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

This study seeks to examine the connection between social media usage and the Enneagram personality model. This connection will aid in a better understanding of what motivates individuals to use social media. The information found in this study will be applied to understanding behavioral addiction. This understanding will allow more personalized treatment for individuals already subject to these behaviors and preventive treatment for those more susceptible to behavioral addiction to social media. In order to find the connection between social media use and the Enneagram personality model, a survey including an Enneagram personality inventory, a social media use inventory, and a demographic questionnaire was provided to college students at the University of Central Florida through an online research system. This provides information about an individual's Enneagram type, social media use habits, and other factors that could influence these variables.

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

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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While research has been conducted on social media, few comparisons have been made in regards to the privacy issues that exist within the most common social media networks, such as Facebook, Google Plus, and Twitter. Most research has concentrated on technical issues with the networks and on the effects of social media in fields such as medicine, law, and science. Although the effects on these fields are beneficial to the people related to them, few studies have shown how everyday users are affected by the use of social media. Social media networks affect the privacy of users because the networks control what happens to user contact information, posts, and other delicate disclosures that users make on those networks. Social media networks also have the ability to sync with phone and tablet applications. Because the use of these applications requires additional contact information from users, social media networks are entrusted with keeping user information secure. This paper analyzes newspaper articles, magazine articles, and research papers pertaining to social media to determine what effects social media has on the user's privacy and how much trust should be placed in social media networks such as Facebook. It provides a comprehensive view of the most used social media networks in 2012 and offers methods and suggestions for users to help protect themselves against privacy invasion.

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