Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction

Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. It's only known if users are connected, while the intensity of the connection is unknown. The aim of the research described was to determine and quantify friendship intensity between OSN users based on analysis of their interaction. We built a mathematical model, which uses: supervised machine learning algorithm Random Forest, experimentally determined importance of communication parameters and coefficients for every interaction parameter based on answers of research conducted through a survey. Taking user opinion into consideration while designing a model for calculation of friendship intensity is a novel approach in opposition to previous researches from literature. Accuracy of the proposed model was verified on the example of determining a better friend in the offered pair

Exploratory Analysis of Pairwise Interactions in Online Social Networks

In the last few decades sociologists were trying to explain human behaviour by analysing social networks, which requires access to data about interpersonal relationships. This represented a big obstacle in this research field until the emergence of online social networks (OSNs), which vastly facilitated the process of collecting such data. Nowadays, by crawling public profiles on OSNs, it is possible to build a social graph where "friends" on OSN become represented as connected nodes. OSN connection does not necessarily indicate a close real-life relationship, but using OSN interaction records may reveal real-life relationship intensities, a topic which inspired a number of recent researches. Still, published research currently lacks an extensive exploratory analysis of OSN interaction records, i.e. a comprehensive overview of users' interaction via different ways of OSN interaction. In this paper we provide such an overview by leveraging results of conducted extensive social experiment which managed to collect records for over 3,200 Facebook users interacting with over 1,400,000 of their friends. Our exploratory analysis focuses on extracting population distributions and correlation parameters for 13 interaction parameters, providing valuable insight in online social network interaction for future researches aimed at this field of study.Comment: Journal Article published 2 Oct 2017 in Automatika volume 58 issue 4 on pages 422 to 42

Social Influence on the User in Social Network: Types of Communications in Assessment of the Behavioral Risks connected with the Socio-engineering Attacks

The purpose of this study is to study the impact of possible types of relationships between users, which are represented in the social network “VKontakte”, on the probability of the spread of a social engineering attack.Methods. To achieve this goal, a survey was developed and a web page was created, which is used to collect responses from respondents. After receiving the data, the obtained results were analyzed using the tools available in Microsoft Excel. In addition, for more in-depth analysis of the results, a C program was developed, which calculates the necessary characteristics and outputs the results to an Excel document.Results. In analyzing the results of the survey, the types of relationships between users were identified, in which they are more likely to respond to the request. It was also revealed that the answers are most often found in which several or even all categories in groups of relationship types between users were assigned the same assessments of the degree of readiness to respond to a request. In addition, it is worth noting that there are often answers in which respondents identified only one of the presented communication options.Conclusion. According to the study, it was hypothesized that the assessments of the degree of readiness to respond to a request to join the community for different groups of relationships are different, but the intragroup assessments differ little. The results obtained, demonstrating the lack of differentiation of values within groups of types of relationships, are significant, but at the same time, a deeper study of the orders that can be traced in the responses of a number of respondents is required

Determination of friendship intensity between online social network users based on their interaction

Društvene mreže su jedan od popularnijih načina komunikacije današnjice, a među najpoznatijim je Facebook koji prosječno ima milijardu aktivnih korisnika dnevno. Cilj ovog diplomskog rada je pratiti međudjelovanja korisnika na društvenoj mreži, otkriti kakav je njihov odnos u stvarnom životu i kvantificirati ga. Provedeno je istraživanje putem upitnika na temelju kojeg je dobiven uvid u pojedine komunikacijske parametre koji su korišteni, iz perspektive korisnika. Izgrađen je matematički model koji koristi važnost pojedinih parametara prema algoritmu nasumičnih šuma koji pripada skupu algoritama nadziranog učenja, raspodjelu temeljenu na odgovorima ispitanika istraživanja i eksperimentalno određene koeficijente u svrhu pridavanja veće važnosti pojedinim parametrima. Model je kroz rad verificiran i uspoređen s modelom postojećeg istraživanja.People today communicate through social networks. One of the most popular social networks today is Facebook which has one billion of daily active users. This thesis objective is to determine and quantify friendship intensity between users based on analysis of their interaction on social network. Inisight of individual communication parameters importance from users perspective was received based on research conducted through survey. Mathematical model that was built uses random forest, supervised learning algorithm, distribution calculated based on users answers and experimentally determined coefficients for the purpose of giving greater importance to individual parameters. The model is verified and compared with the existing model through thesis

Determination of friendship intensity between online social network users based on their interaction

Društvene mreže su jedan od popularnijih načina komunikacije današnjice, a među najpoznatijim je Facebook koji prosječno ima milijardu aktivnih korisnika dnevno. Cilj ovog diplomskog rada je pratiti međudjelovanja korisnika na društvenoj mreži, otkriti kakav je njihov odnos u stvarnom životu i kvantificirati ga. Provedeno je istraživanje putem upitnika na temelju kojeg je dobiven uvid u pojedine komunikacijske parametre koji su korišteni, iz perspektive korisnika. Izgrađen je matematički model koji koristi važnost pojedinih parametara prema algoritmu nasumičnih šuma koji pripada skupu algoritama nadziranog učenja, raspodjelu temeljenu na odgovorima ispitanika istraživanja i eksperimentalno određene koeficijente u svrhu pridavanja veće važnosti pojedinim parametrima. Model je kroz rad verificiran i uspoređen s modelom postojećeg istraživanja.People today communicate through social networks. One of the most popular social networks today is Facebook which has one billion of daily active users. This thesis objective is to determine and quantify friendship intensity between users based on analysis of their interaction on social network. Inisight of individual communication parameters importance from users perspective was received based on research conducted through survey. Mathematical model that was built uses random forest, supervised learning algorithm, distribution calculated based on users answers and experimentally determined coefficients for the purpose of giving greater importance to individual parameters. The model is verified and compared with the existing model through thesis

Determination of friendship intensity between online social network users based on their interaction

Društvene mreže su jedan od popularnijih načina komunikacije današnjice, a među najpoznatijim je Facebook koji prosječno ima milijardu aktivnih korisnika dnevno. Cilj ovog diplomskog rada je pratiti međudjelovanja korisnika na društvenoj mreži, otkriti kakav je njihov odnos u stvarnom životu i kvantificirati ga. Provedeno je istraživanje putem upitnika na temelju kojeg je dobiven uvid u pojedine komunikacijske parametre koji su korišteni, iz perspektive korisnika. Izgrađen je matematički model koji koristi važnost pojedinih parametara prema algoritmu nasumičnih šuma koji pripada skupu algoritama nadziranog učenja, raspodjelu temeljenu na odgovorima ispitanika istraživanja i eksperimentalno određene koeficijente u svrhu pridavanja veće važnosti pojedinim parametrima. Model je kroz rad verificiran i uspoređen s modelom postojećeg istraživanja.People today communicate through social networks. One of the most popular social networks today is Facebook which has one billion of daily active users. This thesis objective is to determine and quantify friendship intensity between users based on analysis of their interaction on social network. Inisight of individual communication parameters importance from users perspective was received based on research conducted through survey. Mathematical model that was built uses random forest, supervised learning algorithm, distribution calculated based on users answers and experimentally determined coefficients for the purpose of giving greater importance to individual parameters. The model is verified and compared with the existing model through thesis

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject