Dissertations / Theses on the topic 'Social network analysis'
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Junuthula, Ruthwik Reddy. "Modeling, Evaluation and Analysis of Dynamic Networks for Social Network Analysis." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1544819215833249.
Full textCURZI, MIRCO. "Content based social network analysis." Doctoral thesis, Università Politecnica delle Marche, 2009. http://hdl.handle.net/11566/242305.
Full textATHANASIOU, THOMAS. "Multi-dimensional analysis of social multi-networks : Analysing a 5-layer social network case study." Thesis, Uppsala universitet, Institutionen för informatik och media, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-273908.
Full textVetro, Carla. "La social network analysis nella valutazione delle politiche sociali." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/341.
Full textIl tema della valutazione emerge periodicamente nella discussione politica italiana. L’azione del valutare, che rappresenta ormai un’operazione ricorrente nella vita quotidiana, diviene una pratica consolidata anche in seno alle istituzioni pubbliche, indispensabile per costruire un giudizio sul funzionamento delle politiche stesse. La pratica valutativa si rivela, però, difficile da applicare in contesti complessi e dinamici come quelli che caratterizzano gli interventi nel sociale, dove la complessità attiene alla eterogeneità e pluralità di attori coinvolti e alla multiproblematicità dei bisogni territoriali. Quando la riuscita di una politica di intervento dipende non solo dalle capacità di coordinamento dall’alto, cioè di chi programma gli interventi sociali e offre i servizi per rispondere ai bisogni di una comunità, ma anche dalla volontà e dalla partecipazione dal basso, cioè di chi fruisce degli interventi, risulta chiaro quanto un processo di valutazione diventi complesso. In tali situazioni, le tecniche della Social Network Analysis (di seguito analisi delle reti sociali) risultano particolarmente adatte a rilevare, studiare ed interpretare le interazioni di tutti gli attori coinvolti in uno o più interventi di politica sociale. Tali tecniche di analisi vengono utilizzate sempre più spesso nella ricerca valutativa, in quanto si presuppone che ci possa essere una relazione fra le caratteristiche della rete, costituita dagli attori sociali coinvolti nell’attuazione di un programma, e l’efficacia del programma stesso. [a cura dell'autore]
IX n.s.
Magnusson, Jonathan. "Social Network Analysis Utilizing Big Data Technology." Thesis, Uppsala universitet, Avdelningen för datalogi, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-170926.
Full textHildorsson, Fredrik. "Scalable Solutions for Social Network Analysis." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-110548.
Full textA telecom operator can get a lot of high quality intelligence by studying the social network of its subscribers. One way to generate such a social network is to study the calls between the subscribers. Social networks generated from telecom networks can consist of millions of subscribers and the majority of the current social network analysis algorithms are too slow to analyze large networks. This master's thesis' objective is to find a more scalable solution to analyze social networks.
The work was divided into three steps; a survey of the existing solutions and algorithms, a pre-study to verify limitations of existing solutions and test some ideas and from the result of the pre-study and the survey a prototype was planned and implemented.
From the pre-study it was clear that the current solutions both took too long and used too much memory to be possible to use on a large social network. A number of algorithms were tested and from those a few was chosen to be implemented in the prototype. To help with the memory and time consumption the solution was also parallelized by using a partitioning algorithm to divide the graph into separate pieces on which each algorithm could run locally.The partitioning algorithm failed to scale well due to an internal modification of the partitioning scheme to adapt the partitioning to social graphs and simplify the parallelization. All but one algorithm scaled well and they were considerably faster than the original algorithms.
Grant, Eli. "Network analysis for social programme evaluation." Thesis, University of Oxford, 2015. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.719991.
Full textFICARA, Annamaria. "Social network analysis approaches to study crime." Doctoral thesis, Università degli Studi di Palermo, 2022. http://hdl.handle.net/10447/537005.
Full textMoore, John David. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2005. http://hdl.handle.net/10919/103640.
Full textDoctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
Moore, John. "Making Sense of Networks: Exploring How Network Participants Understand and Use Information From Social Network Analysis." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103640.
Full textDoctor of Philosophy
Many of today's complex public issues are best addressed by multi-sectoral multi-organizational responses that include different types of organizations working together (Kettl, 2008; O'Toole, 1997). Social network analysis (SNA) of interorganizational networks has emerged as a useful tool for network managers to understand the structure and function of the complex networks in which they seek to manage (Human and Provan, 1997, 2000; Provan and Milward, 1995; Provan, Sebastian, and Milward, 1996; Provan, Veazie, Staten, and Teufel-Shone, 2005). The output of an interorganizational SNA typically provides a range of information to network managers including network plots. The network plots provide visual representations of different aspects of the network by showing the kinds of ties between the actors in the network. The information from network analyses can help network managers encourage systems thinking, see the different roles played by organizations, or identify links to outside resources among many other uses, but "will only have practical value to communities if it can be effectively presented, discussed, accepted, and acted on by community leaders and network participants [emphasis added]." (Provan et al., 2005, p. 610). However, little is currently known about if or how the information embedded in network plots is accepted or acted on by network participants. The visual representations of the network (network plots) provided to network participants following a SNA are often open to a range of interpretations that may or may not align with the findings of the analyst or the intended use by network managers, raising many interesting questions. Little is currently known about how differently situated network participants might interpret the same network plots differently. Nor do we understand what factors might influence different individuals or organizations to come up with different interpretations. After conducting a SNA and presenting it to network participants, I conducted interviews with a range of different representatives from participating organizations. I used a particular form of semi-structured interview, a situated micro-element interview from Dervin's Sense Making Methodology (SMM) (Dervin, Foreman-Wernet, and Lauterbach, 2003). I then analyzed the interview transcripts using standard qualitative coding methods (Bailey, 2007) to see if themes emerged that addressed the research questions. I found that most informants had trouble extracting information and meaning from their examination of the plots without that meaning and interpretation being provided by the expert analyst. I posit some potential explanations for why that might be so in the case I studied. I then turn to some interesting methodological considerations that emerged from taking the perspectives of network participants seriously. Finally, I synthesize the subject area and methodological findings into a refined framework for sense-making around network plots and offer propositions and potential approaches for future research.
Rajasekaran, Sathya Dev Squicciarini Anna C. Metzner John J. "Social network risk analysis and privacy framework." [University Park, Pa.] : Pennsylvania State University, 2009. http://etda.libraries.psu.edu/theses/approved/WorldWideIndex/ETD-4812/index.html.
Full textAfrasiabi, Rad Amir. "Social Network Analysis and Time Varying Graphs." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/34441.
Full textRezaee, Shaliz. "E-mail Prioritization through Social Network Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för datavetenskap och kommunikation, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3356.
Full textThis thesis has addressed E-mail prioritization through social network by using social information. The task has been done by focusing on the interaction and similarity between friends in the OSN. A theoretical analysis has been performed in order to identify the characteristic of suitable trust model. An algorithm (Algorithm 1) has been suggested to estimate weights of different criteria of social information. In order to have the trust predictions based on the user‟s preferences, the algorithm adjusted the weights based on the user‟s feedback. In addition, another algorithm (Algorithm 2) has been proposed to compute trust scores and prioritize E-mails inbox. Finally, an algorithm (Algorithm 3) has been presented to evaluate the error of the computed (predicted) trust scores. In order to display the applicability of the method as well as to motivate the theoretical foundation, a case study was reported in which the proposed method was applied to Facebook. The analysis showed that the proposed method was feasible to be used, and it provided users a mean to prioritize E-mail inboxes based on the social information extracted from Facebook. The analysis indicated that least squares method was a suitable approach to estimate weights that were used in computing trust scores and thus prioritizing E-mails inbox.
Wang, Xin. "Graph pattern matching on social network analysis." Thesis, University of Edinburgh, 2013. http://hdl.handle.net/1842/8277.
Full textBohn, Angela, Norbert Walchhofer, Patrick Mair, and Kurt Hornik. "Social Network Analysis of Weighted Telecommunications Graphs." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2009. http://epub.wu.ac.at/708/1/document.pdf.
Full textSeries: Research Report Series / Department of Statistics and Mathematics
Nohuddin, Puteri. "Predictive trend mining for social network analysis." Thesis, University of Liverpool, 2012. http://livrepository.liverpool.ac.uk/7153/.
Full textHu, Daning. "Analysis and Applications of Social Network Formation." Diss., The University of Arizona, 2009. http://hdl.handle.net/10150/145710.
Full textMACCAGNOLA, DANIELE. "Relational Learning Models for Social Network Analysis." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2016. http://hdl.handle.net/10281/100459.
Full textKibanov, Mark [Verfasser]. "Social Network Mining for Analysis of Social Phenomena / Mark Kibanov." Kassel : Universitätsbibliothek Kassel, 2019. http://d-nb.info/1193090261/34.
Full textLee, Changheon. "Dynamics of Advice Network and Knowledge Contribution: A Longitudinal Social Network Analysis." Diss., The University of Arizona, 2012. http://hdl.handle.net/10150/243117.
Full textGiles-Summers, Brandon. "Targeting Social Network Analysis in Counter IED Operations." Thesis, Monterey, California. Naval Postgraduate School, 2011. http://hdl.handle.net/10945/5703.
Full textThe purpose of this research is to provide insights to Commanders in the field for attack-the-network (AtN) operations in the fight against Improved Explosive Devices (IED). Established in 2006, the Improved Explosive Devices Defeat Organization (JIEDDO) has spent billions of dollars to execute its operational mandate: defeat the device, attack the network, and train the force. JIEDDO has excelled in training the force and defeating the device, but lagged behind in providing necessary information to facilitate attack-the-network operations. To facilitate AtN operations, JIEDDO created a Counter-IED Operation Integration Center (COIC), which provides analysis, but utilizes metrics that are not necessarily intuitive. Rather than metrics, what commanders need is a clear understanding of what attack the network means in order to create lines of operations that undermine networks that use IEDs. The goal of this thesis, therefore, is to define attack-the-network, introduce social network analysis, provide a focused discussion on how to apply social relational information to operations, determine a targeted person's relevance, provide operational commanders with a basic matrix to gain perspective on social interactions of network members, and offer case studies illuminating the difficulties inherent in network targeting.
Abbas, Syed Muhammad Ali. "Design and analysis of social network systems (SNS)." Thesis, Manchester Metropolitan University, 2016. http://e-space.mmu.ac.uk/619490/.
Full textBohn, Angela, Ingo Feinerer, Kurt Hornik, and Patrick Mair. "Content-Based Social Network Analysis of Mailing Lists." The R Foundation for Statistical Computing, 2011. http://epub.wu.ac.at/5435/1/RJournal_2011%2D1_Bohn~et~al.pdf.
Full textPan, Long. "Effective and Efficient Methodologies for Social Network Analysis." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/25962.
Full textPh. D.
Zhao, Meng John. "Analysis and Evaluation of Social Network Anomaly Detection." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/79849.
Full textPh. D.
Yu, En. "Social Network Analysis Applied to Ontology 3D Visualization." Miami University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=miami1206497854.
Full textCaulfield, John. "A social network analysis of Irish language use in social media." Thesis, Cardiff University, 2013. http://orca.cf.ac.uk/53228/.
Full textFidalgo, Patrícia Seferlis Pereira. "Learning networks and moodle use in online courses: a social network analysis study." Doctoral thesis, Faculdade de Ciências e Tecnologia, 2012. http://hdl.handle.net/10362/8862.
Full textThis research presents a case study on the interactions between the participants of the forums of four online undergraduate courses from the perspective of social network analysis (SNA). Due to lack of studies on social networks in online learning environments in higher education in Portugal we have choose a qualitative structural analysis to address this phenomenon. The context of this work was given by the new experiences in distance education (DE) that many institutions have been making. Those experiences are a function of the changes in educational paradigms and due to a wider adoption of Information and Communication Technologies (ICT) from schools as well as to the competitive market. Among the technologies adopted by universities are the Learning Management Systems (LMSs) that allow recording, storing and using large amounts of relational data about their users and that can be accessed through Webtracking. We have used this information to construct matrices that allowed the SNA. In order to deepen knowledge about the four online courses we were studying we have also collect data with questionnaires and interviews and we did a content analysis to the participations in the forums. The three main sources of data collection led us to three types of analysis: SNA, statistical analysis and content analysis. These types of analysis allowed, in turn, a three-dimensional study on the use of the LMS: 1) the relational dimension through the study of forums networks and patterns of interaction among participants in those networks, 2) the dimension relative to the process of teaching and learning through content analysis of the interviews; 3) and finally the dimension related to the participants' perceptions about the use of LMS for educational purposes and as a platform for creating social networks through the analysis of questionnaires.With the results obtained we carried out a comparative study between the four courses and tried to present a reflection on the Online Project of the University as well as possible causes that led to what was observed. We have finished with a proposal of a framework for studying the relational aspects of online learning networks aimed at possible future research in this area.
Trier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-140161.
Full textTrier, Matthias. "Towards a Social Network Intelligence Tool for visual Analysis of Virtual Communication Networks." Technische Universität Dresden, 2006. https://tud.qucosa.de/id/qucosa%3A27871.
Full textCimenler, Oguz. "Social Network Analysis of Researchers' Communication and Collaborative Networks Using Self-reported Data." Scholar Commons, 2014. https://scholarcommons.usf.edu/etd/5201.
Full textFranco, Alessia <1996>. "How Blockchain Technology Can Help Rearchitect Social Networks: An Analysis of Desmos Network." Master's Degree Thesis, Università Ca' Foscari Venezia, 2021. http://hdl.handle.net/10579/19800.
Full textOrkins, William R., and Carla A. Kiernan. "COREnet: the fusion of social network analysis and target audience analysis." Thesis, Monterey, California: Naval Postgraduate School, 2014. http://hdl.handle.net/10945/44638.
Full textThe purpose of this capstone is to highlight and explain how the target audience analysis (TAA) process can be enhanced by incorporating aspects of influence theory, social movement theory (SMT) and social network analysis (SNA). While a large body of literature addresses influence theory, SMT and SNA, little has been written within military information support operations (MISO) doctrine recognizing SNA in the analytical process. This capstone creates a method to apply SNA, SMT, and influence theory to existing MISO doctrine while also developing a scalable web-based application that assists with visualizing and analyzing open source data to draw meaningful conclusions and assist decision making on given operational problem sets. The web-based interface, COREnet, is a high fidelity prototype derived completely from open- source technology. The examples utilized are from a 2006 data set of an Indonesian terrorist network to demonstrate how SNA can be fully integrated into the TAA process.
Broccatelli, Chiara. "Going beyond secrecy : methodological advances for two-mode temporal criminal networks with Social Network Analysis." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/going-beyond-secrecy-methodological-advances-for-twomode-temporal-criminal-networks-with-social-network-analysis(f0f91f79-7bc3-442c-a16b-e9cf44cc68c3).html.
Full textRubano, Vincent. "Social network analysis| Determining betweenness centrality of a network using Ant Colony Optimization." Thesis, Southern Connecticut State University, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10108549.
Full textBetweenness centrality refers to the measure of a node’s influence on the transfer of items within a network. It is a mechanism used to identify participants within an interconnected system that are responsible for processing high frequencies of traffic. This thesis examines the performance characteristics of a specialized artificial intelligence algorithm known as Ant Colony Optimization and its application in the field of social network analysis. The modeling and examination of such algorithms is important largely because of its ability to span across multiple fields of study as well as a variety of network applications. The effects of network analysis can be felt everywhere. Business and military intelligence; hardware resiliency (fault tolerance); network routing, are but a few of the fields that can and do benefit from research due in part to specialized network analysis. In this research paper, extensive social networks are built, execution time is measured, and algorithm viability is tested through the identification of high frequency nodes within real social networks.
Rocco, Giulia Eleonora <1990>. "L'incidenza dei Social Media nell'e-commerce e utilità della Social Network Analysis." Master's Degree Thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/5287.
Full textIsah, Haruna. "Social Data Mining for Crime Intelligence: Contributions to Social Data Quality Assessment and Prediction Methods." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/16066.
Full textJenelius, Erik. "Approaches to road network vulnerability analysis." Licentiate thesis, Stockholm : Infrastruktur, Kungliga Tekniska högskolan, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-4518.
Full textLospinoso, Joshua Alfred. "Statistical models for social network dynamics." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:d5ed9b9c-020c-4379-a5f2-cf96439ca37c.
Full textEiesland, Jon Wostryck. "Communities in a large social network : visualization and analysis." Thesis, Norwegian University of Science and Technology, Department of Physics, 2009. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-6409.
Full textCommunities have been a hot topic in complex network research the last years. Several algorithms for detecting communities have been developed, and in this thesis we use the sequential clique percolation algorithm to detect communities in a large social network. Our network consists of 5.3 million mobile phone users, with mutual communication data aggregated over 18 weeks.
In this thesis we do a visual study of the communities, and we clearly see the nested community structure when we do clique percolation for dierent clique sizes. When we threshold the edge weights we see that the strongest edges are in the densest subcommunities and that the weakest edges keep the communities connected.
We also present numerical analysis of some selected structure and topology properties of the communities. Lastly we confirm, by numerical analysis of the available demographic data on the mobile phone users, that the communities are more conform with respect to zip code, age and sex compared to a reference network where the demographic attributes have been shuffled.
Samfunn har vært et hett emne innen forskning på komplekse nettverk de siste årene. Det har blitt utviklet flere algoritmer for å finne samfunn, og i denne oppgaven bruker vi sekvensiell klikkperkolasjon til å finne samfunn i et stort sosialt nettverk. Nettverket vårt består av 5.3 millioner mobiltelefonbrukere, med gjensidig kommunikasjonsdata aggregert over 18 uker.
I denne oppgaven gjør vi en visuell studie av samfunnene, og vi ser tydelig den vevde sammfunnsstrukturen når vi utfører klikkperkolasjon for ulike klikkstørrelser. Når vi setter terskler for lenkevektene ser vi at de sterkeste lenkene er i de tetteste undersamfunnene og at de svakeste lenkene holder samfunnene i kontakt med hverandre.
Vi presenterer også en numerisk analyse av noen utvalgte struktur- og topologiegenskaper hos samfunnene. Til slutt bekrefter vi, via numerisk analyse av den tilgjengelige demografiske informasjonen om mobiltelefonbrukerne, at samfunnene er mer konforme med tanke på postkode, alder og kjønn sammenlignet med et referansenettverk hvor de demografiske attributtene har blitt stokket om.
Zheng, Ju Kimberly. "A Social Network Analysis of Corporate Venture Capital Syndication." Thesis, University of Waterloo, 2004. http://hdl.handle.net/10012/854.
Full textChandler, Kathryn Suzanne. "Exploring the principle of provenance with social network analysis." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/57849.
Full textArts, Faculty of
Library, Archival and Information Studies (SLAIS), School of
Graduate
Maier, Gunther, and Michael Vyborny. "Internal migration between US-states. A social network analysis." Institut für Regional- und Umweltwirtschaft, WU Vienna University of Economics and Business, 2005. http://epub.wu.ac.at/1084/1/document.pdf.
Full textSeries: SRE - Discussion Papers
Lau, Dora C. S. "Job consequences of trustworthy employees, a social network analysis." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/NQ61133.pdf.
Full textAdjodah, Dhaval D. K. (Adjodlah Dhaval Dhamnidhi Kumar). "Understanding social influence using network analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/81111.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 61-62).
If we are to enact better policy, fight crime and decrease poverty, we will need better computational models of how society works. In order to make computational social science a useful reality, we will need generative models of how social influence sprouts at the interpersonal level and how it leads to emergent social behavior. In this thesis, I take steps at understanding the predictors and conduits of social influence by analyzing real-life data, and I use the findings to create a high-accuracy prediction model of individuals' future behavior. The funf dataset which comprises detailed high-frequency data gathered from 25 mobile phone-based signals from 130 people over a period of 15 months, will be used to test the hypothesis that people who interact more with each other have a greater ability to influence each other. Various metrics of interaction will be investigated such as self-reported friendships, call and SMS logs and Bluetooth co-location signals. The Burt Network Constraint of each pair of participants is calculated as a measure of not only the direct interaction between two participants but also the indirect friendships through intermediate neighbors that form closed triads with both the participants being assessed. To measure influence, the results of the live funf intervention will be used where behavior change of each participant to be more physically active was rewarded, with the reward being calculated live. There were three variants of the reward structure: one where each participant was rewarded for her own behavior change without seeing that of anybody else (the control), one where each participant was paired up with two 'buddies' whose behavior change she could see live but she was still rewarded based on her own behavior, and one where each participant who was paired with two others was paid based on their behavior change that she could see live. As a metric for social influence, it will be considered how the change in slope and average physical activity levels of one person follows the change in slope and average physical activity levels of the buddy who saw her data and/or was rewarded based on her performance. Finally, a linear regression model that uses the various types of direction and indirect network interactions will be created to predict the behavior change of one participant based on her closeness with her buddy. In addition to explaining and demonstrating the causes of social influence with unprecedented detail using network analysis and machine learning, I will discuss the larger topic of using such a technology-driven approach to changing behavior instead of the traditional policy-driven approach. The advantages of the technology-driven approach will be highlighted and the potential political-economic pitfalls of implementing such a novel approach will also be addressed. Since technology-driven approaches to changing individual behavior can have serious negative consequences for democracy and the free-market, I will introduce a novel dimension to the discussion of how to protect individuals from the state and from powerful private organizations. Hence, I will describe how transparency policies and civic engagement technologies can further this goal of 'watching the watchers'.
by Dhaval D.K. Adjodah.
S.M.in Technology and Policy
Zhang, Kunpeng, Siddhartha Bhattacharyya, and Sudha Ram. "LARGE-SCALE NETWORK ANALYSIS FOR ONLINE SOCIAL BRAND ADVERTISING." SOC INFORM MANAGE-MIS RES CENT, 2016. http://hdl.handle.net/10150/623353.
Full textBuchanan, Courtney Nicole, and Courtney Nicole Buchanan. "Network Analysis of “C” Level Executives on Social Media." Thesis, The University of Arizona, 2017. http://hdl.handle.net/10150/624931.
Full textNarayanam, Ramasuri. "Game Theoretic Models For Social Network Analysis." Thesis, 2011. https://etd.iisc.ac.in/handle/2005/2350.
Full textNarayanam, Ramasuri. "Game Theoretic Models For Social Network Analysis." Thesis, 2011. http://etd.iisc.ernet.in/handle/2005/2350.
Full textNaimisha, Kolli. "Applications Of Social Network Analysis To Community Dynamics." Thesis, 2008. https://etd.iisc.ac.in/handle/2005/834.
Full text