Who Would Respond to A Troll? A Social Network Analysis of Reactions to Trolls in Online Communities

2021 ◽  
pp. 106786
Author(s):  
Qiusi Sun ◽  
Cuihua Shen
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Communities

Using Social Network Analysis to Guide Theoretical Sampling in an Ethnographic Study of a Virtual Community

2011 ◽  
pp. 157-174 ◽  
Author(s):  
Enrique Murillo
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Communities ◽  
Online Community ◽  
Virtual Community ◽  
Ethnographic Study ◽  
Qualitative Studies ◽  
The Social ◽  
Theoretical Sampling

Social Network Analysis (SNA) provides a range of models particularly well suited for mapping bonds between participants in online communities and thus reveal prominent members or subgroups. This can yield valuable insights for selecting a theoretical sample of participants or participant interactions in qualitative studies of communities. This chapter describes a procedure for collecting data from Usenet newsgroups, deriving the social network created by participant interaction, and importing this relational data into SNA software, where various cohesion models can be applied. The technique is exemplified by performing a longitudinal core periphery analysis of a specific newsgroup, which identified core members and provided clear evidence of a stable online community. Discussions dominated by core members are identified next, to guide theoretical sampling of text-based interactions in an ongoing ethnography of the community.

scholarly journals Exploring Behavior of People with Suicidal Ideation in a Chinese Online Suicidal Community

2018 ◽  
Vol 16 (1) ◽  
pp. 54 ◽  
Author(s):  
Zheng Wang ◽  
Guang Yu ◽  
Xianyun Tian
Keyword(s):  
Social Media ◽  
Social Network ◽  
Social Network Analysis ◽  
Suicidal Ideation ◽  
Network Analysis ◽  
Online Communities ◽  
Chinese Women ◽  
Intervention Strategies ◽  
Effective Prevention ◽  
Social Media Platforms

People with suicidal ideation (PSI) are increasingly using social media to express suicidal feelings. Researchers have found that their internet-based communication may lead to the spread of suicidal ideation, which presents a set of challenges for suicide prevention. To develop effective prevention and intervention strategies that can be efficiently applied in online communities, we need to understand the behavior of PSI in internet-based communities. However, to date there have been no studies that specifically focus on the behavior of PSI in Chinese online communities. A total of 4489 postings in which users explicitly expressed their suicidal ideation were labeled from 560,000 postings in an internet-based suicidal community on Weibo (one of the biggest social media platforms in China) to explore their behavior. The results reveal that PSI are significantly more active than other users in the community. With the use of social network analysis, we also found that the more frequently users communicate with PSI, the more likely that users would become suicidal. In addition, Chinese women may be more likely to be at risk of suicide than men in the community. This study enriches our knowledge of PSI’s behavior in online communities, which may contribute to detecting and assisting PSI on social media.

scholarly journals Network Analysis of Political Internet Communities: from Formalized to «Unobserved» Groups

2021 ◽  
pp. 160-182
Author(s):  
Olga Popova ◽  
Sergey Suslov
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Online Communities ◽  
Latent Dirichlet Allocation ◽  
The Political ◽  
Analysis Methods ◽  
Methods And Techniques ◽  
Political Communities

The article is dedicated to the development of the political communities in social networks analysis methods. Main stages of network approach in the political science is described in the research. Researchers review the most significant methods and techniques in the political online communities studies for the last decade. The article shows the contemporary Russian scientists contribution in the development of online communities learning techniques. Networks and social network analysis methods and techniques become universal scientific approaches for several scientific fields. Boundary-transcending trends were critical means of science integration. Researchers present the results of experiment in which evaluate the possibilities of study unobserved political groups using latent Dirichlet allocation (LDA) model. The brief LDA foundation history and possible modifications for social topic modeling based on social networks data are discribed in the review. Using sample from one feed aggregator telegram channel in period of 2020 autumn, the authors display the most valuable topics in the Russian segment of political communication. Also it provides communities ideological preferences. Modified qualitative sociological methods can be used in online political communities discursive features research without any specific computer science techniques. Since about 70% of the Internet data are generated in the social networks, velocity and volume data necessitate new data mining techniques, databases capacity and computation processes. In other words, it provides a big data approach in social network analysis.

scholarly journals Detecting Communities in Science Blogs

2017 ◽  
Author(s):  
Christina Kirk Pikas
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Spin Glass ◽  
Community Detection ◽  
Online Communities ◽  
Subject Area ◽  
Analysis Methods ◽  
Science Blogs

Many scientists maintain blogs and participate in online communities through their blogs and other scientists' blogs. This study used social network analysis methods to locate and describe online communities in science blogs. The structure of the science blogosphere was examined using links between blogs in blogrolls and in comments. By blogroll, the blogs are densely connected and cohesive subgroups are not easily found. Using spin glass community detection, six cohesive subgroups loosely corresponding to subject area were found. By commenter links, the blogs form into more easily findable general subject area or interest clusters.

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