Distinguishing Topical and Social Groups Based on Common Identity and Bond Theory

Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.Comment: 10 pages, 6 figures, 2 table

Comparative classification of student's academic failure through Social Network Mining and Hierarchical Clustering

Student academic failure are caused by several factors such as: family relationship, study time, absence, parent education, travel time and etc. This study observe several factors which are related to student academic failure by calculating the centrality degree between students to find the correlation between failure factors for each students. Furthermore, each student will be measured by measuring the geodesic distance for each factors for hierarchical clustering. The flow betwenness measure and hierarchical clustering show the promising result, where students who has similar factors value are tends to be grouped together in the same cluster. The student with high value of flow betwenness is considered as broker of network and play vital character inside network. The result of study is believed can bring important and useful information toward the student performance analysis for future better education

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

Bowling Together Again: Facilitating the Initiation of Collective Action through Awareness of Others

Often within communities there is sufficient interest in group-activities and yet they fail to occur because of insufficient individual initiative. This could be due to diffusion of responsibility or uncertainty about the availability of potential participants. Providing information about the number of interested individuals has conflicting implications, and hence an ambiguous impact on the likelihood of activities occurring. Our experiment examines the impact of providing information about community interest on activity initiation. Subjects (n=2000) were given information about the level of interest in a possible activity within their community and the ability to initiate its planning. Results indicate that displaying sufficient interest in an activity is positively associated with willingness to initiate planning. This suggests that Internet applications which 1) provide awareness of shared activity interest and 2) reduce effort required to initiate activity planning could boost collective action and improve community life

Using Semantic Linking to Understand Persons' Networks Extracted from Text

In this work, we describe a methodology to interpret large persons' networks extracted from text by classifying cliques using the DBpedia ontology. The approach relies on a combination of NLP, Semantic web technologies, and network analysis. The classification methodology that first starts from single nodes and then generalizes to cliques is effective in terms of performance and is able to deal also with nodes that are not linked to Wikipedia. The gold standard manually developed for evaluation shows that groups of co-occurring entities share in most of the cases a category that can be automatically assigned. This holds for both languages considered in this study. The outcome of this work may be of interest to enhance the readability of large networks and to provide an additional semantic layer on top of cliques. This would greatly help humanities scholars when dealing with large amounts of textual data that need to be interpreted or categorized. Furthermore, it represents an unsupervised approach to automatically extend DBpedia starting from a corpus

Distinguishing topical and social groups based on common identity and bond theory

arXiv:1309.2199Social groups play a crucial role in social media platforms because they form the basis for user participation and engagement. Groups are created explicitly by members of the community, but also form organically as members interact. Due to their importance, they have been studied widely (e.g., community detection, evolution, activity, etc.). One of the key questions for understanding how such groups evolve is whether there are different types of groups and how they differ. In Sociology, theories have been proposed to help explain how such groups form. In particular, the common identity and common bond theory states that people join groups based on identity (i.e., interest in the topics discussed) or bond attachment (i.e., social relationships). The theory has been applied qualitatively to small groups to classify them as either topical or social. We use the identity and bond theory to define a set of features to classify groups into those two categories. Using a dataset from Flickr, we extract user-defined groups and automatically-detected groups, obtained from a community detection algorithm. We discuss the process of manual labeling of groups into social or topical and present results of predicting the group label based on the defined features. We directly validate the predictions of the theory showing that the metrics are able to forecast the group type with high accuracy. In addition, we present a comparison between declared and detected groups along topicality and sociality dimensions.This research is partially supported by EU’s FP7/2007-2013 under the ARCOMEM and SOCIALSENSOR projects, by the CDTI (Spain) under the CENIT program, project CEN-20101037“Social Media”, and by MICINN (Spain) through Grant TIN2009-14560-C03-01, and by MINECO (Spain) and FEDER (EU) through projects MODASS (FIS2011-247852) and FISICOS (FIS2007-60327). P.A.G. acknowledges support from the JAE Predoc program of CSIC (Spain).N

Libro Blanco de los Sistemas Complejos Socio-tecnológicos

La Red SocioComplex está formada por la Universitat de Barcelona (coordinación), Fundación IMDEA Networks, Instituto de Física Interdisciplinar y Sistemas Complejos (CSIC-Universitat Illes Balears), Universidad de Burgos, Universidad Carlos III de Madrid, Universitat Rovira i Virgili, Universitat de València y Universidad de Zaragoza - Instituto de Biocomputación y Física de los Sistemas Complejos.Este libro blanco analiza por primera vez las principales fuerzas de la investigación española en ciencias de la complejidad en el contexto de los sistemas socio-tecnológicos.El Libro Blanco de los Sistemas Complejos Socio-tecnológicos forma parte del conjunto de acciones realizadas por la red temática SocioComplex FIS2015-71795-REDT financiada por parte del Ministerio de Economía, Industria y Competitividad – Agencia Estatal de Investigación y del Fondo Europeo de Desarrollo Regional (FEDER)

Complex networks approach to modeling online social systems. The emergence of computational social science

This thesis is devoted to quantitative description, analysis, and modeling of complex social systems in the form of online social networks. Statistical patterns of the systems under study are unveiled and interpreted using concepts and methods of network science, social network analysis, and data mining. A long-term promise of this research is that predicting the behavior of complex techno-social systems will be possible in a way similar to contemporary weather forecasting, using statistical inference and computational modeling based on the advancements in understanding and knowledge of techno-social systems. Although the subject of this study are humans, as opposed to atoms or molecules in statistical physics, the availability of extremely large datasets on human behavior permits the use of tools and techniques of statistical physics. This dissertation deals with large datasets from online social networks, measures statistical patterns of social behavior, and develops quantitative methods, models, and metrics for complex techno-social systemsLa presente tesis está dedicada a la descripción, análisis y modelado cuantitativo de sistemas complejos sociales en forma de redes sociales en internet. Mediante el uso de métodos y conceptos provenientes de ciencia de redes, análisis de redes sociales y minería de datos se descubren diferentes patrones estadísticos de los sistemas estudiados. Uno de los objetivos a largo plazo de esta línea de investigación consiste en hacer posible la predicción del comportamiento de sistemas complejos tecnológico-sociales, de un modo similar a la predicción meteorológica, usando inferencia estadística y modelado computacional basado en avances en el conocimiento de los sistemas tecnológico-sociales. A pesar de que el objeto del presente estudio son seres humanos, en lugar de los átomos o moléculas estudiados tradicionalmente en la física estadística, la disponibilidad de grandes bases de datos sobre comportamiento humano hace posible el uso de técnicas y métodos de física estadística. En el presente trabajo se utilizan grandes bases de datos provenientes de redes sociales en internet, se miden patrones estadísticos de comportamiento social, y se desarrollan métodos cuantitativos, modelos y métricas para el estudio de sistemas complejos tecnológico-sociales

Virtual smarts - optimizing the coalescing of people for collective action within urban communities

Despite the importance of individuals coming together for social group-activities (e.g., pick-up volleyball), the process by which such groups coalesce is poorly understood, and as a consequence is poorly supported by technology. This is despite the emergence of Event-Based Social Network (EBSN) technologies that are specifically designed to assist group coalescing for social activities. Existing theories focus on group development in terms of norms and types, rather than the processes involved in initial group coalescence. This dissertation addresses this gap in the literature through four studies focusing on understanding the coalescing process for interest-based group activities within urban environments and a design of a mobile user interface aimed at increasing collective action initiation. Study One examined how well people\u27s needs for social group activity engagement are being met in the context of an urban university. The analysis of 60 interviews highlighted how participants considered activity leadership a burden, where it took too much time and was difficult to find others. Study Two (a mixed methods study of 763 Meetup.com groups in the NY/NJ/CT Tri-State) and Study Three (A survey of 511 students at an urban university) corroborated results that attendance and participation at the first meeting determined long-term success by giving the organizer belief that their group would be successful. Study Four involved the design and testing of a mobile group coalescing user-interface (UI) that featured several lightweight coalescing features hypothesized to reduce the challenges to organizing. Results from the 2000 participant study indicated that the UI increased the likelihood non-leaders would initiate collective action. The models generated from the study data suggested that a new theory is required to understand the role of critical mass in collective action. The combination of these investigations into interest-based group activity coalescing uncovered important gaps in the current knowledge of interest-based group activity coalescing and collective action initiation. This work extends our knowledge about how to improve coalescing support and encourage non-leaders to initiate activity coalescing, which will lead to a greater number of activities forming. Finally, this research uncovers the need to redefine collective action and critical mass models to include motivation to organize and its moderators