LACAIS - Spanish, Portuguese and Latin America
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Paper Type
Complete
Paper Number
1043
Description
This paper aims to understand how Web 2.0 users who produce artistic content through photographs interact with each other on digital social networks. To this end, this study used Social Network Analysis techniques to understand the characteristics of a network composed of 12,112 users extracted from the Twitter site. The paper observed that Social Network Analysis metrics outdegree, betweenness and closeness are effective in filtering users with relevant artistic content, and while the outdegree and closeness metrics select user profiles with much more uniform content, accounts with high betweenness proved to be more eclectic, with varied techniques and sometimes compilations from several photographers. Another feature observed was the ease of users to increase local centrality metrics such as outdegree and indegree on their own, particularly in the case of indegree which ended up selecting a large amount of accounts suspected of being bots.
Recommended Citation
Kaneko, Nicolas Teichi and Mantovani, Daielly, "Identificação de comportamento de rede na produção de conteúdo artístico em redes sociais" (2022). AMCIS 2022 Proceedings. 1.
https://aisel.aisnet.org/amcis2022/lacais/lacais/1
Identificação de comportamento de rede na produção de conteúdo artístico em redes sociais
This paper aims to understand how Web 2.0 users who produce artistic content through photographs interact with each other on digital social networks. To this end, this study used Social Network Analysis techniques to understand the characteristics of a network composed of 12,112 users extracted from the Twitter site. The paper observed that Social Network Analysis metrics outdegree, betweenness and closeness are effective in filtering users with relevant artistic content, and while the outdegree and closeness metrics select user profiles with much more uniform content, accounts with high betweenness proved to be more eclectic, with varied techniques and sometimes compilations from several photographers. Another feature observed was the ease of users to increase local centrality metrics such as outdegree and indegree on their own, particularly in the case of indegree which ended up selecting a large amount of accounts suspected of being bots.
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