Matching Items (29)
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- All Subjects: Social Media
- Creators: Liu, Huan
Description
This dissertation considers why several characters on the Early Modern Stage choose to remain silent when speech seems warranted. By examining the circumstances and effects of self-silencing on both the character and his/her community, I argue that silencing is an exercise of power that simultaneously subjectifies the silent one and compels the community (textual or theatrical) to ethical self-examination. This argument engages primarily with social philosophers Pierre Bourdieu, Alain Badiou, and Emmanual Levinas, considering their sometimes contradictory ideas about the ontology and representation of the subject and the construction of community. Set alongside the Early Modern plays of William Shakespeare, Ben Jonson and Thomas Kyd, these theories reveal a rich functionality of self-silencing in the contexts of gender relations, aberrant sociality, and ethical crisis. This multi-faceted functionality creates a singular subject, establishes a space for the simultaneous existence of the subject and his/her community, offers an opportunity for empathetic mirroring and/or insight, and thereby leads to social unification. Silence is, in its effects, creative: it engenders empathy and ethical self- and social-reflection.
ContributorsKrouse, Penelope (Author) / Perry, Curtis (Thesis advisor) / Thompson, Ayanna T (Thesis advisor) / Fox, Cora V (Committee member) / Arizona State University (Publisher)
Created2011
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
This thesis aims to analyze and explain the resurgence of the superhero genre, particularly in recent cinema, directly following the terrorist attacks on September 11, 2001. It will also deconstruct the current American political landscape and define how popular culture has historically reflected real-world issues. The study draws heavily on the political ideology of neoliberalism and Henry Jenkins' media theory of convergence culture. I ultimately argue in the course of the analysis that viewers of these superhero films, regardless of their interest in comic books, cathartically release their fears and post-9/11 anxiety through cinematic escapism. It will also relay the evolution of the superhero in the last seventy years as a way to show the effects current events have on popular culture and history, using Captain America and Iron Man as examples of shifting American values.
ContributorsWalker, Lindsay Anne (Author) / Facinelli, Diane (Thesis director) / Himberg, Julia (Committee member) / Barrett, The Honors College (Contributor) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Department of English (Contributor)
Created2014-05
Description
This Thesis presentation and book review is on social media manipulation and the issue of media algorithms developing a close minded perspective in individuals. It discusses the mechanics of these algorithms, the definition of social media manipulation, and the neutral negative impacts on the polarization of our country. It also goes into detail on how I applied this research to design projects throughout 4th year of the Visual Communication Design Program.
ContributorsRomero, Emily Gene (Author) / Sanft, Alfred (Thesis director) / Heywood, William (Committee member) / School of Human Evolution & Social Change (Contributor) / The Design School (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Media witnessing and storytelling for environmental justice (EJ) provide an avenue to understand the relationships between “multiple realities of environmental injury” and to analyze “fleeting phenomena with lasting form; thereby transforming phenomena that are experienced in a plurality of lives into publicly recognized history” (Houston, 2012, 419, 422). This creates opportunities to challenge and eradicate the oppressive structures that deem certain individuals and groups disposable and ultimately protect the possessive investment in whiteness. Therefore, for the purposes of EJ, media witnessing creates space for dynamic, citizen-based storytelling which can undermine narratives that promote the life versus economy framework that has perpetuated oppression, injustice, and state sanctioned violence. Media witnessing in an EJ context demonstrates the potential for collective understanding and action, political opportunities, and healing.<br/>This paper is an analysis of the process of media witnessing in regards to the Flint Water Crisis and the construction of the Dakota Access Pipeline (DAPL) and will apply an EJ lens to this phenomenon. It will discuss how media witnessing in response to these two crises can be used as a precedent for understanding and utilizing this framework and digital storytelling to address the crises of 2020, primarily the COVID-19 pandemic and racial injustice. It will then examine how the intersectionality of race, gender, and age has implications for future media witnessing and storytelling in the context of EJ movements. Finally, it will explain how media witnessing can motivate holistic policymaking in the favor of EJ initiatives and the health and wellbeing of all Americans, as well as how such policymaking and initiatives must acknowledge the double-edged sword that is social media.
ContributorsOConnell, Julia (Author) / Richter, Jennifer (Thesis director) / Adamson, Joni (Committee member) / School of Social Transformation (Contributor) / School of Human Evolution & Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
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
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
A 27k word reinterpretation of William Shakespeare's Romeo and Juliet, focusing on the originally-secondary character Rosaline Capulet and viewing the relationships portrayed between the other characters in a different light through her presence. With hefty consideration of the historical circumstances that existed during Shakespeare's time, including factors ranging from to the death of Shakespeare's son at the age of eleven to the common immigration/trade routes existing in the late 1500s to the ways in which historical figures navigated ideas of gender and sexuality, 'And Rosaline' aims to take a compassionate approach to the story of the Capulet and Montague families and the lives of those around them. Finalized for the purposes of the Barrett Honors Creative Project as a story created in an open source format known as Twine 2.0, produced by Twinery Inc, 'And Rosaline' will be a commercial project available for purchase in Q4 2017 later distributed in Ren'Py.
ContributorsPrice, Finn John (Author) / Himberg, Julia (Thesis director) / LaCroix, Kristin (Committee member) / Department of English (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12