an article by Vasileios Gkioulos and Gaute Wangen (Norwegian University of Science & Technology, Gjøvik, Norway),
Sokratis K. Katsikas (Norwegian University of Science & Technology, Gjøvik, Norway and (University of Piraeus, Greece)
and George Kavallieratos and
Panayiotis Kotzanikolaou (University of Piraeus, Greece)
published in Information Volume 8 Issue 2 Article 42 (2017)
Abstract
Young generations make extensive use of mobile devices, such as smartphones, tablets and laptops, while a plethora of security risks associated with such devices are induced by vulnerabilities related to user behavior. Furthermore, the number of security breaches on or via portable devices increases exponentially.
Thus, deploying suitable risk treatments requires the investigation of how the digital natives (young people, born and bred in the digital era) use their mobile devices and their level of security awareness, in order to identify common usage patterns with negative security impact.
In this article, we present the results of a survey performed across a multinational sample of digital natives with distinct backgrounds and levels of competence in terms of security, to identify divergences in user behavior due to regional, educational and other factors.
Our results highlight significant influences on the behavior of digital natives, arising from user confidence, educational background, and parameters related to usability and accessibility. The outcomes of this study justify the need for further analysis of the topic, in order to identify the influence of fine-grained semantics, but also the consolidation of wide and robust user-models.
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Showing posts with label user_behaviour. Show all posts
Showing posts with label user_behaviour. Show all posts
Friday, 10 November 2017
Tuesday, 3 July 2012
Characterizing user navigation and interactions in online social networks
an article by Fabrício Benevenuto (Federal University of Ouro Preto, Brazil), Tiago Rodrigues and Virgílio Almeida (Federal University of Minas Gerais, Brazil)
and Meeyoung Cha (Graduate School of Culture Technology, KAIST, Republic of Korea) published in Information Sciences
Volume 195 (15th July 2012)
Abstract
Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present an in-depth analysis of user workloads in online social networks.
This study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication.
Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we gather the social network topology of Orkut, so that we could analyse user interaction data in light of the social graph.
Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends’ and non-immediate friends’ pages.
Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends’ pages increase the measured level of interaction among users.
Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks.
Abstract
Understanding how users navigate and interact when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present an in-depth analysis of user workloads in online social networks.
This study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication.
Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we gather the social network topology of Orkut, so that we could analyse user interaction data in light of the social graph.
Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends’ and non-immediate friends’ pages.
Results show that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, silent interactions like browsing friends’ pages increase the measured level of interaction among users.
Additionally, we find that friends requesting content are often within close geographical proximity of the uploader. We also discuss a series of implications of our findings for efficient system and interface design as well as for advertisement placement in online social networks.
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