This study describes an ethnographic investigation of Communities of Play within Communities of Practice. As professional 'content creators', whose 'work' is 'play', are increasingly interacting with non-professional content consumers, whose 'play' is a kind of 'work', the barriers between 'work' and 'play' are increasingly dissolving. The purpose of this study was to examine the nature and frequency of the professionals' interactions with coworkers in an office and with the content consumers while working from home. Data were collected in the form of gameplay videos uploaded by the professional streamers from January of 2017 to March of 2018, downloaded by the researcher from Youtube.com and Twitch.tv. Interactions were coded according to Streamer environment (Work vs. Home) and type of interaction (Talk Aloud Protocol, Knowledge Exchange, or Noise). Six raters categorized interactions across 88 videos in ten-minute segments. Results indicated an effect of platform dependency between the Community of Practice on YouTube and the Community of Play on Twitch.tv. These differences, in both directionality and effect size, suggest that different 'content creator' behaviors are reinforced, depending on the platform used, and that a strategy that is successful on one platform may not be successful on another. Based off the researcher's experiences, recommendations are made for how future researchers can conduct effective ethnographic investigations for online Communities of Practice and Communities of Play.

Social media platforms have been growing at a rapid pace, attracting users' engagement with the online content due to their convenience facilitated by many useful features. Such platforms provide users with interactive options such as likes, dislikes as well as a way of expressing their opinions in the form of text (i.e., comments). As more people engage in different social media platforms, such platforms will increase in both size and importance. This growth in social media data is becoming a vital new area for scholars and researchers to explore this new form of communication. The huge data from social media has been a massive aid to researchers in the mission of exploring the public's behavior and opinion pursuing different venues in social media research. In recent years, social media platforms have facilitated the way people communicate and interact with each other. The recent approach in analyzing the human language in social media has been mostly powered by the use of Natural Language Processing (NLP) and deep learning techniques. NLP techniques are some of the most promising methods used in social media analyses, including content categorization, topic discovery and modeling, sentiment analysis. Such powerful methods have boosted the process of understanding human language by enabling researchers to aggregate data relating to certain events addressing several social issues. The ability of posting comments on these online platforms has allowed some users to post racist and obscene contents, and to spread hate on these platforms. In some cases, this kind of toxic behavior might turn the comment section from a space where users can share their views to a place where hate and profanity are spread. Such issues are observed across various social media platforms and many users are often exposed to these kinds of behaviors which requires comment moderators to spend a lot of time filtering out these inappropriate comments. Moreover, such textual "inappropriate contents" can be targeted towards users irrespective of age, concerning a variety of topics not only controversial, and triggered by various events. Our work is primarily focused on studying, detecting and analyzing users' exposure to this kind of toxicity on different social media platforms utilizing state-of-art techniques in both deep learning and natural language processing areas, and facilitated by exclusively collected and curated datasets that address various domains. The different domains, or applications, benefit from a unified and versatile pipeline that could be applied to various scenarios. Those applications we target in this dissertation are: (1) the detection and measurement of kids' exposure to inappropriate comments posted on YouTube videos targeting young users, (2) the association between topics of contents cover by mainstream news media and the toxicity of the comments and interactions by users, (3) the user interaction with, sentiment, and general behavior towards different topics discussed in social media platforms in light of major events (i.e., the outbreak of the COVID-19 pandemic). Our technical contribution is not limited to only the integration of the various techniques borrowed from the deep learning and natural language processing literature to those new and emerging problem spaces, for socially relevant computing problems, but also in comprehensively studying various approaches to determine their feasibility and relevant to the discussed problems, coupled with insights on the integration, as well as a rich set of conclusions supported with systematic measurements and in-depth analyses towards making the online space safer.

Social media use has grown exponentially world-wide. Nurses in the United States participate in social media for both professional and personal purposes. Positive and useful professional interactions often occur to foster relationships and share information, while personal interactions allow nurses to remain connected to friends and family. Often, boundaries between professional and personal opinions become easily blurred when using social media, and nurses who post uncivil and unprofessional content may face harsh consequences such as loss of employment. The COVID-19 pandemic has further increased social media use. For this research, a qualitative grounded theory approach was used to seek an understanding about the decision-making process by which active practicing professional nurses evaluate ethical choices when participating in social media and how the COVID-19 pandemic changed nurses' social media use. According to the participants in this study, nurses have multidimensional identities and interact on social media with differing enticements and motivations. These motivations combined with fear of consequences for unprofessionalism are balanced by the knowledge of professional laws and expectations. The outcomes of social media interactions, whether directly experienced or indirectly witnessed, impact future social media behaviors. A secondary analysis of the data revealed how the COVID-19 pandemic impacted the purpose for which nurses interacted in social media, changed the nurses' perception of the public opinions of nursing, and united nurses together.

Literature indicates that the United States has fallen behind other countries in the world in terms of creativity. At the same, children's self-motivated gaming and involvement in gaming communities have grown as a major pass time. The literature is inconsistent on the relationship between games and creativity with some indicating benefit while others indicate harm. Might the observed level of creative thinking among children be associated with the level of a priori self-motivated engagement with creative games and associated social media? Given the wide spectrum of the kinds of games, this research considers the "builder" (e.g. Minecraft) genre and related social media. The research question examined is, given a STEM-oriented middle and high school student population, what is the strength of the correlation between the observed level of creative thinking and the level to which a student plays and/or succeeds in a builder game and engages in its social media culture? Level of play is measured in terms of time and level of achievements within Minecraft. Level of engagement is measured in terms of posting and sharing behavior on Minecraft forums, YouTube and other social media platforms. The level of creative thinking is measured by Urban's Test for Creative Thinking – Drawing Production test. Correlations with TCT-DP and time spent, achievements in the games, and social media engagement levels were found to be statistically insignificant across all factors. However, a closer inspection of the individual distributions found evidence that supports an alternative perspective on the role of Minecraft in the play engagement of children.

Social media is a virtual community where users share news, ideas, interests, and information. Learning the information diffusion dynamics and making decisions correspondingly, e.g., selecting the seed nodes to maximize the influence, have been widely applied to the areas of viral marketing and cyber security. In this dissertation, we study the problem of learning diffusion process, i.e., infection prediction, in social media networks utilizing both feature-based machine learning methods and mathematical model-based methods. For feature-based machine learning methods, the neighborhood information is treated as an important feature together with user profile and content similarity features. For model-based methods, two distinctive mathematical models, i.e., Linear Threshold Learning Model and Random Walk Learning Model, are proposed to learn the information diffusion dynamics. Neural networks are implemented to train the proposed models for all aforementioned methods. In this dissertation, we also study the problem of choosing seed nodes to maximize the influence in social media networks. In one project, the problem is addressed through solving the tiered influence and activation thresholds target set selection problem, which is to find the seed nodes that can influence the most users within a limited time frame. Both the minimum influential seeds and maximum influence within budget problems are considered in this study. In addition, we study the impacts arising from the uncertainties in network structures, user behavior and activation prices via two-stage stochastic optimization as well.

Social networking sites (SNS) have revolutionized the communication between consumers and brands, publishers, and marketers. These platforms have become a way for advertisers to communicate directly and engage users with content that is innovative and less intrusive. The aim of this research is to examine the personality traits and motives (based on the uses and gratifications theory) that predict attitudes toward and engagement with non-sponsored and sponsored content in Snapchat. An online survey with 606 participants showed that the main motives of using Snapchat were social information seeking, entertainment, and impression management. Multiple regression analyses were used to determine what personality traits predict the motives for using Snapchat. Finally, hierarchical multiple regressions were used to examine the models that predict attitudes toward the non-sponsored and sponsored features in Snapchat as well as the engagement with them. Recommendations for practitioners were given to help them develop marketing strategies in Snapchat.

As public administration has evolved with the technological advances in today's society, it can be challenging to ensure the demands of the public are being met in terms of efficiency, effectiveness, and engagement. Nonetheless, a focus on community remains at the forefront of public administration. When looking at technology and the community, the tool known as social media emerges. Social media has allowed people to interact in new ways and therefore, has allowed the government to interact with citizens in ways they have not been able to in the past. In addition to attempting to modernize public administration, there has been an increased focus on building citizen trust through providing a more open government structure. The Open Government Directive issued by President Barack Obama focused on three tenets, which included transparency, participation, and collaboration. One of the ways government entities within the United States are strengthening these areas is through the implementation of various social media sites as a means to stay connected with citizens. With an increase of users utilizing social media tools for both information and connection, many government departments and agencies have incorporated social media use into their workplace as a function for their department. However, it is elected officials that are the ones who represent the citizens from their governmental role and thus, can aid in bridging the gap between citizens and government. Yet, there is little research on how elected officials, specifically in municipalities, are utilizing social media to connect with their constituents. This study discusses social media use by municipal elected officials and how it relates to open government community engagement. Open government community engagement is defined in terms of the three tenets of the Open Government Directive: transparency, participation, and collaboration encompassing the rungs of Arnstein's ladder of citizen participation. For this qualitative study, fifty-seven Florida municipal elected officials were interviewed regarding their social media use or lack thereof in terms of engagement with citizens. The interviews are followed-up with content analysis of social media sites. An ethnographic approach is utilized to uncover and develop common themes related to open government community engagement. The findings suggest while some municipal elected officials are utilizing social media well in terms of open government community engagement, there is a lack of clear understanding of social media use within the context of the Sunshine Law, as well as other barriers prohibiting utilizing social media for more of the participation and collaboration components. There are several reasons municipal elected officials opt to avoid social media altogether, while additional concepts related to open government limited engagement and closed government community engagement are explored. The concept of avoidance was addressed, especially as it pertained to the practical implications for both city administrators and elected officials.

Location-based real-time dating (LBRTD) apps have become an increasingly common way for people to broaden their social network and meet others for the purposes of dating, friendship, and more. This investigation focused on Tinder, presently the most widely-used LBRTD app. Semi-structured interviews were conducted with twenty-three current and recent Tinder users to gain insight into their self-presentation strategies and impressions of others on the app. The questions concentrated around four major topic areas: use of photos, use of bio text, perception of others, and real or imagined deception. A grounded theory approach was used to frame the data. From this, four major themes were derived that characterized Tinder as a unique social space. First, Tinder users maintained an idealistic yet authentic portrayal of the self. Secondly, self-presentation was governed by gender norms, both societal and unique to the app. Thirdly, while these strategies were deliberately planned, they were often structured to appear nonchalant. Finally, concerns about "catfishing" and the authenticity of others shaped both how users presented themselves and the others they chose to interact with on the app. The study concluded by suggesting multiple prospective research directions into this intriguing and under-researched field.

This dissertation questions: How is the social imaginary about the meaning of being white in Mexico produced, reproduced, and problematized in Twitter Discourse? How is the social imaginary about the meaning of being white in Argentina produced, reproduced, and problematized in Twitter Discourse? How are the social imaginaries in Twitter Discourse in Mexico and Argentina related to the cultural and symbolic power exercised by the United States, and does US power influence the structure of privileges built around Whiteness? For doing that, I collected up to 10K tweets using two keywords to identify discourses surrounding Whiteness in tweets from users in Mexico and Argentina and analyzed up to 300 tweets per keyword using Critical Discourse Analysis tools. The findings demonstrate that research on Twitter is valid to explore communities from inside and interpret problems that go beyond digital environments. Furthermore, Twitter provides a unique opportunity to review Whiteness and question its privilege structures. In addition, the tweets operate as a cultural manifestation of the latent social unrest gruesomely exposing racism, dehumanization, eliminationism, and contempt for otherness favored by the affordances of the medium. My approach focused on Argentina and Mexico tweets as selected cases able to reflect the reality of the region in order to explore the function of Whiteness in everyday conversations, considering the impact of digital technologies in society. Both countries represent well-differentiated social structures, and embody particular ways of living ethnicity, cultural capitalism, and globalization. Although to be considered 'white' in Argentina is not the same as in Mexico, they also retain certain identity features related to conceptions of Whiteness that allow its study. Even more interesting, I found that studying Whiteness in these two countries also illustrated the influence of the United States as a cultural and symbolic power in the development of white supremacist ideas.

Over the last decade, the advent of social media such as online product reviews (e.g., Amazon.com),blogs and other social networking sites (e.g., Facebook.com) has dramatically changed the way consumers obtain and exchange information about products. This dissertation investigates the impact of various types of social media on product performance and compares the effectiveness of social and traditional media under various conditions. Specifically, the first chapter performs a meta-analysis of consumer-generated WOM elasticity in social media to identify the factors that influence the impact of WOM on product sales and to assess the generalizability of the relationship. The second chapter examines how social media may influence product performance in different product contexts as compared with traditional media, which assists managers in making better media decisions. Taken together, this dissertation evaluates the progress in this field, and then takes a step further by applying past findings to understand how social media may perform at various stages in the product lifecycle.