Browsing by Subject "Social Network Analysis"

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Achieving Equitable and Effective Community Engagement through Social and Relational Network Analysis
    (2024-05-01) Cazares-Reyes , Jesus; Grimlund , Terri; Sniegowski, Erica; Soria, Alejandra
    Through the Humphrey School of Public Affairs capstone program, a student-consultant team of four graduate students worked with their capstone client, the City of Minnetonka, to better understand social networks within the City of Minnetonka and the impacts social networks may have on equitable community engagement. The City of Minnetonka is situated in Hennepin County and has a population of approximately 54,000 (Metropolitan Council, n.d.). Community inclusiveness is a strategic priority of the City of Minnetonka, which includes actively engaging the community to achieve broader policy outcomes, respond to community needs, and remove barriers for participating in programs and services. City staff proposed that, through a better understanding of social and relational networks within the city and between the city and community, the city can develop more effective and equitable public engagement and work towards achieving community inclusiveness. The team focused on understanding the social networks of City of Minnetonka staff and the nature of relationships between city staff and community members. This was informed by background research on social networks, social network analysis, and equitable engagement. Social networks are composed of network members, which can be people or organizations, that are connected to other members through one or more relations (Marin & Wellman, 2009). Through social network analysis, which is the visualization and analysis of network members and their relations, this project sought to bring greater understanding of the social networks among city staff and between city staff and community members.
  • Thumbnail Image
    Item
    Determining influence in social networks using Social Capital
    (2014-05) Sharma, Dhruv
    The proliferation of online social networks enables the influence of a person or an event to propagate to every corner of the globe in a very short duration of time. The problem of identifying such key sources of influence is important for a wide variety of applications from sales and marketing to public health and policies. Most of the existing methods for identifying influencers use the process of information diffusion to discover the nodes (people) with the maximum expected information spread. In this work we have developed a novel method for identifying key influencers in a given network. This method works on the premise that people generate more value for their work by collaborating with peers having high influence. The social value generated through such collaborations denotes the notion of individual social capital. At the core of this method we use the popular valuation-allocation approach for finding the individual social capital value. In this approach first we determine the value of the entire network using a valuation function and then we do a fair allocation of this entire network's value amongst the participating nodes (people). We show that our Valuation and Allocation functions satisfy several axioms of fairness and fall under the Myerson's allocation rule class.Also, we implement our allocation rule using an efficient algorithm and show that our algorithm outperforms the baselines in several real life datasets. Especially, for the DBLP collaboration network our algorithm outperforms PageRank, PMIA and Weighted Degree baselines by up to 8% in terms of precision recall and F1-measure.Furthermore, we use Hypergraphs as a tool to model group collaborations more effectively and empirically show the superiority of hypergraph edge weights as compared to dyadic edge weights for identifying influencers.To conclude with we discuss a couple of popular distributed programming paradigms, namely MapReduce and BSP (Bulk Synchronous Parallel) and the implementation of the algorithm on these.
  • Thumbnail Image
    Item
    Leveraging Informatics To Understand Online Communication Patterns Between Migraine Sufferers On Social Media
    (2021-03) Gomaa, Basma
    Social Network Sites (SNS) have been widely used by patients to exchange emotional support and health information in several health conditions. However, migraine patients’ use of social media is under- investigated. In this study, we investigated migraine patients’ behavior on two social media platforms by applying “Content” and “Social Network Analysis” techniques. The study aimed to compare the connectivity by describing the network structure, tie characteristics, in addition to identifying conversational themes. The migraine network on Twitter is more connected than the Facebook network, indicating more information and emotional support exchanged on Twitter. The informational theme dominated over the emotional theme on both platforms. However, the quality of information exchanged on Facebook was better than on Twitter which contained misinformation, spam and advertisements. The study highlights the vital role of moderators and sheds light on the technical features unique to each platform and impact on the users’ engagement patterns. The study provides guidance to intervention designers, online community managers and public health officials regarding the appropriate platform with specific technical features that will address the unique needs for migraine patients. More studies are needed, however, about the connection between technology, patients and disease conditions.
  • Thumbnail Image
    Item
    The Social Networks: Characterizing L2 Spanish Proficiency Development in Study Abroad through Social Network Analysis
    (2020-06) Strawbridge, Arthur
    This study examines the social network development of L2 Spanish learners over the course of an academic semester spent studying abroad in Spain. Social network analysis has been utilized widely in sociological research to understand and predict a range of human behaviors, and has also been applied to research in sociolinguistics (Milroy, 1980) and second language acquisition (SLA) (e.g., Baker-Smemoe et al., 2014; Isabelli-García, 2006; Mitchell et al., 2017). The present investigation proposes to utilize this level of analysis to identify patterns in university sojourners’ social experience, to describe the relationship between these patterns and L2 Spanish proficiency development, and to contextualize these findings within the landscape of contemporary U.S. study abroad practice. HASH(0x41e8010) Data for this investigation were collected from 43 L2 Spanish learners who were university students enrolled in various institutions in the U.S., and who spent the spring 2019 academic semester studying abroad in Spain. Students were recruited from six study abroad programs located in four cities in Spain (Granada, Madrid, Sevilla, Toledo). Social network data were collected via a specially designed social network questionnaire, while linguistic data were collected via the administration of two Spanish language proficiency tests, a Diploma de Español como Lengua Extranjera (DELE) exam and an elicited imitation task (EIT) (Ortega, Iwashita, Norris, & Rabie, 1999). Data collection was performed at the beginning and end of the participants’ sojourn in Spain, a period of time lasting approximately 12 weeks. HASH(0x41dc1d8) The results of this study identify four prominent social network patterns exhibited by students during their time abroad, characterized by varying levels of Spanish language use, emotional proximity to contacts, frequency of interaction, contact nationality, and network cohesion. These patterns exert a significant influence on learners’ gains in Spanish proficiency, with the most powerful influences on proficiency gains being the development of Spanish language-dominant social networks with NNS program peers, as well as the development of high numbers of integrated NS/NNS social groups. These findings are discussed in the context of the current state of U.S. study abroad programming.
  • Thumbnail Image
    Item
    Sport Brand Communities: A Social Network Analysis Perspective
    (2015-08) Lupinek, Joshua
    This dissertation was created and bound by an “alternative format” where three separate journal articles were created in a sequence that ties brand community literature, social network analysis (SNA) literature, and an empirical case study together. Paper #1 (Chapter 2) serves as conceptual literature review paper which traces the evolution of brand community research from its beginnings in the general business literature to the current brand community research in sport marketing today. Muniz and O’Guinn (2001) define brand communities as a specialized and non-geographically bound community based around a set of structured social relationships amongst admirers of a brand, and are often recognized as the most integral relationship component of consumers to brands (Muge & Ozge, 2013). An attachment to brand community (ABC) framework is proposed through variables gathered in a review of brand community literature. Paper #2 (Chapter 3) is a conceptual paper that proposes several brand community sport marketing applications for the emergent SNA methodology from a foundation of relevant literature. The conceptual direction and methodological techniques of SNA in areas such as fan identification, team success, player movement, internal marketing, marketing to the lifetime fan, and small fan groups as well as subcultural analyses were explicitly utilized. Paper #4 (Chapter 4) is an exploratory analysis of a single off-site fan group, which produced an applicable fit to the Attachment to Brand Community (ABC) framework revealing consumer brand loyalty group structure towards practical marketing implications.