Social Media Data Relevant for Measuring Key Performance Indicators? A Content Analysis Approach

The aim of this study is to explain government officials’ communication and coordination intensity on twitter social media while handling the Covid-19 pandemic in Indonesia. This research uses a qualitative content analysis approach towards the official Indonesian government official’s account. The result showed a developed communication and intensive coordination between President Jokowi and the team, in attempt to properly accelerate the handling process. Furthermore, this activity was also achieved with a number of governors. The presidency aimed to build the commitment of central and local government officials, and jointly support the policy implementation to properly manage Covid-19. These communication and coordination activities positively impacted on the high attention of local governments to accelerate the handling in a number of regions. However, the study limitations include the use of Twitter social media data, characterized by the inability to reveal performance of government officials. Therefore, subsequent research is expected to adopt a triangulation analysis approach to data on twitter social media, online media, official government reports, and information from trends in Indonesian cases. Received: 7 August 2020 / Accepted: 11 February 2021 / Published: 10 May 2021

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

JMIR Public Health and Surveillance ◽

10.2196/18700 ◽

2020 ◽

Vol 6 (2) ◽

pp. e18700 ◽

Cited By ~ 31

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.

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Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions

Sustainability ◽

10.3390/su11185070 ◽

2019 ◽

Vol 11 (18) ◽

pp. 5070 ◽

Cited By ~ 3

Author(s):

Yuguo Tao ◽

Feng Zhang ◽

Chunyun Shi ◽

Yun Chen

Keyword(s):

Machine Learning ◽

Social Media ◽

Content Analysis ◽

Air Quality ◽

Sentiment Analysis ◽

Social Media Data ◽

Sina Weibo ◽

Emotion Words ◽

Tourist Destinations ◽

Media Data

Analyzing tourists’ perceptions of air quality is of great significance to the study of tourist experience satisfaction and the image construction of tourism destinations. In this study, using the web crawler technique, we collected 27,500 comments regarding the air quality of 195 of China’s Class 5A tourist destinations posted by tourists on Sina Weibo from January 2011 to December 2017; these comments were then subjected to a content analysis using the Gooseeker, ROST CM (Content Mining System) and BosonNLP (Natural Language Processing) tools. Based on an analysis of the proportions of sentences with different emotional polarities with ROST EA (Emotion Analysis), we measured the sentiment value of texts using the artificial neural network (ANN) machine learning method implemented through a Chinese social media data-oriented Boson platform based on the Python programming language. The content analysis results indicated that in the adaption stage in Sina Weibo, tourists’ perceptions of air quality were mainly positive and had poor air pollution crisis awareness. Objective emotion words exhibited a similarly high proportion as subjective emotion words, indicating that taking both objective and subjective emotion words into account simultaneously helps to comprehensively understand the emotional content of the comments. The sentiment analysis results showed that for the entire text, sentences with positive emotions accounted for 85.53% of the total comments, with a sentiment value of 0.786, which belonged to the positive medium level; the direction of the temporal “up-down-up” changes and the spatial pattern of high in the south and low in the north (while having little difference between the east and the west) were basically consistent with reality. A further exploration of the theoretical basis of the semi-supervised ANN approach or the introduction of other machine learning methods using different data sources will help to analyze this phenomenon in greater depth. The paper provides evidence for new data and methods for air quality research in tourist destinations and provides a new tool for air quality monitoring.

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Exploring the usage of social media in extant campus sustainability assessment frameworks for sustainable campus development

International Journal of Sustainability in Higher Education ◽

10.1108/ijshe-03-2021-0091 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Yusuf A. Adenle ◽

Mohammed Abdul-Rahman ◽

Oluwole A. Soyinka

Keyword(s):

Social Media ◽

Content Analysis ◽

Environmental Sustainability ◽

Sustainability Indicators ◽

Fair Use ◽

Social Media Data ◽

Content Type ◽

Tertiary Institutions ◽

Campus Sustainability ◽

Media Data

Purpose As one of the buzzwords in the present age with considerable impacts in tertiary institutions, social media use in online teaching, learning and information dissemination have been extensively discussed in extant literature. This paper aims to explore the existing campus sustainability appraisal (CSA) tools to identify the length at which social media has been used, especially in environmental sustainability indicators’ selection and empirical verification. Design/methodology/approach The methodology is mainly based on a desktop study involving comprehensive review and content analysis of existing CSA tools’ documents. Webpage content analysis of selected sustainability monitoring and tracking system in higher education institutions was also conducted. Findings The tools' content analysis reveals insufficient utilization of social media data and platforms in campus sustainability environmental-dimension indicators selection. To bridge this identified research gap, social media user-generated content for appraising the campus-wide environmental sustainability indicators preference in tertiary institutions was proposed. Practical implications The adoption and modification of this study’s proposed approach by tertiary institutions, especially in sub-Saharan African countries, could help address most campus-wide environmental challenges raised, commented on and discussed on social media. Originality/value This study contributes to knowledge gaps by revealing the extent of social media utilization in extant tools. With the expanding utilization of different social media platforms by various tertiary institutions worldwide, their administrators' responsibility is to put these social media data into fair use.

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

10.2196/preprints.18700 ◽

2020 ◽

Cited By ~ 8

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><.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.

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