehabilitation and return to play. Although patient posts have declined over the past 3 years, provider posts have increased substantially with an emphasis on education.


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