VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

Flooded with Information from Social Media: Effects of Disaster Information Source and Visuals on Viewers' Cognitive, Affective, and Behavioral Responses

While a variety of crisis types loom as real risks for organizations and communities, and the media landscape continues to evolve, research is needed to help explain and predict how people respond to various kinds of crisis and disaster information. For example, despite the rising prevalence of digital and mobile media centered on still and moving visuals, and stark increases in Americans’ use of visual-based platforms for seeking and sharing disaster information, relatively little is known about how the presence or absence of disaster visuals online might prompt or deter resilience-related feelings, thoughts, and/or behaviors. Yet, with such insights, governmental and other organizational entities as well as communities themselves may best help individuals and communities prepare for, cope with, and recover from adverse events. Thus, this work uses the theoretical lens of the social-mediated crisis communication model (SMCC) coupled with the limited capacity model of motivated mediated message processing (LC4MP) to explore effects of disaster information source and visuals on viewers’ resilience-related responses to an extreme flooding scenario. Results from two experiments are reported. First a preliminary 2 (disaster information source: organization/US National Weather Service vs. news media/USA Today) x 2 (disaster visuals: no visual podcast vs. moving visual video) factorial between-subjects online experiment with a convenience sample of university students probes effects of crisis source and visuals on a variety of cognitive, affective, and behavioral outcomes. A second between-subjects online experiment manipulating still and moving visual pace in online videos (no visual vs. still, slow-pace visual vs. still, medium-pace visual vs. still, fast-pace visual vs. moving, slow-pace visual vs. moving, medium-pace visual vs. moving, fast-pace visual) with a convenience sample recruited from Amazon’s Mechanical Turk (mTurk) similarly probes a variety of potentially resilience-related cognitive, affective, and behavioral outcomes. The role of biological sex as a quasi-experimental variable is also investigated in both studies. Various implications for community resilience and recommendations for risk and disaster communicators are explored. Implications for theory building and future research are also examined. Resulting modifications of the SMCC model (i.e., removing “message strategy” and adding the new category of “message content elements” under organizational considerations) are proposed.

A VOICE OF THEIR OWN: MUSIC AND SOCIAL COHESION IN TURKISH ALEVI LIFE

This dissertation examines the role that music has played in the expression of identity and revitalization of culture of the Alevis in Turkey, since the start of their sociocultural revival movement in the late 1980s. Music is central to Alevi claims of ethnic and religious difference—singing and playing the bağlama (Turkish folk lute) constitutes an expressive practice in worship and everyday life. Based on research conducted from 2012 to 2014, I investigate and present Alevi music through the lens of discourses on the construction of identity as a social and musical process. Alevi musicians perform a revived repertoire of the ritual music and folk songs of Anatolian bards and dervish-lodge poets that developed over several centuries. Contemporary media and performance contexts have blurred former distinctions between sacred and secular, yet have provided new avenues to build community in an urban setting. I compare music performances in the worship services of urban and small-town areas, and other community events such as devotional meetings, concerts, clubs, and broadcast and social media to illustrate the ways that participation—both performing and listening—reinforces identity and solidarity. I also examine the influence of these different contexts on performers’ musical choices, and the power of music to evoke a range of responses and emotional feelings in the participants. Through my investigation I argue that the Alevi music repertoire is not only a cultural practice but also a symbol of power and collective action in their struggle for human rights and self-determination. As Alevis have faced a redefined Turkish nationalism that incorporates Sunni Muslim piety, this music has gained even greater potency in their resistance to misrecognition as a folkloric, rather than a living, tradition.

Using Statistics to Set Priorities

In November 2015-March 2016, I assigned my Graduate Assistant, David Durden, a project to compile usage statistics and trends for digitized collections between 2013-2015 from UMD Digital Collections and our contributions to the Internet Archive between 2008-2015. The original intent of the project was to provide usage metrics to assist the Digitization Initiatives Committee in prioritizing projects or content areas. The project also uncovered trends that should impact how we think about making digital collections discoverable and accessible. For example, if 50-60% of traffic into UMD Digital Collections comes from outside the University or College Park, MD, how will this impact the potential usage of content when access is restricted to campus due to licensing, copyright, or ownership restrictions? With a growing population using mobile browsers, how will a flash-based viewer restrict users’ access to content? How might we develop content or its discoverability for a growing social media user base? In this talk, I will briefly discuss the usage trends for the represented collections, how we may use these in prioritizing future projects, and issues I will discuss with collection managers as we develop project plans and the Manager of Digital Programs and Initiatives as we develop the digital collections repository.

Towards Proactive Context-aware Computing and Systems

A primary goal of context-aware systems is delivering the right information at the right place and right time to users in order to enable them to make effective decisions and improve their quality of life. There are three key requirements for achieving this goal: determining what information is relevant, personalizing it based on the users’ context (location, preferences, behavioral history etc.), and delivering it to them in a timely manner without an explicit request from them. These requirements create a paradigm that we term as “Proactive Context-aware Computing”. Most of the existing context-aware systems fulfill only a subset of these requirements. Many of these systems focus only on personalization of the requested information based on users’ current context. Moreover, they are often designed for specific domains. In addition, most of the existing systems are reactive - the users request for some information and the system delivers it to them. These systems are not proactive i.e. they cannot anticipate users’ intent and behavior and act proactively without an explicit request from them. In order to overcome these limitations, we need to conduct a deeper analysis and enhance our understanding of context-aware systems that are generic, universal, proactive and applicable to a wide variety of domains. To support this dissertation, we explore several directions. Clearly the most significant sources of information about users today are smartphones. A large amount of users’ context can be acquired through them and they can be used as an effective means to deliver information to users. In addition, social media such as Facebook, Flickr and Foursquare provide a rich and powerful platform to mine users’ interests, preferences and behavioral history. We employ the ubiquity of smartphones and the wealth of information available from social media to address the challenge of building proactive context-aware systems. We have implemented and evaluated a few approaches, including some as part of the Rover framework, to achieve the paradigm of Proactive Context-aware Computing. Rover is a context-aware research platform which has been evolving for the last 6 years. Since location is one of the most important context for users, we have developed ‘Locus’, an indoor localization, tracking and navigation system for multi-story buildings. Other important dimensions of users’ context include the activities that they are engaged in. To this end, we have developed ‘SenseMe’, a system that leverages the smartphone and its multiple sensors in order to perform multidimensional context and activity recognition for users. As part of the ‘SenseMe’ project, we also conducted an exploratory study of privacy, trust, risks and other concerns of users with smart phone based personal sensing systems and applications. To determine what information would be relevant to users’ situations, we have developed ‘TellMe’ - a system that employs a new, flexible and scalable approach based on Natural Language Processing techniques to perform bootstrapped discovery and ranking of relevant information in context-aware systems. In order to personalize the relevant information, we have also developed an algorithm and system for mining a broad range of users’ preferences from their social network profiles and activities. For recommending new information to the users based on their past behavior and context history (such as visited locations, activities and time), we have developed a recommender system and approach for performing multi-dimensional collaborative recommendations using tensor factorization. For timely delivery of personalized and relevant information, it is essential to anticipate and predict users’ behavior. To this end, we have developed a unified infrastructure, within the Rover framework, and implemented several novel approaches and algorithms that employ various contextual features and state of the art machine learning techniques for building diverse behavioral models of users. Examples of generated models include classifying users’ semantic places and mobility states, predicting their availability for accepting calls on smartphones and inferring their device charging behavior. Finally, to enable proactivity in context-aware systems, we have also developed a planning framework based on HTN planning. Together, these works provide a major push in the direction of proactive context-aware computing.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.

Screening Diversity: Women & Work in Twenty-First-Century Popular Culture

Screening Diversity: Women and Work in Twenty-first Century Popular Culture explores contemporary representations of diverse professional women on screen. Audiences are offered successful women with limited concerns for feminism, anti-racism, or economic justice. I introduce the term viewsers to describe a group of movie and television viewers in the context of the online review platform Internet Movie Database (IMDb) and the social media platforms Twitter and Facebook. Screening Diversity follows their engagement in a representative sample of professional women on film and television produced between 2007 and 2015. The sample includes the television shows, Scandal, Homeland, VEEP, Parks and Recreation, and The Good Wife, as well as the movies, Zero Dark Thirty, The Proposal, The Heat, The Other Woman, I Don’t Know How She Does It, and Temptation. Viewsers appreciated female characters like Olivia (Scandal), and Maya (Zero Dark Thiry) who treated their work as a quasi-religious moral imperative. Producers and viewsers shared the belief that unlimited time commitment and personal identification were vital components of professionalism. However, powerful women, like The Proposal’s Margaret and VEEP’s Selina, were often called bitches. Some viewsers embraced bitch-positive politics in recognition of the struggles of women in power. Women’s disproportionate responsibility for reproductive labor, often compromises their ability to live up to moral standards of work. Unlike producers, viewsers celebrated and valued that labor. However, texts that included serious consideration of women as workers were frequently labelled chick flicks or soap operas. The label suggested that women’s labor issues were not important enough that they could be a topic of quality television or prestigious film, which bolstered the idea that workplace equality for women is not a problem in which the general public is implicated. Emerging discussions of racial injustice on television offered hope that these formations are beginning to shift.

Augmented Dissent: The Affordances of ICTs for Citizen Protest (A Case Study of the Ukraine Euromaidan Protests of 2013-2014).

This dissertation research project uses the Euromaidan protests in Ukraine to inform and shape a theory of augmented dissent to help explain the complex ways in which protest participants guided by the political, social, and cultural contexts engage in dissent augmented by ICTs in a reality where both the physical and the digital are used in concert. The purpose of this research is to conceptualize the use and perception of ICTs in protest activity using the communicative affordances framework. Through a mixed-method research approach involving interviews with protest participants, as well as qualitative and thematic analysis of online content from social media pages of several key Euromaidan protest communities, the research project examines the role ICTs played in the information and media landscape during the Euromaidan protest. The findings of the online content analysis were used to inform the questions for the 59 semi-structured, open-ended interviews with Euromaidan protest participants in Ukraine and abroad. The research findings provide in-depth insights about how ICTs were used and perceived by protest participants, and their role as vehicles for information and civic media content. The study employs the theoretical framework of social media affordances to interpret the data gathered during the interviews and content analysis to better understand how digital media augmented citizens’ protest activity through affording them new possibilities for dissent, and how they made meaning of said protest activity as augmented by ICTs. The findings contribute towards shaping a theory of digitally augmented dissent that conceptualizes the complex relationship between citizens and ICTs during protest activity as an affordance-driven one, where online and offline tools and activity merge into a unified dissent space and extend or augment the possibilities for action in interesting, and sometimes unexpected ways. Such a conceptual model could inform broader theories about civic participation and digital activism in the post-Soviet world and beyond, as ICTs become an inseparable part of civic life.