As of 2021, nearly 538 million adults and children live with diabetes mellitus worldwide, with projections for 2045 estimated at 645 million adults globally. In the United States, there are 34 million adults with diabetes and an additional 88 million with prediabetes. The economic costs are staggering, primarily due to management of acute or chronic complications. As a chronic condition requiring daily self-care, the psychosocial burden of diabetes is significant. Therefore, diabetes self-management education is recommended as a standard of care for all people with diabetes. The Association of Diabetes Care and Education Specialists (ADCES) has created a framework of education incorporating seven self-care behaviors, each with several knowledge, skill, and barrier resolution outcome measures. However, research has suggested that diabetes self-management and support services are not utilized sufficiently. YouTube™ with a reach of over 2 billion users is a potential medium to reach more people with diabetes. At the present time, there is a paucity of research describing the source and content of the most widely viewed videos on diabetes selfcare. This study aimed to help fill that gap. Specific aims of the study included: (a) describe characteristics of widely viewed YouTube™ videos on Diabetes Self-Care concerning length, date posted, source, speaker(s), format, and number of views; (b) describe the content of the most widely viewed YouTube™ videos on diabetes self-management education and support, categorized by the ADCES7 Self-Care™ behaviors; and (c) examine the source of videos in relation to number of views. The researcher used a YouTube™ Application Programming Interface to retrieve video URLs along with meta data such as source, duration, date posted, and view counts. Data were sorted by URL and view count, duplicates removed, and screened for inclusion and exclusion criteria. The top 100 videos by view count were used as the sample in the study. A codebook developed for this study categorized the upload source, speaker, format, and seven content categories. Descriptive analyses were conducted to understand the most viewed sources and the content categories likely and not likely to be mentioned. Collectively, these 100 videos were viewed 146,405,133 times, with an average duration of 12.2 minutes. Most of the videos (N = 77) were uploaded between 2017 and 2021. Results indicated that the two most popular sources for videos were Professionals and Corporations together uploading 72 videos and garnering 77% of cumulative views. In contrast, government agencies uploaded 1 video (<1% of cumulative views). Professionals was the most common protagonist (N = 42) when a speaker could be identified. Talk by professional received 34.09% of cumulative views, almost as much as Animation with voice (35.95%). The content areas most mentioned were Background on Diabetes, focusing on factors affecting blood sugar and ADCES7 Self-Care BehaviorsTM, especially Healthy Eating. Reversal of Diabetes was broached in 18 videos with 23.13% of cumulative views. Prevention Strategies for Communities was not mentioned at all, and Prevention Strategies for Individuals garnered less than 4% of cumulative views. YouTube™ is a popular source of online information for people with diabetes. As such, it presents an excellent avenue to raise awareness of prediabetes and dissemination of diabetes self-management education. Significant opportunity exists for government and advocacy agencies to increase their presence on YouTube™ in terms of viewership, while presenting meaningful and credible information. Recommendations for population and public health initiatives as well as future research and practice were presented to utilize the power of YouTube™ as a medium to expand the reach of diabetes self-management education and support.

With the increasing number of breast cancer survivors, there is a need to enhance health education to help survivors make informed decisions about maximizing their quality of life. YouTube is one of the most popular video applications that can be used for public health education. Nonetheless, there is little research on the content of health-related information that is uploaded to YouTube relevant to breast cancer survivors. This study was intended to help fill that gap in knowledge by describing the sources, formats, and content conveyed in the most widely viewed YouTube videos on breast cancer. YouTube was searched with a cleared browsing history using the key search term “breast cancer.” The resulting videos were sorted by view count. Videos were then screened for inclusion and exclusion criteria, yielding a sample of 100 videos with the most views. Video title, link, number of views, and date of upload were coded along with content included in each video. The inter- and intra-rater reliability was acceptable (Kappa’s = .79 and .97, respectively). The sample of 100 videos was collectively viewed 135,311,626 times, suggesting that the subject of breast cancer is a popular topic on YouTube. Nearly half of the sample videos (n = 45) were uploaded by television news/media agencies. Combined/multiple formats were the most popular format (n = 61), followed by still images/text (n = 48). General information on cancer was found to be the most common (n = 71), followed by screening for breast cancer occurrence/ recurrence (n = 62), and cancer treatments/breast cancer treatments (n = 45). Several of the content categories were rarely covered in the most-watched videos—for example, cancer rehabilitation recommendations, returning to work after cancer treatment, and financial burden/management of cancer. Thus, while topics such as breast cancer screening are widely covered, topics for breast cancer survivors regarding maximizing their quality of life are less widely covered. Few videos (n = 3) contained misinformation, but these videos were viewed millions of times, emphasizing the need for ongoing monitoring to identify and remove misinformation. The findings of this study indicated that YouTube videos on breast cancer gained over 135 million views. YouTube can be a great media channel for public health education. Nonetheless, there is significant need for more high-quality YouTube videos to be created to help breast cancer survivors navigate their cancer journey.

The emergence of new Internet applications and technologies have resulted in an increased complexity as well as a need for lower latency, higher bandwidth, and increased reliability. This ultimately results in an increased complexity of network operation and management. Manual management is not sufficient to meet these new requirements. There is a need for data driven techniques to advance from manual management to autonomous management of network systems. One such technique, Machine Learning (ML), can use data to create models from hidden patterns in the data and make autonomous modifications. This approach has shown significant improvements in other domains (e.g., image recognition and natural language processing). The use of ML, along with advances in programmable control of Software- Defined Networks (SDNs), will alleviate manual network intervention and ultimately aid in autonomous network operations. However, realizing a data driven system that can not only understand what is happening in the network but also operate autonomously requires advances in the networking domain, as well as in ML algorithms. In this thesis, we focus on developing ML-based network architectures and data driven net- working algorithms whose objective is to improve the performance and management of future networks and network applications. We focus on problems spanning across the network protocol stack from the application layer to the physical layer. We design algorithms and architectures that are motivated by measurements and observations in real world or experimental testbeds. In Part I we focus on the challenge of monitoring and estimating user video quality of experience (QoE) of encrypted video traffic for network operators. We develop a system for REal-time QUality of experience metric detection for Encrypted Traffic, Requet. Requet uses a detection algorithm to identify video and audio chunks from the IP headers of encrypted traffic. Features extracted from the chunk statistics are used as input to a random forest ML model to predict QoE metrics. We evaluate Requet on a YouTube dataset we collected, consisting of diverse video assets delivered over various WiFi and LTE network conditions. We then extend Requet, and present a study on YouTube TV live streaming traffic behavior over WiFi and cellular networks covering a 9-month period. We observed pipelined chunk requests, a reduced buffer capacity, and a more stable chunk duration across various video resolutions compared to prior studies of on-demand streaming services. We develop a YouTube TV analysis tool using chunks statistics detected from the extracted data as input to a ML model to infer user QoE metrics. In Part II we consider allocating end-to-end resources in cellular networks. Future cellular networks will utilize SDN and Network Function Virtualization (NFV) to offer increased flexibility for network infrastructure operators to utilize network resources. Combining these technologies with real-time network load prediction will enable efficient use of network resources. Specifically, we leverage a type of recurrent neural network, Long Short-Term Memory (LSTM) neural networks, for (i) service specific traffic load prediction for network slicing, and (ii) Baseband Unit (BBU) pool traffic load prediction in a 5G cloud Radio Access Network (RAN). We show that leveraging a system with better accuracy to predict service requirements results in a reduction of operation costs. We focus on addressing the optical physical layer in Part III. Greater network flexibility through SDN and the growth of high bandwidth services are motivating faster service provisioning and capacity management in the optical layer. These functionalities require increased capacity along with rapid reconfiguration of network resources. Recent advances in optical hardware can enable a dramatic reduction in wavelength provisioning times in optical circuit switched networks. To support such operations, it is imperative to reconfigure the network without causing a drop in service quality to existing users. Therefore, we present a ML system that uses feedforward neural networks to predict the dynamic response of an optically circuit-switched 90-channel multi-hop Reconfigurable Optical Add-Drop Multiplexer (ROADM) network. We show that the trained deep neural network can recommend wavelength assignments for wavelength switching with minimal power excursions. We extend the performance of the ML system by implementing and testing a Hybrid Machine Learning (HML) model, which combines an analytical model with a neural network machine learning model to achieve higher prediction accuracy. In Part IV, we use a data-driven approach to address the challenge of wireless content delivery in crowded areas. We present the Adaptive Multicast Services (AMuSe) system, whose objective is to enable scalable and adaptive WiFi multicast. Specifically, we develop an algorithm for dynamic selection of a subset of the multicast receivers as feedback nodes. Further, we describe the Multicast Dynamic Rate Adaptation (MuDRA) algorithm that utilizes AMuSe’s feedback to optimally tune the physical layer multicast rate. Our experimental evaluation of MuDRA on the ORBIT testbed shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of nodes while meeting quality requirements. We leverage the lessons learned from AMuSe for WiFi and use order statistics to address the performance issues with LTE evolved Multimedia Broadcast/Multicast Service (eMBMS). We present the Dynamic Monitoring (DyMo) system which provides low-overhead and real-time feedback about eMBMS performance to be used for network optimization. We focus on the Quality of Service (QoS) Evaluation module and develop a Two-step estimation algorithm which can efficiently identify the SNR Threshold as a one time estimation. DyMo significantly outperforms alternative schemes based on the Order-Statistics estimation method which relies on random or periodic sampling.

This dissertation studies economic issues in the digital economy with a specific focus on the economic aspects of how firms acquire and use consumer data. Chapter 1 empirically studies the drivers of digital attention in the space of social media applications. In order to do so I conduct an experiment where I comprehensively monitor how participants spend their time on digital services and use parental control software to shut off access to either their Instagram or YouTube. I characterize how participants substitute their time during and after the restrictions. I provide an interpretation of the substitution during the restriction period that allows me to conclude that relevant market definitions may be broader than those currently considered by regulatory authorities, but that the substantial diversion towards non-digital activities indicates significant market power from the perspective of consumers for Instagram and YouTube. I then use the results on substitution after the restriction period to motivate a discrete choice model of time usage with inertia and, using the estimates from this model, conduct merger assessments between social media applications. I find that the inertia channel is important for justifying blocking mergers, which I use to argue that currently debated policies aimed at curbing digital addiction are important not only just in their own right but also from an antitrust perspective and, in particular, as a potential policy tool for promoting competition in these markets. More broadly, my paper highlights the utility of product unavailability experiments for demand and merger analysis of digital goods. I thank Maayan Malter for working together with me on collecting the data for this paper. Chapter 2 then studies the next step in consumer data collection process – the extent to which a firm can collect a consumer’s data depends on privacy preferences and the set of available privacy tools. This chapter studies the impact of the General Data Protection Regulation on the ability of a data-intensive intermediary to collect and use consumer data. We find that the opt-in requirement of GDPR resulted in 12.5% drop in the intermediary-observed consumers, but the remaining consumers are trackable for a longer period of time. These findings are consistent with privacy-conscious consumers substituting away from less efficient privacy protection (e.g, cookie deletion) to explicit opt out—a process that would make opt-in consumers more predictable. Consistent with this hypothesis, the average value of the remaining consumers to advertisers has increased, offsetting some of the losses from consumer opt-outs. This chapter is jointly authored with Yeon-Koo Che and Tobias Salz. Chapter 3 and Chapter 4 make up the third portion of the dissertation that studies one of the most prominent uses of consumer data in the digital economy – recommendation systems. This chapter is a combination of several papers studying the economic impact of these systems. The first paper is a joint paper with Duarte Gonçalves which studies a model of strategic interaction between producers and a monopolist platform that employs a recommendation system. We characterize the consumer welfare implications of the platform’s entry into the production market. The platform’s entry induces the platform to bias recommendations to steer consumers towards its own goods, which leads to equilibrium investment adjustments by the producers and lower consumer welfare. Further, we find that a policy separating recommendation and production is not always welfare improving. Our results highlight the ability of integrated recommender systems to foreclose competition on online platforms. The second paper turns towards understanding how such systems impact consumer choices and is joint with Duarte Gonçalves and Shan Sikdar. In this paper we study a model of user decision-making in the context of recommender systems via numerical simulation. Our model provides an explanation for the findings of Nguyen et. al (2014), where, in environments where recommender systems are typically deployed, users consume increasingly similar items over time even without recommendation. We find that recommendation alleviates these natural filter-bubble effects, but that it also leads to an increase in homogeneity across users, resulting in a trade-off between homogenizing across-user consumption and diversifying within-user consumption. Finally, we discuss how our model highlights the importance of collecting data on user beliefs and their evolution over time both to design better recommendations and to further understand their impact.

This dissertation unites two contrasting phenomena, musical theorizing as practiced on YouTube and dreamlike experiences involving music, under a single rubric: virtual space. While the two phenomena are disconnected in time, geography, and culture, they are nonetheless similar in that they are spatialized in ways that contravene how we typically experience physical space, So, I develop the concept of virtual space as a means of approaching the commonalities underlying these phenomena. Building on a definition of space as a medium in which entities are positionally related, I propose a framework for analyzing virtual spaces that emphasizes a phenomenon’s subjective immersivity and objective relationality. In order to bring out the human dimension of these virtual spaces, I concentrate on the discursive, instrumental, experiential, and generative aspects of embodied virtual spaces that are entangled in social, cultural, and political networks. To that end, in the first half of the dissertation, I discuss how a community of YouTube content creators has carved out a place for practicing, teaching, and learning music theory. I detail YouTube’s affordances as a space for theorizing music and a medium of communication, showing how content creators have leveraged these to great effect in their theorizing of game music. Flitting between the general and the particular, I balance case studies of content creators and close readings of audiovisual content with sociological approaches. In spite of the platform’s self-image and the community’s political positioning, I contend that YouTube’s egalitarian promise has been left unfulfilled in the English-language, Western-centric field of YouTube music theory, which replicates or even exacerbates some of the epistemological issues and unjust social structures that pervade academia and Western society more broadly. The other half of the dissertation concerns the analytical interpretation and precise differentiation of dreamlike experiences centered on music. I demonstrate that much of the discourse on this topic comes from close readings of music as dream. As this perspective locates dreaming in an object, I argue for counterbalancing this discourse towards a dreaming subject, and thus I propose a framework with three interrelated components. First, I carefully distinguish dreaming, as a virtual and spatialized experience, from standard waking consciousness through recourse to neuroscience and phenomenology. After that, I set forth a tripartite scheme that articulates the many permutations of how we might position ourselves, other subjects, and music in this non-dreamer–dreamer dynamic. Last, I classify the various interactions between music, dreamlike experience, and analytical interpretation. Using the music of Fryderyk Chopin as my example, I show that, though this music has accrued much historical and cultural meaning through being read as dreamlike, we have much to gain from the analytical insights unique to our subjective, dreamlike experiences with this music.

Title from first page of PDF document. Includes bibliographical references (p. 47-53).