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- All Subjects: Social Media
- All Subjects: Nonprofits
- Creators: Liu, Huan
Description
This thesis deals with the analysis of interpersonal communication dynamics in online social networks and social media. Our central hypothesis is that communication dynamics between individuals manifest themselves via three key aspects: the information that is the content of communication, the social engagement i.e. the sociological framework emergent of the communication process, and the channel i.e. the media via which communication takes place. Communication dynamics have been of interest to researchers from multi-faceted domains over the past several decades. However, today we are faced with several modern capabilities encompassing a host of social media websites. These sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information, our modes of social engagement, and mechanisms of how the media characteristics impact our interactional behavior. The outcomes of this research are manifold. We present our contributions in three parts, corresponding to the three key organizing ideas. First, we have observed that user context is key to characterizing communication between a pair of individuals. However interestingly, the probability of future communication seems to be more sensitive to the context compared to the delay, which appears to be rather habitual. Further, we observe that diffusion of social actions in a network can be indicative of future information cascades; that might be attributed to social influence or homophily depending on the nature of the social action. Second, we have observed that different modes of social engagement lead to evolution of groups that have considerable predictive capability in characterizing external-world temporal occurrences, such as stock market dynamics as well as collective political sentiments. Finally, characterization of communication on rich media sites have shown that conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Based on all these outcomes, we believe that this research can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.
ContributorsDe Choudhury, Munmun (Author) / Sundaram, Hari (Thesis advisor) / Candan, K. Selcuk (Committee member) / Liu, Huan (Committee member) / Watts, Duncan J. (Committee member) / Seligmann, Doree D. (Committee member) / Arizona State University (Publisher)
Created2011
Description
Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.
ContributorsCole, William David, M.S (Author) / Liu, Huan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Candan, Kasim S (Committee member) / Arizona State University (Publisher)
Created2011
Description
A statement appearing in social media provides a very significant challenge for determining the provenance of the statement. Provenance describes the origin, custody, and ownership of something. Most statements appearing in social media are not published with corresponding provenance data. However, the same characteristics that make the social media environment challenging, including the massive amounts of data available, large numbers of users, and a highly dynamic environment, provide unique and untapped opportunities for solving the provenance problem for social media. Current approaches for tracking provenance data do not scale for online social media and consequently there is a gap in provenance methodologies and technologies providing exciting research opportunities. The guiding vision is the use of social media information itself to realize a useful amount of provenance data for information in social media. This departs from traditional approaches for data provenance which rely on a central store of provenance information. The contemporary online social media environment is an enormous and constantly updated "central store" that can be mined for provenance information that is not readily made available to the average social media user. This research introduces an approach and builds a foundation aimed at realizing a provenance data capability for social media users that is not accessible today.
ContributorsBarbier, Geoffrey P (Author) / Liu, Huan (Thesis advisor) / Bell, Herbert (Committee member) / Li, Baoxin (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
The 284 residents of the rural community of Cooper Landing, Alaska are subject to many health risks. Cooper Landing is home to a large population of older adults whom suffer from a disproportionate physician to population ratio. Limited rural health care infrastructure and poor physician to population ratios are not conducive to primary health care implementation. Limited access to primary health care is linked to vast health disparities in rural communities like Cooper Landing. Preventive care and healthy lifestyle incentives have been largely overlooked as viable alternatives to primary health care access. In Cooper Landing, implementation of such incentives has proved to be either underutilized or unsuccessful by the private, public, and nonprofit sectors. To remedy this, the Rural Alaska Wellness Project (RAWP), a nonprofit organization, carries out its mission to promote health and wellness by providing a community resource for preventive care in Cooper Landing, Alaska. RAWP intends to increase the availability of the Cooper Landing School's gymnasium for community use, donate fitness equipment, implement TeleHealth initiatives, and host annual health fairs through grant funding, generous donations, and fundraising activities.
ContributorsNolan, Erin Sachi (Author) / Shockley, Gordon (Thesis director) / Hrncir, Shawn (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / Department of Psychology (Contributor)
Created2015-05
Description
The prevalence of bots, or automated accounts, on social media is a well-known problem. Some of the ways bots harm social media users include, but are not limited to, spreading misinformation, influencing topic discussions, and dispersing harmful links. Bots have affected the field of disaster relief on social media as well. These bots cause problems such as preventing rescuers from determining credible calls for help, spreading fake news and other malicious content, and generating large amounts of content which burdens rescuers attempting to provide aid in the aftermath of disasters. To address these problems, this research seeks to detect bots participating in disaster event related discussions and increase the recall, or number of bots removed from the network, of Twitter bot detection methods. The removal of these bots will also prevent human users from accidentally interacting with these bot accounts and being manipulated by them. To accomplish this goal, an existing bot detection classification algorithm known as BoostOR was employed. BoostOR is an ensemble learning algorithm originally modeled to increase bot detection recall in a dataset and it has the possibility to solve the social media bot dilemma where there may be several different types of bots in the data. BoostOR was first introduced as an adjustment to existing ensemble classifiers to increase recall. However, after testing the BoostOR algorithm on unobserved datasets, results showed that BoostOR does not perform as expected. This study attempts to improve the BoostOR algorithm by comparing it with a baseline classification algorithm, AdaBoost, and then discussing the intentional differences between the two. Additionally, this study presents the main factors which contribute to the shortcomings of the BoostOR algorithm and proposes a solution to improve it. These recommendations should ensure that the BoostOR algorithm can be applied to new and unobserved datasets in the future.
ContributorsDavis, Matthew William (Author) / Liu, Huan (Thesis director) / Nazer, Tahora H. (Committee member) / Computer Science and Engineering Program (Contributor, Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2018-12
Description
The beginnings of this paper developed from the initial question of: how can tribal nations create private economies on their reservations? Written and researched from an undergraduate student perspective, this paper begins to answer the question by analyzing the historical and current states of Indian Country's diverse tribal economies. Additionally, this paper will identify various tribal economic development challenges with a specific emphasis on education attainment as a key factor. Then, a solution will be presented in the form of a tribal business program modeled within the W.P. Carey School of Business at Arizona State University located in Tempe, Arizona. The solution is grounded in the idea that a highly qualified workforce is the best resource for economic development.
ContributorsTso, Cora Lee (Author) / Miller, Robert (Thesis director) / Hillman, Amy (Committee member) / School of Politics and Global Studies (Contributor) / American Indian Studies Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
Social media users are inundated with information. Especially on Instagram--a social media service based on sharing photos--where for many users, missing important posts is a common issue. By creating a recommendation system which learns each user's preference and gives them a curated list of posts, the information overload issue can be mediated in order to enhance the user experience for Instagram users. This paper explores methods for creating such a recommendation system. The proposed method employs a learning model called ``Factorization Machines" which combines the advantages of linear models and latent factor models. In this work I derived features from Instagram post data, including the image, social data about the post, and information about the user who created the post. I also collect user-post interaction data describing which users ``liked" which posts, and this was used in models leveraging latent factors. The proposed model successfully improves the rate of interesting content seen by the user by anywhere from 2 to 12 times.
ContributorsFakhri, Kian (Author) / Liu, Huan (Thesis director) / Morstatter, Fred (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
The ecological benefits provided by trees include improving air quality (Nowak, et. al., 2006), mitigating climate change by sequestering carbon (Nowak, 1993), providing animal habitats (Livingston, et. al., 2003), and reducing heat (Edmonson, 2016), among others. Trees also provide numerous social benefits, impacting urban sustainability in particular by improving human health (Salmond, 2016), aesthetically and economically improving neighborhoods (Torres, 2012), and contributing to thriving communities by creating gathering spaces and even reducing crime (Abraham, et. al., 2010). Because of the tremendous potential of trees to provide social and ecological services, particularly in urban areas, tree planting has become an important facet of many sustainability initiatives. This thesis assesses one such initiative aimed at planting trees for the diverse benefits they provide. Valley Permaculture Alliance (VPA), a nonprofit based in Phoenix, Arizona, is known for its Shade Tree Program. The author conducted an internal, quantitative assessment of the program between August and December of 2015. The assessment included evaluation of several indicators of ecological and community health related to the presence of shade trees, culminating in a report released in 2016. This paper evaluates the use of sustainability indicators in the VPA assessment as well as their value in different types of organizations. It culminates with an assessment of VPA's strengths, challenges faced by the organization, and suggestions for its future development.
ContributorsJones, Michaela Martine (Author) / Larson, Kelli (Thesis director) / Eakin, Hallie (Committee member) / School of Sustainability (Contributor) / American Indian Studies Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12
Description
This creative project aimed to combine knowledge, qualifications, and experience in networking and marketing to host a live music event. As a social media industry employee, an avid concertgoer, and a digital marketer, I felt sufficiently able to complete this task. The process included working within the Barrett budget to secure a venue and acts with the option of paid marketing for the event. Once I secured The Graduate Hotel and three acts— bands Study Habit and Moose Titans and DJ/emcee Malcolm Alexndr—it was time to publicize the event. I found a photographer and organized a photo shoot then created social media profiles and a website with these photos. In total, the attendance was roughly 100 people, and the night was a smash success.
Keywords: event planning, social media, music
Keywords: event planning, social media, music
ContributorsGilliam, Taylor Mikel (Author) / Shockley, Gordon (Thesis director) / Hultsman, Wendy (Committee member) / College of Integrative Sciences and Arts (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This creative project is a collection of profiles focused on Arizona nonprofits and refugees. The profiles share stories of refugees, volunteers, employees and others involved in the community serving refugees. Nonprofits are a vital resource for refugee resettlement. These organizations offer services to support refugees as they transition into new communities. Some services include: housing, English language learning, cultural orientation, job placement, medical treatment, education, and farming. Each of these programs support resiliency for refugees and for the communities in which they live. We Are Resilient was created first, to show the important role nonprofits have in serving refugees. Second, to connect people to a few of the stories and experiences within the Arizona refugee community. And third, to build understanding of the strength refugees bring to communities of Arizona and by extension the country. Visit weareresilientaz.com to learn more.
ContributorsGray, Elizabeth (Co-author) / Johnson, Kelcie (Co-author) / Shockley, Gordon (Thesis director) / O'Flaherty, Katherine (Committee member) / School of Community Resources and Development (Contributor) / School of Sustainability (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05