Thesis (Ph.D.) - University of St Andrews, May 2010.

Thesis (Ph.D.)--University of Alberta, 2010. / Title from PDF file main screen (viewed on July 29, 2010). A thesis submitted to the Faculty of Graduate Studies and Research in partial fulfillment of the requirements for the degree of Doctor of Philosophy, Department of Computing Science, University of Alberta. Includes bibliographical references.

This study intends to explore how social media or social networking sites (SNSs) such as Facebook can facilitate communication channels or create dialogue spaces in a language class. Social media is a form of participatory media, which broadly refers to the “collection of communication channels or mediums (primarily online and mobile) through which social networks originate and are sustained” (Flew 2008:109). Although the term social media is often used as a collective term for SNSs or as the core trademark of Web 2.0, Flew (2008:17) also distinguishes social media by calling it a “communications infrastructure” that allows for “participation, interactivity, collaborative learning and social networking”. Flew (2008) identifies various online sites including the online encyclopaedia Wikipedia and the online user-generated video site YouTube as well as various personalised web space sites such as MySpace, Facebook, Friendster and Bebo as participatory media. The study’s data collection was situated at Nelson Mandela Metropolitan University (NMMU) and investigated how social media can be used to facilitate dialogue between a tutor and BKI1120 Communication in English B students in a Higher Education (HE) context using qualitative methodology. This study compared the use of existing and more traditional or conventional classroom communication practices with those of SNSs as a communication channel, while focusing on social media application as a communication tool to create dialogue spaces that support teaching and learning practices. The research also attempted to identify alternative applications of social media for teaching and learning practices to inform researchers in the fields of HE and media. In the first data-collection phase, BKI1120 Communication in English B Public Management students were selected as the sample for the study. Seventeen students participated in the BKI1120 Facebook page created for the purpose of this study. In the second data-collection phase, a taped focus-group interview was conducted with eight BKI1120 Communication in English B students. The interview transcript was then analysed qualitatively for themes. The research findings showed that social media or SNSs such as Facebook can facilitate communication channels or create dialogue spaces in a language class, if it is managed effectively.

This study aimed to provide researchers in the development of media studies with research into understanding the star as the object of desire in a contemporary context, using Robert Pattinson as the star and The Twilight Saga, which made him famous, as an example of the effects that the use of social and online media have on audiences in terms of their perception and identification of a particular star. This study drew from literature and theories such as stardom, star as the object of desire, audience theory, fantasy, desire and escapism as well as theory on globalisation, the mass media and online and social media. Whilst social and online media have been in existence for a number of years, there is little research that has been performed in order to determine whether or not the use of social and online media directly affect users’ understanding and perception of certain stars and films. There has also been little research performed in order to gain an understanding of fantasy and desire, in terms of films and film stars, outside the constraints of the cinema. This study examined this notion, noting that The Twilight Saga has been successful production worldwide, in order to discover whether or not the use of social and online media perpetuates obsession in the fans and audience members. The first part of the study that was conducted, applied certain theories discussed and developed in the literature review, to Robert Pattinson and The Twilight Saga in order to obtain a better understanding of the star and the film series in terms of cinema, stardom, fantasy and escapism and online and social media. A comparative case study of six online articles, from prominent online sources featuring Pattinson, was then conducted in order to investigate Pattinson’s image and status in the online community. Finally, a content analysis of various online and social media platforms such as Facebook, Twitter and YouTube was performed in order to find out what type of information and imagery was being generated about Pattinson and the Saga as well as to investigate how fans and followers engaged with the different media channels and what kinds of comments they were making about the star and the Saga. It was found that Pattinson, the character he plays in the film series, Edward Cullen, and The Twilight Saga have a large presence on key social media platforms such as Facebook and Twitter, with a vast amount of followers and fans; Facebook and Twitter being the most popular and interactive media avenues. It was also found that Pattinson, Edward and The Twilight Saga, through the avid use of the social media tools, elicited and incited signs of obsession, fantasy and desire within an extensive amount of fans and followers, outside the constraints of the cinema

隨著Web 2.0系統在過去十年的迅猛發展，社會化媒體，比如社會化評分系統、社會化標籤系統、在線論壇和社會化問答系統，已經革命性地改變了人們在互聯網上創造和分享內容的方式。但是，面對社會化媒體數據的飛速增長，用戶面臨嚴重的信息過載的問題。現在，基於社會化媒體學習的社會化計算，已經發展成爲了幫助社會化媒體用戶有效解決信息需求的一個重要的研究領域。一般來說，用戶在社會化媒體中發佈信息，期望通過社會化計算尋找到合適的項目。爲了更好地理解用戶的興趣，分析不同類型的用戶產生數據是非常重要的。另一方面，返回給用戶的可以是項目，或是擁有相似興趣的其他用戶。除了基於用戶的分析，進行基於項目的分析也是非常有趣和重要的，比如理解項目的屬性，將語義相關的項目聚在一起爲了更好地滿足用戶的信息需求等。 / 本論文的目地是提出自動化和可擴展的模型來幫助社會化媒體用戶更有效的解決信息需求。這些模型基於社會化媒體中兩個重要的組成提出：用戶和項目。因此，基於以下兩個目標，我們提出一個統一的框架來整合用戶信息和項目信息：1) 通過用戶的行為找出用戶的興趣，并為之推薦可能感興趣的項目和相似興趣的用戶；2) 理解項目的屬性，並將語義相關的項目聚合在一起從而能更好的滿足用戶的信息需求。 / 爲了完成第一個目標，我們提出了一個新的矩陣分解的框架來整合不同的用戶行為數據，從而預測用戶對新項目的興趣。這個框架有效地解決了數據稀疏性以及傳統方法中信息來源單一的問題，其次，爲了給社會化媒體用戶提供自動發現類似興趣的其他用戶的方式，通過利用社會化標籤信息，我們提出了基於用戶興趣挖掘和基於興趣的用戶推薦的框架。大量的真實數據實驗驗證了提出的基於用戶的模型的有效性。 / 爲了完成第二個目標，我們在具問答性質的社會化媒體中提出了問題推薦的應用。問題推薦的目標是基於一個用戶問題推薦語義相關的問題。傳統的詞袋模型不能有效地解決相關問題中用詞不同的問題。因此，我們提出了兩個模型來結合詞法分析以及潛在語義分析，從而有效地衡量問題間的語義相關度。在問題分析中，當前研究缺少對問題屬性的認識。爲了解決這個問題，我們提出了一個有監督學習的方法來識別問題的主觀性。具體來說，我們提出了一種基於社會化信號的無人工參與的自動收集訓練數據的方法。大量實驗證實了提出的方法的效果超過了之前的其他算法。 / 概括起來，圍繞社會化媒體中兩個重要的組成，我們提出了兩個基於用戶的模型和兩個基於項目的模型來幫助社會化媒體的用戶更準確更有效地解決信息需求。我們通過不同社會化媒體中的大量實驗證實了提出模型的有效性。 / With the astronomical growth of Web 2.0 over the past decade, social media systems, such as rating systems, social tagging systems, online forums, and community-based question answering (Q&A) systems, have revolutionized people’s way of creating and sharing contents on the Web. However, due to the explosive growth of data in social media systems, users are drowning in information and encountering information overload problem. Currently, social computing techniques, achieved through learning with social media, have emerged as an important research area to help social media users find their information needs. In general, users post contents which reflect their interests in social media systems, and expect to obtain the suitable items through social computing techniques. To better understand users’ interests, it is very essential to analyze different types of user generate content. On the other hand, the returned information may be items, or users with similar interests. Beyond the user-based analysis, it would be quite interesting and important to conduct item-oriented study, such as understand items’ characteristics, and grouping items that are semantically related for better addressing users’ information needs. / The objective of this thesis is to establish automatic and scalable models to help social media users find their information needs more effectively. These models are proposed based on the two key entities in social media systems: user and item. Thus, one important aspect of this thesis is therefore to develop a framework to combine the user information and the item information with the following two purposes: 1) modeling users’ interests with respect to their behavior, and recommending items or users they may be interested in; and 2) understanding items’ characteristics, and grouping items that are semantically related for better addressing users’ information needs. / For the first purpose, a novel unified matrix factorization framework which fuses different types of users’ behavior data, is proposed for predicting users’ interests on new items. The framework tackles the data sparsity problem and non-flexibility problem confronted by traditional algorithms. Furthermore, to provide users with an automatic and effective way to discover other users with common interests, we propose a framework for user interest modeling and interest-based user recommendation by utilizing users’ tagging information. Extensive evaluations on real world data demonstrate the effectiveness of the proposed user-based models. / For the second purpose, a new functionality question suggestion, which targets at suggesting questions that are semantically related to a queried question, is proposed in social media systems with Q&A functionalities. Existing bag-of-words approaches suffer from the shortcoming that they could not bridge the lexical chasm between semantically related questions. Therefore, we present two models which combines both the lexical and latent semantic knowledge to measure the semantic relatedness among questions. In question analysis, there is a lack of understanding of questions’ characteristics. To tackle this problem, a supervised approach is developed to identify questions’ subjectivity. Moreover, we come up with an approach to collect training data automatically by utilizing social signals without involving any manual labeling. The experimental results show that our methods perform better than the state-of-theart approaches. / In summary, based on the two key entities in social media systems,we present two user-based models and two item-oriented models to help social media users find their information needs more accurately and effectively through learning with social media. Extensive experiments on various social media systems confirm the effectiveness of proposed models. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhou, Chao. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 130-163). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Contribution --- p.9 / Chapter 1.3 --- Thesis Organization --- p.11 / Chapter 2 --- Background Review --- p.15 / Chapter 2.1 --- Recommender System Techniques --- p.15 / Chapter 2.1.1 --- Content-based Filtering --- p.16 / Chapter 2.1.2 --- Collaborative Filtering --- p.17 / Chapter 2.2 --- Information Retrieval Models --- p.23 / Chapter 2.2.1 --- Vector Space Model --- p.24 / Chapter 2.2.2 --- Probabilistic Model and Language Model --- p.27 / Chapter 2.2.3 --- Translation Model --- p.31 / Chapter 2.3 --- Machine Learning --- p.32 / Chapter 2.3.1 --- Supervised Learning --- p.32 / Chapter 2.3.2 --- Semi-Supervised Learning --- p.34 / Chapter 2.3.3 --- Unsupervised Learning --- p.36 / Chapter 2.4 --- Rating Prediction --- p.37 / Chapter 2.5 --- User Recommendation --- p.39 / Chapter 2.6 --- Automatic Question Answering --- p.40 / Chapter 2.6.1 --- Automatic Question Answering (Q&A) from theWeb --- p.40 / Chapter 2.6.2 --- Proliferation of community-based Q&A services and online forums --- p.41 / Chapter 2.6.3 --- Automatic Question Answering (Q&A) in social media --- p.42 / Chapter 3 --- Item Recommendation with Tagging Ensemble --- p.44 / Chapter 3.1 --- Problem and Motivation --- p.44 / Chapter 3.2 --- TagRec Framework --- p.45 / Chapter 3.2.1 --- Preliminaries --- p.45 / Chapter 3.2.2 --- User-Item Rating Matrix Factorization --- p.45 / Chapter 3.2.3 --- User-Tag Tagging Matrix Factorization --- p.47 / Chapter 3.2.4 --- Item-Tag Tagging Matrix Factorization --- p.49 / Chapter 3.2.5 --- A Unified Matrix Factorization for TagRec --- p.50 / Chapter 3.2.6 --- Complexity Analysis --- p.53 / Chapter 3.3 --- Experimental Analysis --- p.54 / Chapter 3.3.1 --- Description of Data Set and Metrics --- p.54 / Chapter 3.3.2 --- Performance Comparison --- p.55 / Chapter 3.3.3 --- Impact of Parameters and --- p.56 / Chapter 3.4 --- Summary --- p.58 / Chapter 4 --- User Recommendation via Interest Modeling --- p.60 / Chapter 4.1 --- Problem and Motivation --- p.60 / Chapter 4.2 --- UserRec Framework --- p.61 / Chapter 4.2.1 --- User Interest Modeling --- p.61 / Chapter 4.2.2 --- Interest-based User Recommendation --- p.65 / Chapter 4.3 --- Experimental Analysis --- p.67 / Chapter 4.3.1 --- Dataset Description and Analysis --- p.67 / Chapter 4.3.2 --- Experimental Results --- p.70 / Chapter 4.4 --- Summary --- p.75 / Chapter 5 --- Item Suggestion with Semantic Analysis --- p.76 / Chapter 5.1 --- Problem and Motivation --- p.76 / Chapter 5.2 --- Question Suggestion Framework --- p.77 / Chapter 5.2.1 --- Question Suggestion in Online Forums --- p.77 / Chapter 5.2.2 --- Question Suggestion in Community-based Q&A Services --- p.84 / Chapter 5.3 --- Experiments And Results --- p.88 / Chapter 5.3.1 --- Experiments in Online Forums --- p.88 / Chapter 5.3.2 --- Experiments in Community-based Q&A Services --- p.96 / Chapter 5.4 --- Summary --- p.105 / Chapter 6 --- Item Modeling via Data-Driven Approach --- p.106 / Chapter 6.1 --- Problem and Motivation --- p.106 / Chapter 6.2 --- Question Subjectivity Identification --- p.107 / Chapter 6.2.1 --- Social Signal Investigation --- p.107 / Chapter 6.2.2 --- Feature Investigation --- p.110 / Chapter 6.3 --- Experimental Evaluation --- p.112 / Chapter 6.3.1 --- Experimental Setting --- p.112 / Chapter 6.3.2 --- Effectiveness of Social Signals --- p.114 / Chapter 6.3.3 --- Effectiveness of Heuristic Features --- p.116 / Chapter 6.4 --- Summary --- p.122 / Chapter 7 --- Conclusion --- p.123 / Chapter 7.1 --- Summary --- p.123 / Chapter 7.2 --- Future Work --- p.124 / Chapter A --- List of Publications --- p.126 / Chapter A.1 --- Conference Publications --- p.126 / Chapter A.2 --- Journal Publications --- p.127 / Chapter A.3 --- Under Review --- p.128 / Bibliography --- p.129

Millions of users share their experiences, thoughts, and interests online, through social media sites (e.g., Twitter, Flickr, YouTube). As a result, these sites host a substantial number of user-contributed documents (e.g., textual messages, photographs, videos) for a wide variety of events (e.g., concerts, political demonstrations, earthquakes). In this dissertation, we present techniques for leveraging the wealth of available social media documents to identify and characterize events of different types and scale. By automatically identifying and characterizing events and their associated user-contributed social media documents, we can ultimately offer substantial improvements in browsing and search quality for event content. To understand the types of events that exist in social media, we first characterize a large set of events using their associated social media documents. Specifically, we develop a taxonomy of events in social media, identify important dimensions along which they can be categorized, and determine the key distinguishing features that can be derived from their associated documents. We quantitatively examine the computed features for different categories of events, and establish that significant differences can be detected across categories. Importantly, we observe differences between events and other non-event content that exists in social media. We use these observations to inform our event identification techniques. To identify events in social media, we follow two possible scenarios. In one scenario, we do not have any information about the events that are reflected in the data. In this scenario, we use an online clustering framework to identify these unknown events and their associated social media documents. To distinguish between event and non-event content, we develop event classification techniques that rely on a rich family of aggregate cluster statistics, including temporal, social, topical, and platform-centric characteristics. In addition, to tailor the clustering framework to the social media domain, we develop similarity metric learning techniques for social media documents, exploiting the variety of document context features, both textual and non-textual. In our alternative event identification scenario, the events of interest are known, through user-contributed event aggregation platforms (e.g., Last.fm events, EventBrite, Facebook events). In this scenario, we can identify social media documents for the known events by exploiting known event features, such as the event title, venue, and time. While this event information is generally helpful and easy to collect, it is often noisy and ambiguous. To address this challenge, we develop query formulation strategies for retrieving event content on different social media sites. Specifically, we propose a two-step query formulation approach, with a first step that uses highly specific queries aimed at achieving high-precision results, and a second step that builds on these high-precision results, using term extraction and frequency analysis, with the goal of improving recall. Importantly, we demonstrate how event-related documents from one social media site can be used to enhance the identification of documents for the event on another social media site, thus contributing to the diversity of information that we identify. The number of social media documents that our techniques identify for each event is potentially large. To avoid overwhelming users with unmanageable volumes of event information, we design techniques for selecting a subset of documents from the total number of documents that we identify for each event. Specifically, we aim to select high-quality, relevant documents that reflect useful event information. For this content selection task, we experiment with several centrality-based techniques that consider the similarity of each event-related document to the central theme of its associated event and to other social media documents that correspond to the same event. We then evaluate both the relative and overall user satisfaction with the selected social media documents for each event. The existing tools to find and organize social media event content are extremely limited. This dissertation presents robust ways to organize and filter this noisy but powerful event information. With our event identification, characterization, and content selection techniques, we provide new opportunities for exploring and interacting with a diverse set of social media documents that reflect timely and revealing event content. Overall, the work presented in this dissertation provides an essential methodology for organizing social media documents that reflect event information, towards improved browsing and search for social media event data.

基于地理位置的社交网络近年得到了非常多的关注。为了提升用戶粘性和吸引用戶，社交問路提供商会提供給用戶基于地理信息的广告和优惠券等服务。方了让广告和优惠券的投递更有效， 预测用戶下个可能访问的地点变得尤为重要。但是，预测地点一个不可避免的挑战就是數一百万计的候选地点构成了庞大的預測空间，使得整个预测过程变成复杂且缓慢。在本论文中，我們利用用戶签到的类別信息对潜在的用戶运动模式進行了建模并提出了一个混合隐马尔可夫模型去预测用戶下个可能访问的地点类别。基于预测出的类别，我們继而对用戶可能访问的地点进行了預測。在类別层次进行建模的好处是能有效地減少候选地点的个數并且能准确地描述用戶行动的实际意义。一般来說，用戶的行为会受到令人偏好的影响，基于这个現象，我們还运用分类的方法对用戶根据其令人愛好的不同進行了划分并对每个组群制定各自的隐马尔可夫模型。实验結果表示如果先预测用可能访问的地点类别，能使得地点预测空间极大地减少预测精度也会变高。 / 在预测用可能访问的地点之后，另外一个很重要的问题是选择将优惠券投递给哪些用从而将产品或地点的影响最大化。在实际运用中，这种将影响最大化的算法会遇到速度上的壁垒。在本论文中，我们研究了在基于地理位置的社交网络中的影响最大化问题，并提出了一个分割方法能有效地提升算法的运行速度。实验结果显示我们的算法在于业界标准方法达到几乎一致的影响力的前提下，能更快地运行。 / Location-based social networks have been gaining increasing popularity in recent years. To increase users’ engagement with location-based services, it is important to provide attractive features, one of which is geo-targeted ads and coupons. To make ads and coupons delivery more effective, it is essential to predict the location that is most likely to be visited by a user at the next step. However, an inherent challenge in location prediction is a huge prediction space, with millions of distinct check-in locations as prediction target. In this thesis we exploit the check-in category information to model the underlying user movement pattern. We propose a framework which uses a mixed hidden Markov model to predict the category of user activity at the next step and then predicts the most likely location given the estimated category distribution. The advantages of modeling on the category level include a significantly reduced prediction space and a precise expression of the semantic meaning of user activities. In addition, as user check-in behaviors are heavily influenced by their preferences, we take a clustering approach to group users with similar preferences, and train a separate hidden Markov model for each group. Extensive experimental results show that, with the predicted category distribution, the number of location candidates for prediction is much smaller, while the location prediction accuracy becomes higher. / Choosing the right users to deliver the coupons and maximizing the influence spread is also an important problem in LBSN, which is called influence maximization problem. In practice speed is an important issue to solve the influence maximization problem. In this thesis, we study the influence maximization problem in location-based social networks and propose a scalable partition approach to solve the influence maximization problem efficiently. Experimental results show that our partition approach achieves quite similar influence spread performance with the original influence maximization approach, while running much faster. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Zhu, Zhe. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2012. / Includes bibliographical references (leaves 93-101). / Abstracts also in Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Location prediction --- p.11 / Chapter 2.2 --- Influence maximization --- p.16 / Chapter 3 --- User Activity and Location Prediction in Location-based Social Networks --- p.20 / Chapter 3.1 --- Data Analysis --- p.20 / Chapter 3.1.1 --- Data Collection --- p.21 / Chapter 3.1.2 --- Dataset Properties --- p.22 / Chapter 3.2 --- User Activity Prediction --- p.26 / Chapter 3.2.1 --- Definitions --- p.27 / Chapter 3.2.2 --- Category Prediction based on HMM --- p.28 / Chapter 3.2.3 --- Mixed HMM with Temporal and Spatial Covariates --- p.34 / Chapter 3.2.4 --- User Preference Modeling --- p.41 / Chapter 3.3 --- Location Prediction --- p.43 / Chapter 3.4 --- Experimental Evaluation --- p.45 / Chapter 3.4.1 --- Data Preparation --- p.46 / Chapter 3.4.2 --- Category Prediction --- p.47 / Chapter 3.4.3 --- Location Prediction --- p.51 / Chapter 3.5 --- Summary --- p.58 / Chapter 4 --- A Partition Approach to Scalable Influence Maximization in Location-based Social Networks --- p.60 / Chapter 4.1 --- Problem definition --- p.60 / Chapter 4.2 --- Influence probability --- p.62 / Chapter 4.2.1 --- Base model --- p.62 / Chapter 4.2.2 --- Distance and similarity model --- p.65 / Chapter 4.2.3 --- Location entropy model --- p.72 / Chapter 4.3 --- Partition approach --- p.74 / Chapter 4.4 --- Evaluation --- p.79 / Chapter 4.4.1 --- Data preparation --- p.79 / Chapter 4.4.2 --- Precision evaluation --- p.80 / Chapter 4.4.3 --- Influence spread evaluation --- p.83 / Chapter 4.4.4 --- Running time --- p.86 / Chapter 4.5 --- Summary --- p.88 / Chapter 5 --- Conclusion --- p.90 / Bibliography --- p.93

Mo, Mingzhen. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2011. / Includes bibliographical references (p. 111-123). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.v / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Thesis Contributions --- p.5 / Chapter 1.3 --- Problem Description --- p.6 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Background Study --- p.11 / Chapter 2.1 --- Overview --- p.11 / Chapter 2.2 --- Problem Definitions --- p.12 / Chapter 2.3 --- Privacy in Online Social Networks --- p.14 / Chapter 2.4 --- Attack --- p.17 / Chapter 2.4.1 --- Statistical Learning --- p.18 / Chapter 2.4.2 --- Graph Theory --- p.22 / Chapter 2.5 --- Protection --- p.23 / Chapter 2.5.1 --- Clustering-Based Approach --- p.24 / Chapter 2.5.2 --- Modification-Based Approach --- p.27 / Chapter 3 --- Exploit Social Networks with SSL --- p.30 / Chapter 3.1 --- Overview --- p.31 / Chapter 3.2 --- Semi-Supervised Learning Framework --- p.35 / Chapter 3.2.1 --- Co-Training SSL --- p.36 / Chapter 3.2.2 --- Graph-Based SSL --- p.38 / Chapter 3.2.3 --- Local and Global Consistency Graph-Based SSL --- p.39 / Chapter 3.3 --- Experiment --- p.40 / Chapter 3.3.1 --- Dataset Description --- p.41 / Chapter 3.3.2 --- Data Preprocessing --- p.43 / Chapter 3.3.3 --- Experiment Process --- p.45 / Chapter 3.3.4 --- Experiment Results --- p.47 / Chapter 3.4 --- Conclusion --- p.49 / Chapter 4 --- Exploiting Social Networks with CG SSL --- p.50 / Chapter 4.1 --- Overview --- p.51 / Chapter 4.2 --- Exploit Learning Model and Algorithms --- p.56 / Chapter 4.2.1 --- Exploit Learning Model --- p.57 / Chapter 4.2.2 --- Algorithms --- p.60 / Chapter 4.2.3 --- Community Generation --- p.65 / Chapter 4.3 --- Experiment --- p.66 / Chapter 4.3.1 --- Dataset Description --- p.67 / Chapter 4.3.2 --- Data Preprocessing --- p.70 / Chapter 4.3.3 --- Experiment Process --- p.72 / Chapter 4.3.4 --- Experiment Results --- p.77 / Chapter 4.4 --- Conclusion --- p.82 / Chapter 5 --- APA Comparison Scheme --- p.83 / Chapter 5.1 --- Overview --- p.84 / Chapter 5.2 --- Attack-Protect-Attack (APA) Comparisons Scheme --- p.87 / Chapter 5.2.1 --- Algorithm --- p.87 / Chapter 5.2.2 --- Attack & Protection Approaches --- p.88 / Chapter 5.3 --- Experiment --- p.91 / Chapter 5.3.1 --- Dataset Description --- p.92 / Chapter 5.3.2 --- Data Preprocessing --- p.92 / Chapter 5.3.3 --- Experiment Process --- p.94 / Chapter 5.3.4 --- Experiment Result --- p.95 / Chapter 5.4 --- Conclusion --- p.103 / Chapter 6 --- Conclusion and Future Work --- p.105 / Chapter 6.1 --- Conclusion --- p.105 / Chapter 6.2 --- Future Work --- p.107 / Bibliography --- p.111

Online social networks have quickly become the most popular destination on the World Wide Web. These networks are still a fairly new form of online human interaction and have gained wide popularity only recently within the past three to four years. Few models or descriptions of the dynamics of these systems exist. This is largely due to the difficulty in gaining access to the data from these networks which is often viewed as very valuable. In these networks, members maintain list of friends with which they share content with by first uploading it to the social network service provider. The content is then distributed to members by the service provider who generates a feed for each member containing the content shared by all of the member's friends aggregated together. Direct access to dynamic linkage data for these large networks is especially difficult without a special relationship with the service provider. This makes it difficult for researchers to explore and better understand how humans interface with these systems. This dissertation examines an event driven sampling approach to acquire both dynamics link event data and blog content from the site known as LiveJournal. LiveJournal is one of the oldest online social networking sites whose features are very similar to sites such as Facebook and Myspace yet smaller in scale as to be practical for a research setting. The event driven sampling methodology and analysis of the resulting network model provide insights for other researchers interested in acquiring social network dynamics from LiveJournal or insight into what might be expected if an event driven sampling approach was applied to other online social networks. A detailed analysis of both the static structure and network dynamics of the resulting network model was performed. The analysis helped motivated work on a model of link prediction using both topological and content-based metrics. The relationship between topological and content-based metrics was explored. Factored into the link prediction analysis is the open nature of the social network data where new members are constantly joining and current members are leaving. The data used for the analysis spanned approximately two years.

Dissertation Ludwig Maximilians Universität München, 2009.