Identification of cohesive subgroups in a university hall of residence during the COVID-19 pandemic using a social network analysis approach

The impact and management of internet-based public opinion dissemination during emergencies: A case study of Baidu News during the first wave of coronavirus disease 2019 (COVID-19)

BACKGROUND AND AIMS

The coronavirus disease 2019 (COVID-19) public health emergency has had a huge impact worldwide. We analyzed news headlines and keywords from the initial period of COVID-19, and explored the dissemination timeline of news related to the epidemic, and the impact of Internet-based media on the public using lifecycle theory and agenda-setting theory. We aimed to explore the impact of Baidu news headlines on public attention during the first wave of COVID-19, as well as the management mechanism of regulatory departments for social public opinion.

METHODS

We searched Baidu News using the keywords "Novel Coronavirus" and "COVID-19" from 8 January to 21 February 2020, a total of 45 days, and used Python V3.6 to extract news samples during the first wave of the epidemic. We used text analysis software to structurally process captured news topics and content summaries, applied VOSviewer V6.19 and Ucinet V6.0 to examine key aspects of the data.

RESULTS

We analyzed the impact of Baidu News headlines on social opinion during the first wave of COVID-19 in the budding, spread, and outbreak stage of the information lifecycle. From clustering visualization and social network analysis perspectives, we explored the characteristics of Baidu News during the initial stage of the COVID-19. The results indicated that agenda-setting coverage through online media helped to mitigate the negative impact of COVID-19. The findings revealed that news reporting generated a high level of public attention toward a specific emergency event.

CONCLUSIONS

The public requires accurate and objective information on the progress of COVID-19 through Baidu News headlines to inform their planning for the epidemic. Meanwhile, government can enhance the management mechanism of news dissemination, correct false and inaccurate news, and guide public opinion in a positive direction. In addition, timely official announcements on the progress of the COVID-19 outbreak and responses to matters of public concern can help calm tensions and maintain social stability.

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

AIM

To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques.

METHODS

235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed.

RESULTS

After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained.

CONCLUSION

Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

A Social Network Analysis Approach to Evaluate the Relationship Between the Mobility Network Metrics and the COVID-19 Outbreak

The emergence of the new coronavirus in late 2019 further highlighted the human need for solutions to explore various aspects of deadly pandemics. Providing these solutions will enable humans to be more prepared for dealing with possible future pandemics. In addition, it helps governments implement strategies to tackle and control infectious diseases similar to COVID-19 faster than ever before. In this article, we used the social network analysis (SNA) method to identify high-risk areas of the new coronavirus in Iran. First, we developed the mobility network through the transfer of passengers (edges) between the provinces (nodes) of Iran and then evaluated the in-degree and page rank centralities of the network. Next, we developed 2 Poisson regression (PR) models to predict high-risk areas of the disease in different populations (moderator) using the mobility network centralities (independent variables) and the number of patients (dependent variable). The P-value of .001 for both prediction models confirmed a meaningful interaction between our variables. Besides, the PR models revealed that in higher populations, with the increase of network centralities, the number of patients increases at a higher rate than in lower populations, and vice versa. In conclusion, our method helps governments impose more restrictions on high-risk areas to handle the COVID-19 outbreak and provides a viable solution for accelerating operations against future pandemics similar to the coronavirus.

Leadership and contagion by COVID-19 among residence hall students: A social network analysis approach

University students have changed their behaviour due to the COVID-19 pandemic. In this paper, we describe the characteristics of PCR+ and PCR- nodes, analyse the structure, and relate the structure of student leaders to pandemic contagion as determined by PCR+ in 93 residential university students. Leadership comes from the male students of social science degrees who have PCR +, with an eigenvector centrality structure, β-centrality, and who are part of the bow-tie structure. There was a significant difference in β-centrality between leaders and non-leaders and in β-centrality between PCR+ and non-leaders. Leading nodes were part of the bow-tie structure. MR-QAP results show how residence and scientific branch were the most important factors in network formation. Therefore, university leaders should consider influential leaders, as they are vectors for disseminating both positive and negative outcomes.