This dissertation focuses on understanding and supporting individual and collective sensemaking within online health communities (OHCs). This major goal was achieved in three aims. In Aim 1, this dissertation contributes a rich descriptive account of collective sensemaking in OHCs forums by describing how it occurs and develops, what triggers it, what elements constitute collective construction of meaning, and what conversational moves positively contribute to this process. Further, it describes how collective sensemaking in OHCs is impacted by the interplay between informational and socio-emotional needs of OHCs members. Moreover, it examines how design of different social computing platforms influences OHCs members’ ability to meet their informational and socio-emotional needs and engage in collective sensemaking. In Aim 2, this dissertation explores the design space of tools for supporting individual sensemaking through optimized information access. Through the design and evaluation of a prototype DisVis it examines the impact of such tools on OHCs members’ ability to understand information within discussion threads. In the final Aim 3, this dissertation proposes a novel approach for meeting the three main needs identified in Aims 1 and 2: promoting individual sensemaking, while at the same time encouraging collective sensemaking, and facilitating development of social awareness and ties among community members. The design and evaluation of the novel solution for visualizing discussion threads that synergistically addresses these three needs—dSense—provides insights for future research and design of interactive solutions for supporting individual and collective sensemaking within OHCs.

Sentiment Analysis has been researched in a variety of contexts but in this thesis, the focus is on sentiment analysis in Twitter, which poses its own unique challenges such as the use of slang, abbreviations, emoticons, hashtags, and user mentions. The 140-character restriction on the length of tweets can also lead to text that is difficult even for a human to determine its sentiment. Specifically, this study will analyze sentiment analysis of bilingual (U.S. English and Spanish language) Tweets. The hypothesis here is that Bilingual sentiment analysis is more accurate than sentiment analysis in a single language (English or Spanish) when analyzing bilingual tweets. In general, currently sentiment analysis in bilingual tweets is done against an English dictionary. For each of the test cases in this thesis’ experiment we will use the Python NLTK sentiment package. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Chen, Wenhao. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (p. 63-70). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Ontologies and Folksonomies --- p.1 / Chapter 1.2 --- Motivation --- p.3 / Chapter 1.2.1 --- Semantics in Folksonomies --- p.3 / Chapter 1.2.2 --- Ontologies with basic level concepts --- p.5 / Chapter 1.2.3 --- Context and Context Effect --- p.6 / Chapter 1.3 --- Contributions --- p.6 / Chapter 1.4 --- Structure of the Thesis --- p.8 / Chapter 2 --- Background Study --- p.10 / Chapter 2.1 --- Semantic Web --- p.10 / Chapter 2.2 --- Ontology --- p.12 / Chapter 2.3 --- Folksonomy --- p.14 / Chapter 2.4 --- Cognitive Psychology --- p.17 / Chapter 2.4.1 --- Category (Concept) --- p.17 / Chapter 2.4.2 --- Basic Level Categories (Concepts) --- p.17 / Chapter 2.4.3 --- Context and Context Effect --- p.20 / Chapter 2.5 --- F1 Evaluation Metric --- p.21 / Chapter 2.6 --- State of the Art --- p.23 / Chapter 2.6.1 --- Ontology Learning --- p.23 / Chapter 2.6.2 --- Semantics in Folksonomy --- p.26 / Chapter 3 --- Ontology Learning from Folksonomies --- p.28 / Chapter 3.1 --- Generating Ontologies with Basic Level Concepts from Folksonomies --- p.29 / Chapter 3.1.1 --- Modeling Instances and Concepts in Folksonomies --- p.29 / Chapter 3.1.2 --- The Metric of Basic Level Categories (Concepts) --- p.30 / Chapter 3.1.3 --- Basic Level Concepts Detection Algorithm --- p.31 / Chapter 3.1.4 --- Ontology Generation Algorithm --- p.34 / Chapter 3.2 --- Evaluation --- p.35 / Chapter 3.2.1 --- Data Set and Experiment Setup --- p.35 / Chapter 3.2.2 --- Quantitative Analysis --- p.36 / Chapter 3.2.3 --- Qualitative Analysis --- p.39 / Chapter 4 --- Context Effect on Ontology Learning from Folksonomies --- p.43 / Chapter 4.1 --- Context-aware Basic Level Concepts Detection --- p.44 / Chapter 4.1.1 --- Modeling Context in Folksonomies --- p.44 / Chapter 4.1.2 --- Context Effect on Category Utility --- p.45 / Chapter 4.1.3 --- Context-aware Basic Level Concepts Detection Algorithm --- p.46 / Chapter 4.2 --- Evaluation --- p.47 / Chapter 4.2.1 --- Data Set and Experiment Setup --- p.47 / Chapter 4.2.2 --- Result Analysis --- p.49 / Chapter 5 --- Potential Applications --- p.54 / Chapter 5.1 --- Categorization of Web Resources --- p.54 / Chapter 5.2 --- Applications of Ontologies --- p.55 / Chapter 6 --- Conclusion and Future Work --- p.57 / Chapter 6.1 --- Conclusion --- p.57 / Chapter 6.2 --- Future Work --- p.59 / Bibliography --- p.63

<p>Following the trend of online data sharing and publishing, researchers raise their concerns about the privacy problem. Online Social Networks (OSNs), for example, often contain sensitive information about individuals. Therefore, anonymizing network data before releasing it becomes an important issue. This dissertation studies the privacy preservation problem from the perspectives of both attackers and defenders. </p> <p><br></p> <p>To defenders, preserving the private information while keeping the utility of the published OSN is essential in data anonymization. At one extreme, the final data equals the original one, which contains all the useful information but has no privacy protection. At the other extreme, the final data is random, which has the best privacy protection but is useless to the third parties. Hence, the defenders aim to explore multiple potential methods to strike a desirable tradeoff between privacy and utility in the published data. This dissertation draws on the very fundamental problem, the definition of utility and privacy. It draws on the design of the privacy criterion, the graph abstraction model, the utility method, and the anonymization method to further address the balance between utility and privacy. </p> <p><br></p> <p>To attackers, extracting meaningful information from the collected data is essential in data de-anonymization. De-anonymization mechanisms utilize the similarities between attackers’ prior knowledge and published data to catch the targets. This dissertation focuses on the problems that the published data is periodic, anonymized, and does not cover the target persons. There are two thrusts in studying the de-anonymization attacks: the design of seed mapping method and the innovation of generating-based attack method. To conclude, this dissertation studies the online data privacy problem from both defenders’ and attackers’ point of view and introduces privacy and utility enhancement mechanisms in different novel angles.</p>

Interaction event times observed on a social network provide valuable information for social scientists to gain insight into complex social dynamics that are challenging to understand. However, it can be difficult to accurately represent the heterogeneity in the data and to model the dependence structure in the network system. This requires flexible models that can capture the complicated dynamics and complex patterns. Point process models offer an elegant framework for modeling event time data. This dissertation concentrates on developing point process models and related diagnostic tools, with a real data application involving an animal behavior network. In this dissertation, we first propose a Markov-modulated Hawkes process (MMHP) model to capture the sporadic and bursty patterns often observed in event time data. A Bayesian inference procedure is developed to evaluate the likelihood by using a variational approximation and the forward-backward algorithm. The validity of the proposed model and associated estimation algorithms is demonstrated using synthetic data and the animal behavior data. Facilitated by the power of the MMHP model, we construct network point process models that can capture a social hierarchy structure by embedding nodes in a latent space that can represent the underlying social ranks. Our model provides a ranking method for social hierarchy studies and describes the dynamics of social hierarchy formation from a novel perspective – taking advantage of the detailed information available in event time data. We show that the network point process models appropriately captures the temporal dynamics and heterogeneity in the network event time data, by providing meaningful inferred rankings and by calibrating the accuracy of predictions with relevant measures of uncertainty. In addition to developing a sensible and flexible model for network event time data, the last part of this dissertation provides essential tools for diagnosing lack of fit issues for such models. We develop a systematic set of diagnostic tools and visualizations for point process models fitted to data in the dynamic network setting. By inspecting the structure of the residual process and Pearson residual on the network, we can validate whether a model adequately captures the temporal and network dependence structures in the observed data.

<p>The cereal couture company, <em>[me] & goji, </em>is the dream of three young entrepreneurs. They were the first online company ever to provide the U.S market with customized cereal mix. This case study finds that online companies with an innovative product such as <em>[me] & goji</em> may benefit from using Social Networks as a marketing channel to communicate their Value Proposition. Supported by Roger's Adoption theory, selected theories on, Value Proposition, Social Networks, and qualitative data gathered from, interviews and surveys several findings were made. The conclusion is that despite offering a relatively non complex product, with a high relative advantage the market may have difficulties with recognizing the value of the product. This is mainly due to the fact that products sold online cannot be tried by the customer until after purchase. This may be perceived as an uncertainty factor for some customers. The main benefit with viral marketing tools such as Social Networks is that they may increase the rate of the market adopting new products.</p>

The cereal couture company, [me] &amp; goji, is the dream of three young entrepreneurs. They were the first online company ever to provide the U.S market with customized cereal mix. This case study finds that online companies with an innovative product such as [me] &amp; goji may benefit from using Social Networks as a marketing channel to communicate their Value Proposition. Supported by Roger's Adoption theory, selected theories on, Value Proposition, Social Networks, and qualitative data gathered from, interviews and surveys several findings were made. The conclusion is that despite offering a relatively non complex product, with a high relative advantage the market may have difficulties with recognizing the value of the product. This is mainly due to the fact that products sold online cannot be tried by the customer until after purchase. This may be perceived as an uncertainty factor for some customers. The main benefit with viral marketing tools such as Social Networks is that they may increase the rate of the market adopting new products.

University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

University of Macau / Faculty of Social Sciences and Humanities / Department of Psychology

<p>Online Social Network (OSN) services such as Facebook and Google+ are fun and useful. Hundreds of millions of users rely on these services and third-party applications to process and share personal data such as friends lists, photos, and geographic location histories. The primary drawback of today's popular OSNs is that users must fully trust a centralized service provider to properly handle their data.</p><p>This dissertation explores the feasibility of building feature-rich, privacy-preserving OSNs by shifting the bases for trust away from centralized service providers and third-party application developers and toward infrastructure providers and OSN users themselves.</p><p>We propose limiting the trust users place in service providers through two decentralized OSNs: Vis-a-Vis and Confidant. In Vis-a-Vis, privacy-sensitive data is only accessed by user-controlled code executing on ``infrastructure as a service" platforms such as EC2. In Confidant this data may only be accessed by code running on desktop PCs controlled by a user's close friends. To reduce</p><p>the risks posed by third-party OSN applications, we also developed a Multi-User Taint Tracker (MUTT). MUTT is a secure ``platform as a service" that ensures that third-party applications adhere to access policies defined by service providers and users. </p><p>Vis-a-Vis is a decentralized framework for location-based OSN services based on the</p><p>privacy-preserving notion of a Virtual Individual Server (VIS). A VIS is a personal virtual machine running within a paid compute utility. In Vis-a-Vis, a person stores her data on her own VIS, which arbitrates access to that data by others. VISs self-organize into overlay networks corresponding to social groups with whom their owners wish to share location information. Vis-a-Vis uses distributed location trees to provide efficient and scalable operations for creating, joining, leaving, searching, and publishing location data to these groups.</p><p>Confidant is a decentralized OSN platform designed to support a scalable application framework for OSN data without compromising users' privacy. Confidant replicates a user's data on servers controlled by her friends. Because data is stored on trusted servers, Confidant allows application code to run directly on these storage servers. To manage access-control policies under weakly-consistent replication, Confidant eliminates write conflicts through a lightweight cloud-based state manager and through a simple mechanism for updating the bindings between access policies and replicated data.</p><p>For securing risks from third-party OSN applications, this thesis proposes a Multi-User Taint Tracker (MUTT) -- a secure ``platform as a service'' designed to ensure that third-party applications adhere to access policies defined by service providers and users. Mutt's design is informed by a careful analysis of 170 Facebook apps, which allows us to characterize the requirements and risks posed by several classes of apps. Our MUTT prototype has been integrated into the AppScale cloud system, and experiments show that the additional data-confidentiality guarantees of running an app on MUTT come at a reasonable performance cost.</p> / Dissertation