scholarly journals Data Mining on Social Interaction Networks

2014 ◽  
Vol 2014 ◽  
Author(s):  
Martin Atzmueller
Keyword(s):  
Data Mining ◽  
Social Interaction ◽  
Real World ◽  
Social Systems ◽  
Interaction Networks ◽  
Descriptive Data ◽  
Face To Face ◽  
The Social ◽  
Mining Methods ◽  
Predictive Data Mining

Social media and social networks have already woven themselves into the very fabric of everyday life. This results in a dramatic increase of social data capturing various relations between the users and their associated artifacts, both in online networks and the real world using ubiquitous devices. In this work, we consider social interaction networks from a data mining perspective - also with a special focus on real-world face-to-face contact networks: We combine data mining and social network analysis techniques for examining the networks in order to improve our understanding of the data, the modeled behavior, and its underlying emergent processes. Furthermore, we adapt, extend and apply known predictive data mining algorithms on social interaction networks. Additionally, we present novel methods for descriptive data mining for uncovering and extracting relations and patterns for hypothesis generation and exploration, in order to provide characteristic information about the data and networks. The presented approaches and methods aim at extracting valuable knowledge for enhancing the understanding of the respective data, and for supporting the users of the respective systems. We consider data from several social systems, like the social bookmarking system BibSonomy, the social resource sharing system flickr, and ubiquitous social systems: Specifically, we focus on data from the social conference guidance system Conferator and the social group interaction system MyGroup. This work first gives a short introduction into social interaction networks, before we describe several analysis results in the context of online social networks and real-world face-to-face contact networks. Next, we present predictive data mining methods, i.e., for localization, recommendation and link prediction. After that, we present novel descriptive data mining methods for mining communities and patterns.

Development of IT and Virtual Communities

2009 ◽  
pp. 349-355 ◽  
Author(s):  
Stefano Tardini
Keyword(s):  
Social Interaction ◽  
Virtual Communities ◽  
Imagined Communities ◽  
Community Members ◽  
Face To Face ◽  
Perfect Form ◽  
The Social ◽  
Community Finding ◽  
German Sociologist ◽  
The City

The notion of community is pivotal in the sociological tradition. According to Nisbet (1966), “the most fundamental and far-reaching of sociology’s unit ideas is community” (p. 47). Yet, it is not easy to define what a community is. Though in everyday life the concept of “community” is widespread, nonetheless this concept is very problematic in scientific reflections, partly because of its strongly interdisciplinary nature. As long ago as 1955, Hillery could list and compare 94 different definitions of “community,” finding only some common elements among them, such as social interaction, area, and common ties. Generally speaking, a community can be defined as “a group of persons who share something more or less decisive for their life, and who are tied by more or less strong relationships” (Cantoni & Tardini, 2006, p. 157). It is worth noticing here that the term “community” seems to have only favorable connotations. As observed in 1887 by Ferdinand Tönnies, the German sociologist who first brought the term “community” into the scientific vocabulary of the social sciences, “a young man is warned about mixing with bad society: but ‘bad community’ makes no sense in our language” (Tönnies, 2001, p. 18; Williams, 1983). Two main ways of considering communities can be singled out: 1. Communities can be intended as a set of people who have something in common, and 2. Communities can be intended as groups of people who interact. The distinction between the two ways of conceiving a community is very well illustrated by an example provided by Aristotle. In his Politics (3.1.12), the Greek philosopher tells that, when Babylon was captured by an invading army of Persians, in certain parts of the city the capture itself had not been noticed for three days. This is the reason why Aristotle considers Babylon not a polis, but an ethnos. In fact, according to Aristotle, what distinguishes the polis, that is, the perfect form of community (see Politics 1.1.1), from the ethnos is the presence of interactions and communications among the citizens. In a polis citizens speak to each other, they interact and communicate, while in an ethnos they just have the same walls in common. In the sense of the ethnos, we speak, for instance, of the community of the linguists, of the community of Italian speaking people, of the open source community, and so on. The members of such communities usually do not know each other, they do not communicate each with all the others, but they have the perception of belonging to the community, they are aware of being part of it. According to Cohen (1985), such communities are symbolic constructions. Rather than being structures, they are entities of meaning, founded on a shared conglomeration of normative codes and values that provide community members with a sense of identity. In a similar way, Anderson (1991) defines the modern nations (the Aristotelian ethne) as “imagined communities”: [They are] imagined because the members of even the smallest nation will never know most of their fellowmembers, meet them, or even hear of them, yet in the minds of each lives the image of their communion. […] In fact, all communities larger than primordial villages or face-to-face contact (and perhaps even these) are imagined. (pp. 5-6)

Overview of Predictive Modeling Approaches in Health Care Data Mining

2016 ◽  
pp. 73-95 ◽  
Author(s):  
Sunita Soni
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Clinical Information ◽  
Ease Of Use ◽  
Classification Model ◽  
Computational Time ◽  
Data Sets ◽  
Advantages And Disadvantages ◽  
Mining Methods ◽  
Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

scholarly journals Placing self-assessed health within the systems framework: a preliminary insight into social synergy and syntony

2020 ◽  
Vol 14 (1) ◽  
pp. 626-635
Author(s):  
Elena-Mădălina Vătămănescu ◽  
Andreia Gabriela Andrei ◽  
Adriana Zaiţ
Keyword(s):  
Social Trust ◽  
Social Systems ◽  
Face To Face ◽  
Fields Of Study ◽  
The Social ◽  
Social Environmental ◽  
Self Assessed Health ◽  
Systems Framework ◽  
Insight Into ◽  
Specific Contribution

AbstractThe issue of self-assessed health (SAH) has been discussed within the scope of multiple interdisciplinary and transdisciplinary studies, gathering the attention and interest of scholars from various fields of study. Emerged at the confluence of subjective and objective measurements, the construct has triggered controversies and debates on its relevance and reliability, yet it is employed in many analyses as a pertinent reference point for individuals’ perceptions regarding their health status or wellbeing. Starting from these considerations, the current study aims to move the discussion further, by placing SAH in a broader argumentative perspective, as a multivalent process dependent on a myriad of individual, social, environmental, digital, etc. factors apposite to complex social systems. Therefore, the specific contribution intended via this approach is the advancement of a preliminary outlook on SAH within the social systems framework with a special emphasis on synergy and syntony. Against the backdrop of a conceptual undertaking, several factors are brought forward – i.e., environmental factors such as housing, neighborhood, residence and social (interactional) factors such as digital exposure, face-to-face communication, and social trust – hewing the path for future in-depth investigations on the topic.

scholarly journals PENGARUH PENGGUNAAN HANDPHONE TERHADAP INTERAKSI SOSIAL REMAJA DI DESA DAYAH MEUNARA KECAMATAN KUTAMAKMUR KABUPATEN ACEH UTARA

2018 ◽  
Vol 4 (2) ◽  
pp. 19
Author(s):  
Muchlis Aziz ◽  
Nurainiah Nurainiah
Keyword(s):  
Social Interaction ◽  
Mobile Phones ◽  
Qualitative Method ◽  
Analysis Data ◽  
Reliable Data ◽  
Face To Face ◽  
Inhibiting Factors ◽  
The Social ◽  
Data Collection Technique

This study entitled the influence of the use of mobile phones on the social interaction of adolescents in Dayah Meunara Village, Kutamakmur District, North Aceh Regency. Mobile is one of the elegant devices that can help make it easier for humans to interact through long distances though. However, the presence of this mobile phone should not be a cause of changes in social interaction for teenagers. The purpose of this study was to determine the effect of using mobile phones on the social interaction of adolescents in Dayah Meunara Village, Kutamakmur District, North Aceh Regency and to understand the factors that hindered the pattern of social interaction in the Dayah Meunara Village, Kutamakmur District, North Aceh Regency. This study uses a qualitative method, where the researcher thoroughly examines the facts found in the research location in accordance with the focus of the problem, by examining directly, then the results of the analysis data are presented and given discussion. To get accurate and reliable data, the data collection technique is done through observation, interviews and documentation. The results of the study explained that mobile phones can influence the social interaction of adolescents in Dayah Meunara Village, both positive and negative influences. Positive influences include making it easy to communicate even when far away. While the negative influences include being able to make teenagers experience dysfunction, when face-to-face interaction decreases immediately, the presence of mobile phones interferes with the quality of direct interactions, mobile phones make hyperpersonal teenagers, mobile phones make consumers consumptive and mobile phones make teenagers less sensitive to the environment. The inhibiting factors for the pattern of social interaction among adolescents in Dayah Meunara Village are family, community, religious, customs and habits. These factors can make social change in adolescents in particular and society in general. Keywords: Mobile, Social Interaction

scholarly journals Mining on Social Media Data: To Determine the Personality of Unrevealed Person

2019 ◽  
Vol 8 (4) ◽  
pp. 8574-8577
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Information Mining ◽  
Social Media Data ◽  
Data Mining Techniques ◽  
Network Discovery ◽  
The Social ◽  
Mining Methods ◽  
Using Data ◽  
Media Data

The unavoidable utilization of online networking like Facebook is giving exceptional measures of social information. Information mining methods have been broadly used to separate learning from such information. The character of the person is predicted whether he is good or not by using data mining techniques from user self-made data. Mining methods are being broadly using to separate learning from such information, main examples for them are network discovery and slant investigation. Notwithstanding, there is still a lot of room to investigate as far as the occasion information (i.e., occasions with timestamps, for example, posting an inquiry, altering an article in Wikipedia, and remarking on a tweet. These occasions react users' personal conduct standards and working forms in the social media websites.

Overview of Predictive Modeling Approaches in Health Care Data Mining

2014 ◽  
pp. 349-370
Author(s):  
Sunita Soni
Keyword(s):  
Data Mining ◽  
Predictive Modeling ◽  
Clinical Information ◽  
Ease Of Use ◽  
Classification Model ◽  
Computational Time ◽  
Data Sets ◽  
Advantages And Disadvantages ◽  
Mining Methods ◽  
Predictive Data Mining

Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.

The Basis For A Neurobiological-Associative Model of Personality and Group Cohesion: The Evolutionary And System Biological Origins Of Social Exclusion, Hierarchy, and Structure

2020 ◽  
Author(s):  
Michael Thomas
Keyword(s):  
Social Interaction ◽  
Social Exclusion ◽  
Feedback Loop ◽  
Group Structure ◽  
Social Systems ◽  
Group Cohesiveness ◽  
Associative Model ◽  
Environmental Conditioning ◽  
The Social ◽  
Group Structures

By using a systems biological perspective and available literature on human social interaction, grouping, and cohesiveness, a new coherent model is proposed that integrates existing social integration and neurobiological research into a theoretical neurobiological framework of personality and social interaction. This model allows for the coherent analysis of complex social systems and interactions within them, and proposes a framework for estimating group cohesiveness and evaluating group structures in order to build and organize optimized social groups. This „Neurobiological-Associative“ model proposes two primary feedback loops, with environmental conditioning (learning) being sorted into an associative model that modulates interaction with the social environment, and which impacts the second feedback loop involving the individuals' neurobiological capacity. In this paper, the concept of neurobiological capacity is developed and based upon contemporary research on intelligence, personality, and social behavior with a focus on the oxytocin, serotonin, and dopamine systems. The basis of social exclusion and group structure is thus, expressed in the very most simple terms, neurobiological compatibility and risk assessment modulated by an internal associative model.

scholarly journals Who Are Controlled by The Same User? Multiple Identities Deception Detection via Social Interaction Activity (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13853-13854
Author(s):  
Jiacheng Li ◽  
Chunyuan Yuan ◽  
Wei Zhou ◽  
Jingli Wang ◽  
Songlin Hu
Keyword(s):  
Social Media ◽  
Social Interaction ◽  
Social Interactions ◽  
Real World ◽  
Deception Detection ◽  
State Of The Art ◽  
Multiple Identities ◽  
The Social ◽  
Real World Datasets ◽  
Multiple Accounts

Social media has become a preferential place for sharing information. However, some users may create multiple accounts and manipulate them to deceive legitimate users. Most previous studies utilize verbal or behavior features based methods to solve this problem, but they are only designed for some particular platforms, leading to low universalness.In this paper, to support multiple platforms, we construct interaction tree for each account based on their social interactions which is common characteristic of social platforms. Then we propose a new method to calculate the social interaction entropy of each account and detect the accounts which are controlled by the same user. Experimental results on two real-world datasets show that the method has robust superiority over state-of-the-art methods.

scholarly journals PENGARUH PENGGUNAAN HANDPHONE TERHADAP INTERAKSI SOSIAL REMAJA DI DESA DAYAH MEUNARA KECAMATAN KUTAMAKMUR KABUPATEN ACEH UTARA

2018 ◽  
Vol 4 (2) ◽  
Author(s):  
Muchlis Aziz ◽  
Nurainiah Nurainiah
Keyword(s):  
Social Interaction ◽  
Mobile Phones ◽  
Qualitative Method ◽  
Analysis Data ◽  
Reliable Data ◽  
Face To Face ◽  
Inhibiting Factors ◽  
The Social ◽  
Data Collection Technique

This study entitled the influence of the use of mobile phones on the social interaction of adolescents in Dayah Meunara Village, Kutamakmur District, North Aceh Regency. Mobile is one of the elegant devices that can help make it easier for humans to interact through long distances though. However, the presence of this mobile phone should not be a cause of changes in social interaction for teenagers. The purpose of this study was to determine the effect of using mobile phones on the social interaction of adolescents in Dayah Meunara Village, Kutamakmur District, North Aceh Regency and to understand the factors that hindered the pattern of social interaction in the Dayah Meunara Village, Kutamakmur District, North Aceh Regency. This study uses a qualitative method, where the researcher thoroughly examines the facts found in the research location in accordance with the focus of the problem, by examining directly, then the results of the analysis data are presented and given discussion. To get accurate and reliable data, the data collection technique is done through observation, interviews and documentation. The results of the study explained that mobile phones can influence the social interaction of adolescents in Dayah Meunara Village, both positive and negative influences. Positive influences include making it easy to communicate even when far away. While the negative influences include being able to make teenagers experience dysfunction, when face-to-face interaction decreases immediately, the presence of mobile phones interferes with the quality of direct interactions, mobile phones make hyperpersonal teenagers, mobile phones make consumers consumptive and mobile phones make teenagers less sensitive to the environment. The inhibiting factors for the pattern of social interaction among adolescents in Dayah Meunara Village are family, community, religious, customs and habits. These factors can make social change in adolescents in particular and society in general. Keywords: Mobile, Social Interaction

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