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This study evaluates the impacts of brand storytelling on customers of a craft brewery and distillery in East Tennessee. Brand storytelling is well researched, but has not been applied to niche sectors and customer response. The study consisted of a 15-question survey distributed to customers of Tennessee Hills Distillery and Brewstillery (TN Hills), and received 139 responses. The results were that despite a seeming trend of lack of engagement with the business, respondents value brand stories and can identify unique brand characteristics. Respondents’ answers reflected a personal connection with TN Hills, and connected the brand to grander ideals like the American Dream, community acceptance, and legacy. Thus, customers’ feelings toward a business are influenced by brand storytelling.

Research has shown that the use of technology in curriculum, and art classrooms in particular, can benefit students. This thesis outlines these benefits which include the potential for technology to make learning more personal, assist students in their future careers, and allow opportunity for collaboration. Still, several obstacles impede the full-fledged realization of that potential, often leading teachers to avoid or ignore technology in their pedagogical strategies. This thesis addresses these obstacles and provides practical and theoretical solutions. Once these obstacles are overcome, teachers will be better able to incorporate new technology in their lessons, such as social media, podcasts, open-source websites, and online programs. As an example of art teaching that uses technology, this thesis also provides a sample lesson plan for secondary students, incorporating elements of interactive Web technologies that have been recommended by art education scholars.

There has been a dramatic increase in the digitization of content consumers create and accumulate on their devices or social media platforms. People either create and store content on digital devices or cloud storage services or post content on social media platforms that provide network affordances. Given the sheer volume of user-generated digital content on devices and social media platforms, it is imperative to examine how consumers value it. Across eleven studies, I show that user-generated content on social media is valued less than similar user-generated content stored on digital devices or in the cloud. I propose a goal-theoretic framework to explain this difference in value. Consumers value user-generated content less as its creation and use are motivated more by a sharing goal and less by a memory preservation goal. However, consumers do value their user-generated content on social media when it is perceived to preserve meaningful memories. Lastly, I empirically demonstrate how enhancing the value of social media user-generated content has implications for platform loyalty, revenue model, and digital legacy regulation on these platforms. / 2024-05-16T00:00:00Z

Der Einsatz von Social Media-Plattformen wird immer wichtiger für Gedenkstätten. Das Ziel dieser Masterarbeit ist es, einen Überblick zu den Einsatzmöglichkeiten der Kurzvideoplattform TikTok für Bildung und Vermittlung in Gedenkstätten zu geben. Bisherige Annahmen, Vorbehalte oder erwartete Potenziale sollen mit Zahlen und Daten unterlegt werden. Die Forschungsfragen der Arbeit sind deshalb, ob es für Gedenkstätten möglich ist, junge Schüler:innen, über Kurzvideos zu erreichen und wie es ihnen gelingt die Geschichtsvermittlung in die Zielgruppe zu tragen. Hierbei wird vor allem die Pionierarbeit der KZ-Gedenkstätten Neuengamme, Mauthausen, Bergen-Belsen, Dachau und Flossenbürg; der Erinnerungsstätte Villa ten Hompel und des Dokumentationszentrums NS-Zwangsarbeit Berlin genauer untersucht. Die Arbeit steht inhaltlich auf zwei Säulen. Die erste Säule bildet eine Ist-Stand Analyse und stellt einen repräsentativen Stand der Gedenkstättenszene in den Jahren 2022 bis 2023 auf TikTok dar. Dafür wurden die TikTok-Profile von insgesamt neun Gedenkstätten, Erinnerungsorten und Dokumentationszentren beobachtet und ausgewertet. Zudem werden sieben beantwortete Fragebögen von Gedenkstätten, die TikTok bereits einsetzten, analysiert und ausgewertet. Die zweite Säule umfasst die Antworten aus einer einjährigen Umfrage mit insgesamt 850 Teilnehmer:innen in der KZ-Gedenkstätte Mittelbau-Dora. Durch diese Befragung sind auch Rückschlüsse auf das Nutzer:innenverhalten von jungen Menschen, die TikTok als soziales Netzwerk nutzen, möglich.:Einleitung 1. Die Kurzvideo-Plattform TikTok 1.1 Geschichtsvermittlung mit TikTok 1.2 #GedenkenBildet 1.3 Storytelling auf TikTok 2. Gedenkstätten auf TikTok 2.1 Initiative „TikTok - Shoah Education and Commemoration“ 2.2 Holocaust Education auf TikTok 2.3 Welche Gedenkstätten nutzen TikTok 2.3.1 @neuengamme.memorial 2.3.2 @belsenmemorial 2.3.3 @mauthausenmemorial 2.3.4 @dauchaumemorial 2.3.5 @keeping_memories 2.3.6 @wannseekonferenz 2.3.7 @villatenhompel 2.3.8 @naziforcedlabourdc 2.3.9 @kazernedossin 2.3.10 Exkurs: @keine.erinnerungskultur 2.4 Wie nutzen Gedenkstätten TikTok 2.4.1 Strategie, Social Media und Content 2.4.2 Genese und Vernetzung 2.4.3 Standards, Budget und Marketing 2.4.4 Zielgruppen 2.4.5 Zukunft 2.4.6 Exkurs: Instagramprojekt @ichbinsophiescholl 3. Potentiale auf TikTok für die Geschichtsvermittlung von Gedenkstätten 3.1 Umfrage zum Nutzungsverhalten von Social Media 3.1.1 Antworten zum Nutzer:innenverhalten von Instagram 3.1.2 Antworten zum Nutzer:innenverhalten von TikTok 3.2 Chancen, Risiken und perspektivische Zusammenhänge für Gedenkstätten 3.2.1 Zielgruppen 3.2.2 Mechanismen der Plattform 3.2.3 Bildung und Vermittlung Schlussbetrachtung Literaturverzeichnis Abbildungsverzeichnis Glossar Danksagung Selbstständigkeitserklärung Anhang Dokumentation Fragebogen Fragebogen KZ-Gedenkstätte Neuengamme Fragebogen Gedenkstätte Bergen-Belsen Fragebogen Geschichtsort Villa ten Hompel Fragebogen KZ-Gedenkstätte Flossenbürg Fragebogen KZ-Gedenkstätte Dachau Fragebogen KZ-Gedenkstätte Mauthausen Fragebogen Dokumentationszentrum NS-Zwangsarbeit

This dissertation focuses on identifying various types of anomalous information pattern in social media and news outlets. We focus on three types of anomalous information, including (1) media censorship in news outlets, which is information that should be published but is actually missing, (2) fake news in social media, which is unreliable information shown to the public, and (3) media propaganda in news outlets, which is trustworthy information but being over-populated. For the first problem, existing approaches on censorship detection mostly rely on monitoring posts in social media. However, media censorship in news outlets has not received nearly as much attention, mostly because it is difficult to systematically detect. The contributions of our work include: (1) a hypothesis testing framework to identify and evaluate censored clusters of keywords, (2) a near-linear-time algorithm to identify the highest scoring clusters as indicators of censorship, and (3) extensive experiments on six Latin American countries for performance evaluation. For the second problem, existing approaches studying fake news in social media primarily focus on topic-level modeling or prediction based on a set of aggregated features from a col- lection of posts. However, the credibility of various information components within the same topic can be quite different. The contributions of our work in this space include: (1) a new benchmark dataset for fake news research, (2) a cluster-based approach to improve instance- level prediction of information credibility, and (3) extensive experiments for performance evaluations. For the last problem, existing approaches to media propaganda detection primarily focus on investigating the pattern of information shared over social media or evaluation from domain experts. However, these approaches cannot be generalized to a large-scale analysis of media propaganda in news outlets. The contributions of our work include: (1) non- parametric scan statistics to identify clusters of over-populated keywords, (2) a near-linear-time algorithm to identify the highest scoring clusters as indicators of propaganda, and (3) extensive experiments on two Latin American countries for performance evaluation. / Doctor of Philosophy / Nowadays, massive information is available through a variety of social media platforms. However, the information accessed by the audience might be not exactly correct in different ways. In order for the audience being able to get access to the correct information, we develop various machine learning algorithms to uncover the anomalous information pattern in social media and explain the reason behind this behavior. Our algorithms can be used to learn what different information patterns can exist in the open data source.

In recent years the usage of public transit services has been rapidly increased, thanks to huge progress on network technologies. However, the disruptions in modern public transit services also increased, due to aging infrastructure, non-comprehensive system design and the needs for maintenance. Any disruptions happened in current transit networks can cause to major disasters on passengers who use these networks for their daily commutes. Although we have lots of usage on transit network, still most current disruptions detection systems either lack of network coverage or did not have real-time system. The goal of this thesis was to create a system that can leverage Twitter data to help in detecting service disruptions in their early stage. This work involves a web applications which contains front-end, back-end and database, along with data mining techniques that obtain Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. The fundamental features of the system includes real-time incidents panel, historical events review, activities search near specific metro station and recent news review, which allowing people to have more relatively information based on their needs. After the initial functionalities is being settled, we further developed storytelling and sentiment analysis applications, which allowed people have more comprehensive information about the incidents that are happened around metro stations. Also, with the emergency report we developed, the developer can have immediate notification when an urgent event occurred. After fully testified the system's case study on storytelling, sentiment analysis and emergency report, the outcomes are extreme convincing and trustworthy. / Master of Science / As public transit network become more and more accessible, people around the world rely on these network for their daily commutes. It is clearly that service disruptions among these system will affect passengers severely, especially when there are more and more people using it. This thesis is dedicated to build a web application that will not only allowing people to search latest information, but also assisting on the early detection of the disruptions. In this work we have developed an web application which has easy to use user interface, along with data mining techniques that connected with live data from Twitter to identify these disruptions. Our website is a real-time platform that contains real-time incidents panel, historical events review, activities search near specific metro station and recent news review based on latest tweets and news. By collecting live data from Twitter and various news website, we further developed storytelling and sentiment analysis features. For storytelling, we applied a machine learning model to help us clustering the related tweets/news, after summarize and track the evolution of tweets/news, we converted into stories and displayed it with interactive timelines. For sentiment analysis, we integrated a machine learning model which will scaled the emotional strength of tweets/news, then show the feelings of particular tweets/news. Additionally, we create an emergency report functionality, since it is important for the authority to where and when the incidents happened as soon as possible. The outcome of the system has been well-testify based on the daily case studies, and the results not only meet the ground truth, but also provide with various information.

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This article-format dissertation focuses on how scholars use social media to support their scholarship. The first article is a scoping literature review that outlines current research. While overviewing an emergent field of literature, the article highlights motivations for using social media use, discusses benefits and drawbacks of this use for scholarship, explains how this discourse has evolved over time, and shares implications of what further topics can be investigated as well as implications for post-secondary institutions and scholars integrating social media use into their professional roles. In article two, I built a classifier and used it to analyze the Twitter data of those who self-identified as post-secondary scholars. The goal of the classifier was to analyze patterns of actual use rather than self-reported use. It was discovered that use is primarily personal, although many scholars use these to represent an aspect of their professional identity. The classifier was then tested on a sub-group of learning technology scholars to identify variance that may exist in the ways in which scholars of a singular field may be using Twitter. The third article was a phenomenological study that investigated the lived experiences of faculty using social media to support their scholarship. Through the study, five themes emerged as scholars discussed their use of Twitter and other social media and those were (a) positive relational impacts and community building, (b) using social media as a tool for open scholarship, (c) identity presentation, (d) the ethics of social media platforms, and (e) drivers for change over time. The study also found that although scholars may appear to be using social media for varied purposes, their Twitter profiles are often seen as primarily or completely a professional platform with which they engage with their communities and expand their definition of scholarship. The conclusion of the dissertation highlights findings across the three articles and provides directions for future research.

The purpose of this quantitative study is to examine the impact of social media habits on student disciplinary outcomes. The study explored the role of social media, its impact on adolescent mental health, and digital citizenship. Pre- and post-pandemic discipline and survey data regarding principals' perceptions of student behavior were analyzed for trends. Discipline data revealed that the highest number of discipline incidents occurred in the post-pandemic school year of 2021. In addition, analyses of the principal survey data indicated that principals are experiencing an increase in discipline incidents and student needs for mental health services because of social media. The findings from the study can help school districts consider the relationship between social media, digital citizenship, and leadership.