The power of indirect social ties

While direct social ties have been intensely studied in the context of computer-mediated social networks, indirect ties (e.g., friends of friends) have seen little attention. Yet in real life, we often rely on friends of our friends for recommendations (of good doctors, good schools, or good babysitters), for introduction to a new job opportunity, and for many other occasional needs. In this work we attempt to 1) quantify the strength of indirect social ties, 2) validate it, and 3) empirically demonstrate its usefulness for distributed applications on two examples. We quantify social strength of indirect ties using a(ny) measure of the strength of the direct ties that connect two people and the intuition provided by the sociology literature. We validate the proposed metric experimentally by comparing correlations with other direct social tie evaluators. We show via data-driven experiments that the proposed metric for social strength can be used successfully for social applications. Specifically, we show that it alleviates known problems in friend-to-friend storage systems by addressing two previously documented shortcomings: reduced set of storage candidates and data availability correlations. We also show that it can be used for predicting the effects of a social diffusion with an accuracy of up to 93.5%.Comment: Technical Repor

Predicting Friendship Strength in Facebook

Effective friend classification in Online Social Networks (OSN) has many benefits in privacy. Anything posted by the user in social networks like Facebook is distributed among all their friends. Although the user can select the manual option for their post-dissemination, it is not feasible every time. Since not all friends are the same in a social network, the visibility access for the post should be different for different strengths of friendship for privacy. We propose a model with 24 features for finding friendship strength in a social network like Facebook. Previous works in finding friendship strength in social networks have used interaction and similarity based features but none of them has considered using linguistic features as the driving factor to determine the strength. In this paper, we developed a supervised friendship strength model to estimate the friendship strength based upon 24 different features comprising of structure based, interaction based, homophily based and linguistic-based features. We evaluated our approach using a real-world Facebook dataset that has 680 user-friend pairs and obtained accuracy of 85% across close and acquaintance friend classification. Our experiments suggest that features like average comment length; likes, love, friend posts, mutual friends and closeness variable consistently perform better in predicting friendship strength across different classifiers. In addition, combining language-based features with homophilic, structural and interaction features produces more accurate and trustworthy models to evaluate friendship strength

Reconsidering ‘Ties’: The Sociotechnical Job Search Network

This study explored how job seekers perceived human and technological sources in their sociotechnical ego-networks. United States residents (N = 285) who had sought jobs in the past 2 years responded to questions about their perceptions of sources used during the job search (n = 1297). Participants rated each source they used across a variety of perceived attributes. We measured tie strength using an amalgam of frequency of interaction and closeness, and strong tie sources included humans contacted online and in-person as well as websites. In contrast, the weakest tie sources were direct online application, employment agencies, and career events. Results showed a newly developed perceived bridging scale, social support, ease of access, and homophily were all positively related to tie strength. Influence was negatively related to tie strength. Information quality was not related to tie strength. We discuss implications for network and job search research, theory, and practice

Computing tie strength

Relationships make social media social. But, not all relationships are created equal. We have colleagues with whom we correspond intensely, but not deeply; we have childhood friends we consider close, even if we fell out of touch. Social media, however, treats everybody the same: someone is either a completely trusted friend or a total stranger, with little or nothing in between. In reality, relationships fall everywhere along this spectrum, a topic social science has investigated for decades under the name tie strength, a term for the strength of a relationship between two people. Despite many compelling findings along this line of research, social media does not incorporate tie strength or its lessons. Neither does most research on large-scale social phenomena. In social network analyses, a link either exists or not. Relationships have few properties of their own. Simply put, we do not understand a basic property of relationships expressed online. This dissertation addresses this problem, merging the theories behind tie strength with the data from social media. I show how to reconstruct tie strength from digital traces in online social media, and how to apply it as a tool in design and analysis. Specifically, this dissertation makes three contributions. First, it offers a rich, high-accuracy and general way to reconstruct tie strength from digital traces, traces like recency and a message???s emotional content. For example, the model can split users into strong and weak ties with nearly 89% accuracy. I argue that it also offers us a chance to rethink many of social media???s most fundamental design elements. Next, I showcase an example of how we can redesign social media using tie strength: a Twitter application open to anyone on the internet which puts tie strength at the heart of its design. Through this application, called We Meddle, I show that the tie strength model generalizes to a new online community, and that it can solve real people???s practical problems with social media. Finally, I demonstrate that modeling tie strength is an important new tool for analyzing large-scale social phenomena. Specifically, I show that real-life diffusion in online networks depends on tie strength (i.e., it depends on social relationships). As a body of work, diffusion studies make a big simplifying assumption: simple stochastic rules govern person-to-person transmission. How does a disease spread? With constant probability. How does a chain letter diffuse? As a branching process. I present a case where this simplifying assumption does not hold. The results challenge the macroscopic diffusion properties in today???s literature, and they hint at a nest of complexity below a placid stochastic surface. It may be fair to see this dissertation as linking the online to the offline; that is, it connects the traces we leave in social media to how we feel about relationships in real life

Studying social network sites with the combination of traditional social science and computational approaches

Social Network Sites (SNSs) are fundamentally changing the way humans connect, communicate and relate to one another and have attracted a considerable amount of research attention. In general, two distinct research approaches have been followed in the pursuit of results in this research area. First, established traditional social science methods, such as surveys and interviews, have been extensively used for inquiry-based research on SNSs. More recently, however, the advent of Application Programming Interfaces (APIs) has enabled data-centric approaches that have culminated in theory-free “big data” studies. Both of these approaches have advantages, disadvantages and limitations that need to be considered in SNS studies. The objective of this dissertation is to demonstrate how a suitable combination of these two approaches can lead to a better understanding of user behavior on SNSs and can enhance the design of such systems. To this end, I present two two-part studies that act as four pieces of evidence in support of this objective. In particular, these studies investigate whether a combination of survey and API-collected data can provide additional value and insights when a) predicting Facebook motivations, b) understanding social media selection, c) understanding patterns of communication on Facebook, and d) predicting and modeling tie strength, compared to what can be gained by following a traditional social science or a computational approach in isolation. I then discuss how the findings from these studies contribute to our understanding of online behavior both at the individual user level, e.g. how people navigate the SNS ecosystem, and at the level of dyadic relationships, e.g. how tie strength and interpersonal trust affect patterns of dyadic communication. Furthermore, I describe specific implications for SNS designers and researchers that arise from this work. For example, the work presented has theoretical implications for the Uses and Gratifications (U&G) framework and for the application of Rational Choice Theory (RCT) in the context of SNS interactions, and design implications such as enhancing SNS users’ privacy and convenience by supporting reciprocity of interactions. I also explain how the results of the conducted studies demonstrate the added value of combining traditional social science and computational methods for the study of SNSs, and, finally, I provide reflections on the strengths and limitations of the overall research approach that can be of use to similar research efforts.As Redes Sociais (SNSs - Social Network Sites) estão a mudar de form fundamental a maneira como os seres humanos estabelecem ligações entre si, como comunicam e como relacionam-se uns com os outros, tendo atraído uma considerável quantidade de atenção investigativa. Em geral, duas abordagens de investigação distintas foram seguidas na procura de resultados nesta área de investigação. Em primeiro lugar, os já estabelecidos métodos tradicionais das ciências sociais, tais como inquéritos e entrevistas foram amplamente utilizados na investigação baseada em SNSs. Contudo, o surgimento mais recente das Interfaces de Programação de Aplicações (APIs - Application Programming Interfaces) tem permitido abordagens centradas em dados que têm culminado em estudos de "dados extensos", livres de teoria. Ambas estas abordagens têm vantagens, desvantagens e limitações que precisam de ser consideradas nos estudos de SNS. O objectivo desta dissertação é demonstrar como uma combinação adequada destas duas abordagens pode levar a uma melhor compreensão do comportamento do utilizador em SNSs e pode melhorar a concepção de tais sistemas. Para esse efeito, apresento dois estudos, em duas partes, que funcionam como quatro peças de prova em apoio a este objectivo. Estes estudos investigam, em particular, se uma combinação de dados recolhidos através de inquéritos e API pode fornecer valor adicional e conhecimentos ao a) prever as motivações do Facebook, b) compreender a selecção dos meios de comunicação social, c) compreender os padrões de comunicação no Facebook, e d) prever e modelar a força dos laços, em comparação com o que pode ser ganho seguindo uma ciência social tradicional ou uma abordagem computacional isolada. Abordo em seguida como os resultados destes estudos contribuem para uma compreensão do comportamento online tanto a nível do utilizador individual, por exemplo, como as pessoas percorrem o ecossistema SNS, e ao nível das relações diádicas, por exemplo, como a força dos laços e a confiança interpessoal afectam os padrões de comunicação diádica. Além disso, descrevo as implicações específicas para os designers e investigadores do SNS que decorrem deste trabalho. Por exemplo, o trabalho apresentado tem implicações teóricas para o quadro de Usos e Gratificações (U&G - Uses and Gratifications framework) e para a aplicação da Teoria da Escolha Racional (RCT - Rational Choice Theory) no contexto das interacções SNS, e implicações de design, como o reforço da privacidade e conveniência dos utilizadores de SNS, com o apoio à reciprocidade das interacções. Explico também como os resultados dos estudos realizados demonstram o valor acrescentado de combinar as ciências sociais tradicionais e os métodos computacionais para o estudo de SNS, e, por fim, apresento reflexões sobre os pontos fortes e limitações da abordagem global de investigação que podem ser úteis a esforços de investigação semelhantes

Strength of weak ties and the modern job search

This dissertation examines the social networks of job seekers and information sources using two samples of Americans who have sought jobs in the past two-years. After a brief introductory chapter and a chapter reviewing network terminology and three network theories (strength of weak ties, structural holes theory, and social capital), two studies were conducted. The final chapter proposes nested levels of network influence and suggests revision to social network theory and research. Study 1 explored a random-digit dial survey of job seekers collected by the Pew Research Center. Data from participants who sought a job in the past two years was used to construct an affiliation (two-mode) network of job seekers and types of sources. Results from correspondence analysis, centrality measures, and an exponential random graph model (ERGM) show that job seekers used sources in conjunction at a rate greater than chance. Specifically, job seekers used three types of sources: (a) social sources (close friends and family, personal acquaintances, and professional acquaintances); (b) formal sources (print advertisements, career events, and employment agencies); (c) and online sources (social networking sites and online information and resources). Based on centrality measures, online information and resources were at the center of job seeker’s affiliation network. A discussion section reviews implications for Strength of Weak Ties theory as well as practical implications for the job search. Study 2 uses a survey of Amazon Mechanical Turk ® (MTurk) workers from the United States who have sought a job in the past two years. These participants responded to questions about the sources they used during the job search, including information sources accessed online and offline. Strong ties included close friends and family contacted online and in-person as well as websites; in contrast, the weakest ties were direct online application, employment agencies, and career events. Results showed that, controlling for homophily, tie strength was positively related to social support, bridging, and ease of access. Additionally, weakness of tie was related to influence. Contrary to strength of weak ties theory, information quality was not related to tie strength. Finally, this study explored within-person attributes related to tie strength. Perceptions of the job search as a networking task were positively related to use of stronger ties; in contrast, feelings of uncertainty, above one’s comfort, led to use of weaker ties on average. Perceptions of a larger personal network had a positive indirect effect on the strength of ties. A final chapter presents implications for sociomateriality, latent tie theory, and network research, in general. These studies paint a complicated picture both supporting and challenging strength of weak ties theory. Specifically, the final chapter discusses these findings and concludes that the modern job search does not follow the premises accepted by most strength of weak ties research. Implications of research findings in three major areas: (a) the social and material similarities and differences between human and non-human information sources are discussed, (b) the situated use of ties is explored using the lens of latent tie theory, (c) the implications for social network analysis are detailed at multiple levels

INVESTIGATING THE CO-EVOLUTION OF INDIVIDUAL AND NETWORK-LEVEL RECOVERY CAPITAL: A DYNAMIC SOCIAL NETWORK ANALYSIS TESTING NETWORK COHESION AND EXCHANGE THEORIES

Historically, treatment professionals, researchers, and policymakers widely regarded substance use disorders (SUDs) as acute conditions that patients could “recover” from after a single treatment. Recent efforts have redefined recovery as a lifelong, dynamic process that involves improvements in multiple domains over time. Thus, recovery capital frameworks and theory have gained momentum as a way to operationalize recovery from SUDs. Recovery capital is a multifaceted framework with theoretical underpinnings in the social capital literature that provides a way of conceptualizing and measuring the complexities of the recovery process. While the literature on recovery capital has grown significantly since its conception, the extant research has focused on investigating recovery capital at the individual-level and not on how it is developed contextually. The current longitudinal study sought to advance understanding of how recovery capital is developed using social network analysis while testing network cohesion, social exchange, and generalized exchange theories. Stochastic Actor Oriented Modeling was conducted on individuals recovering from SUDs (N = 627) residing in 42 recovery homes. Findings indicated that while cohesion, social exchanges, and generalized exchanges were prevalent across various types of networks, these network-level effects had no influences on changes in the individual-level of recovery capital. However, a dyadic-level effect was found, indicating that residents’ individual-level recovery capital increased when they were directly connected to those rich in recovery capital. Additionally, compared to men, women had slower increases in their recovery capital over time. The theoretical and practical implications and recommendations for future research are discussed

Legal Networks: The Promises and Challenges of Legal Network Analysis

Article published in the Michigan State Law Review

Multichannel Social Signatures and Persistent Features of Egocentric Networks

Mobile phones are perfect sensors for capturing the behavior of people. They are widespread personal devices that we carry around all day. Modern smartphones, equipped with an arsenal of various sensors, monitor their environments and also their owners. However, even the simplest mobile phone device, when used with a SIM card, can collect rich behavioral data. Call Detail Records (CDRs), collected by telecommunication companies for billing purposes, contain detailed information on communication behavior of the users which can not be collected by traditional data collection methods such as questionnaires. Scientists have used CDRs to study the structure and dynamics of societal-level communication networks as well as the properties of egocentric networks. The structure of weighted egocentric networks can be quantified with the so-called social signatures. It is known that call-based social signatures are distinct and persistent at the individual level. However, calling is just one of the several channels that people use to communicate. To get a more realistic picture of people's social behavior we should include more communication channels. However, because of their intrinsic differences, it is challenging to combine the usage frequencies on multiple channels into single combined weights. In this Thesis, we propose a method for determining link weights which enables us to compare the egocentric networks across different channels and also to construct multichannel egocentric networks and multichannel social signatures. Using two different datasets on calling and texting behavior of people, we observed that similarly to call signatures, text-message signatures and multichannel signatures (combining information on calls and texts) are also persistent in time. Moreover, we observed that even though people call and text different sets of people, their call and text signatures are similar in shape. In other words, the shapes of our social signatures--which are distinct from signatures of others--seem to be independent of the communication channel or the people whom we contact. Further research is needed to explain the mechanism behind these shapes and to investigate the roots of persistence and stability of social signatures