scholarly journals Risks associated with antiretroviral treatment for human immunodeficiency virus (HIV): qualitative analysis of social media data and health state utility valuation

2017 ◽  
Vol 26 (7) ◽  
pp. 1785-1798
Author(s):  
Louis S. Matza ◽  
Karen C. Chung ◽  
Katherine J. Kim ◽  
Trena M. Paulus ◽  
Evan W. Davies ◽  
...  
Keyword(s):  
Social Media ◽  
Human Immunodeficiency Virus ◽  
Qualitative Analysis ◽  
Antiretroviral Treatment ◽  
Health State Utility ◽  
Health State ◽  
Social Media Data ◽  
Immunodeficiency Virus ◽  
Media Data

scholarly journals Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

10.2196/18700 ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. e18700 ◽  
Author(s):  
Jiawei Li ◽  
Qing Xu ◽  
Raphael Cuomo ◽  
Vidya Purushothaman ◽  
Tim Mackey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Hubei Province ◽  
Wuhan City ◽  
Social Media Data ◽  
Public Reaction ◽  
Media Data ◽  
Chinese Social Media

Background The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. Objective The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. Methods Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. Results A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. Conclusions The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

scholarly journals Analyzing social media data: A mixed-methods framework combining computational and qualitative text analysis

2019 ◽  
Author(s):  
Matthew Andreotta ◽  
Robertus Nugroho ◽  
Mark Hurlstone ◽  
Fabio Boschetti ◽  
Simon Farrell ◽  
...  
Keyword(s):  
Social Media ◽  
Qualitative Analysis ◽  
Data Science ◽  
Large Data ◽  
Extraction Process ◽  
Data Set ◽  
Diverse Range ◽  
Social Media Data ◽  
Qualitative Thematic Analysis ◽  
Media Data

To qualitative researchers, social media offers a novel opportunity to harvest a massive and diverse range of content, without the need for intrusive or intensive data collection procedures. However, performing a qualitative analysis across a massive social media data set is cumbersome and impractical. Instead, researchers often extract a subset of content to analyze, but a framework to facilitate this process is currently lacking. We present a four-phased framework for improving this extraction process, which blends the capacities of data science techniques to compress large data sets into smaller spaces, with the capabilities of qualitative analysis to address research questions. We demonstrate this framework by investigating the topics of Australian Twitter commentary on climate change, using quantitative (Non-Negative Matrix inter-joint Factorization; Topic Alignment) and qualitative (Thematic Analysis) techniques. Our approach is useful for researchers seeking to perform qualitative analyses of social media, or researchers wanting to supplement their quantitative work with a qualitative analysis of broader social context and meaning.

scholarly journals Health State Utilities of Risks Associated with Antiretroviral Treatment for Human Immunodeficiency Virus (Hiv)

2015 ◽  
Vol 18 (7) ◽  
pp. A590
Author(s):  
LS Matza ◽  
KC Chung ◽  
KJ Kim ◽  
T Paulus ◽  
EW Davies ◽  
...  
Keyword(s):  
Human Immunodeficiency Virus ◽  
Antiretroviral Treatment ◽  
Health State ◽  
Immunodeficiency Virus ◽  
Health State Utilities

scholarly journals Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study (Preprint)

2020 ◽  
Author(s):  
Jiawei Li ◽  
Qing Xu ◽  
Raphael Cuomo ◽  
Vidya Purushothaman ◽  
Tim Mackey
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Quantitative Analysis ◽  
Qualitative Analysis ◽  
Hubei Province ◽  
Wuhan City ◽  
Social Media Data ◽  
Public Reaction ◽  
Media Data ◽  
Chinese Social Media

BACKGROUND The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak. OBJECTIVE The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak. METHODS Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics. RESULTS A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (<i>P</i>&lt;.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors. CONCLUSIONS The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

scholarly journals Understanding Students’ Learning Experiences through Mining User-Generated Contents on Social Media

2017 ◽  
Vol 33 (2) ◽  
Author(s):  
Oanh Tran
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Qualitative Analysis ◽  
Natural Science ◽  
Negative Emotion ◽  
Learning Experiences ◽  
Social Media Data ◽  
Data Volume ◽  
Timely Fashion ◽  
Media Data

This paper presents a work of mining informal social media data to provide insights into students’ learning experiences. Analyzing such kind of data is a challenging task because of the data volume, the complexity and diversity of languages used in these social sites. In this study, we developed a framework which integrating both qualitative analysis and different data mining techniques in order to understand students’ learning experiences. This is the first work focusing on mining Vietnamese forums for students in natural science fields to understand issues and problems in their education. The results indicated that these students usually encounter problems such as heavy study load, sleepy problem, negative emotion, English barriers, and carreers’ targets. The experimental results are quite promising in classifying students’ posts into predefined categories developed for academic purposes. It is expected to help educational managers get necessary information in a timely fashion and then make more informed decisions in supporting their students in studying.

Post, Mine, and Be Disturbed: Social Media Data Mining

PsycCRITIQUES ◽  
2016 ◽  
Vol 61 (51) ◽  
Author(s):  
Daniel Keyes
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Social Media Data ◽  
Media Data
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