scholarly journals Identifying student opinion leaders to lead e-cigarette interventions: protocol for a randomized controlled pragmatic trial

Trials ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kar-Hai Chu ◽  
Sara Matheny ◽  
Alexa Furek ◽  
Jaime Sidani ◽  
Susan Radio ◽  
...  
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Pragmatic Trial ◽  
Opinion Leader ◽  
Opinion Leaders ◽  
Student Opinion ◽  
Randomized Controlled ◽  
School Based ◽  
Peer Leader

Abstract Background After the US Surgeon General declared youth electronic cigarette (e-cigarette) use an epidemic in 2018, the number of youth e-cigarette users continued to surge, growing from 3.8 million in 2018 to over 5 million 2019. Youth who use e-cigarettes are at a substantially higher risk of transitioning to traditional cigarettes, becoming regular cigarette smokers, and increasing their risk of developing tobacco-related cancer. A majority of youth are misinformed about e-cigarettes, often believing they are not harmful or contain no nicotine. Middle school students using e-cigarettes have been affected by its normalization leading to influence by their peers. However, social and group dynamics can be leveraged for a school-based peer-led intervention to identify and recruit student leaders to be anti-e-cigarette champions to prevent e-cigarette initiation. This study outlines a project to use social network analysis to identify student opinion-leaders in schools and train them to conduct anti-e-cigarette programming to their peers. Methods In the 2019–2020 academic school year, 6th grade students from nine schools in the Pittsburgh area were recruited. A randomized controlled trial (RCT) was conducted with three arms—expert, elected peer-leader, and random peer-leader—for e-cigarette programming. Sixth grade students in each school completed a network survey that assessed the friendship networks in each class. Students also completed pre-intervention and post-intervention surveys about their intention-to-use, knowledge, and attitudes towards e-cigarettes. Within each peer-led arm, social network analysis was conducted to identify peer-nominated opinion leaders. An e-cigarette prevention program was administered by (1) an adult content-expert, (2) a peer-nominated opinion leader to assigned students, or (3) a peer-nominated opinion leader to random students. Discussion This study is the first to evaluate the feasibility of leveraging social network analysis to identify 6th grade opinion leaders to lead a school-based e-cigarette intervention. Trial registration ClinicalTrials.gov NCT04083469. Registered on September 10, 2019.

scholarly journals Using Social Network Analysis and Social Capital to Identify User Roles on Polarized Political Conversations on Twitter

2019 ◽  
Vol 5 (2) ◽  
pp. 205630511984874 ◽  
Author(s):  
Raquel Recuero ◽  
Gabriela Zago ◽  
Felipe Soares
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Opinion Leaders ◽  
Information Flows ◽  
Network Metrics ◽  
Network Analysis Metrics

In this article, we discuss the roles users play in political conversations on Twitter. Our case study is based on data collected in three dates during the former Brazilian president Lula’s corruption trial. We used a combination of social network analysis metrics and social capital to identify the users’ roles during polarized discussions that took place in each of the dates analyzed. Our research identified four roles, each associated with different aspects of social capital and social network metrics: activists, news clippers, opinion leaders, and information influencers. These roles are particularly useful to understand how users’ actions on political conversations may influence the structure of information flows.

scholarly journals Exploring Crisis Communication and Information Dissemination on Social Media: Social Network Analysis of Hurricane Irma Tweets

2020 ◽  
Vol 3 (2) ◽  
pp. 179-210 ◽  
Author(s):  
Xianlin Jin
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Crisis Communication ◽  
Information Diffusion ◽  
Information Dissemination ◽  
Opinion Leader ◽  
Communication Model ◽  
Hurricane Irma

This study utilized social network analysis to identify the top 10 Twitter influentials during the Hurricane Irma crisis period and examined the relationship between social media attributes and the bridge influence of controlling information flow. The number of a user’s followers and tweets significantly predicted one’s control of information. Crisis information tended to be shared in scattered subgroups. Social network boundaries impeded information diffusion, and the communication pattern was largely one-way. The findings partially supported the opinion leader argument while indicating that influentials can directly generate information, which is consistent with the social-mediated crisis communication model. Such findings will contribute to crisis literature and help emergency management professionals advance social media usage to disseminate crisis information, build effective communication, and provide immediate disaster relief responses

scholarly journals Identification of Key Opinion Leaders in Pharmaceutics Using Network Analysis

2021 ◽  
pp. 01-05
Author(s):  
Prof. Anuja Phapale ◽  
Sarthak Kulkarni ◽  
Pritam Bagad ◽  
Hrishikesh Joshi ◽  
Himanshu Randad
Keyword(s):  
Social Network ◽  
Network Analysis ◽  
Life Science ◽  
Opinion Leader ◽  
Opinion Leaders ◽  
Centrality Measures ◽  
Pubmed Central ◽  
Key Opinion Leaders ◽  
Key Opinion Leader ◽  
The Web

The term Key Opinion Leader in marketing is not new. Key Opinion Leaders (KOLs) commonly known as thought leaders who play a crucial role in the life science industry. We through this project intend to implement the concept of identifying key opinion leaders using weighted Social Network Analysis (SNA). We intend to use European PubMed Central dataset for creating a weighted social Network of authors who have healthcare and medicine related publications and apply different centrality measures on it. In order to collect the data, we will be using one of the web scraping methods and predefined libraries like scrapy. After fetching and processing the data we intend to form a network of authors using python’s NetworkX library. This network will then be subjected to various centrality measures which in turn will give prominent opinion leaders as the output.

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