One of the de ning characteristics of the modern Internet is its massive connectedness, with information and human connection simply a few clicks away. Social media and online retailers have revolutionized how we communicate and purchase goods or services. User generated content on the web, through social media, plays a large role in modern society; Twitter has been in the forefront of political discourse, with politicians choosing it as their platform for disseminating information, while websites like Amazon and Yelp allow users to share their opinions on products via online reviews. The information available through these platforms can provide insight into a host of relevant topics through the process of machine learning. Speci - cally, this process involves text mining for sentiment analysis, which is an application domain of machine learning involving the extraction of emotion from text. Unfortunately, there are still those with malicious intent and with the changes to how we communicate and conduct business, comes changes to their malicious practices. Social bots and fake reviews plague the web, providing incorrect information and swaying the opinion of unaware readers. The detection of these false users or posts from reading the text is di cult, if not impossible, for humans. Fortunately, text mining provides us with methods for the detection of harmful user generated content. This dissertation expands the current research in sentiment analysis, fake online review detection and election prediction. We examine cross-domain sentiment analysis using tweets and reviews. Novel techniques combining ensemble and feature selection methods are proposed for the domain of online spam review detection. We investigate the ability for the Twitter platform to predict the United States 2016 presidential election. In addition, we determine how social bots in uence this prediction. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

This thesis project seeks to answer the question of how visual rhetoric put forward in social media content by pro-life and pro-choice organizations may persuade their audiences’ perspective on abortion. Using Sonja Foss’s guidelines for analysis of visual rhetoric, I analyze 24 selected examples of Facebook content posted by two pro-life organizations (Human Coalition and Feminists for Life) and two pro-choice organizations (Planned Parenthood Action and NARAL Pro-Choice America) in 2017. My analysis found that the visual rhetoric posted by both organizations on social media can and does function as a form of visual metonymy. Because of this, these visual strategies can stand in for more complex arguments in dramatic ways. / Includes bibliography. / Thesis (M.A.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

Yam, Shing Chung Jonathan. / Thesis Ph.D. Chinese University of Hong Kong 2015. / Includes bibliographical references (leaves 143-170). / Abstracts also in Chinese. / Title from PDF title page (viewed on 15, September, 2016). / Yam, Shing Chung Jonathan.

当今，越来越多的用倾向于使用论坛、博客、脸书等在线工具来表达关于商品、电影和政治等话题的观点。这些观点不仅可以帮助用进行决策，同时也为各个商业和社会领域提供了具有重要价值的反馈信息。因此，面向观点应用成为了当前最活跃的研究领域之一，其中包括观点检索，观点摘要，观点问答。面向观点应用与面向事实应用的根本区别是信息需求的不同，分别是传统的客观信息和主观信息。所谓主观信息是指对于某个特定目标的观点或评论。为了表示主观信息，应该综合考虑观点性、主题相关性，以及观点与主题之间的关联。现有的基于词袋的表示方法将词作为描述客观信息的基本语义单元，它可以有效的表示主题相关性以满足客观信息的需求。而主观信息需要同时考虑观点性和主题相关性，由于单独一个词不能同时表示观点性和相关性，因此词不再是最小的语义单位。此外，基于词袋的表示方法忽略了词序和词义，这使得观点性和相关性两类信息通常混在一起，难以区分。因此，基于词袋方法不能够准确的表示主观信息，并严重的影响了面向观点应用的性能。 / 本文回答了以下几个由主观信息表示不当所引发的研究问题： 1. 对于主观信息而言单个词将不再是基本语义单元，是否存在一种有效的表示方法对其进行描述？ 2. 由于主观信息是观点信息和相关性信息的结合，如何利用新的表示方法来描述这二者之间的关联信息？3. 如何对主观信息进行量化，以便对文档进行检索和分析？ 4. 如何在面向观点应用中实现全新的主观信息表示方法？ / 由于观点检索的结果会直接影响到其它面向观点应用的性能，因此本文从观点检索这一问题入手。首先，我们提出了一种基于句子的方法来分析词袋表示方法的局限性。以此为据，定义了一种具有丰富语义的表达方式来表示主观信息，即词对，它是由出现在同一句子中的情感词和与之关联的目标词共同组成的。然后，我们提出了一系列方法来描述和获取两类语境信息：1）观点内信息：我们给出了三种提取词对的方法以获取观点与主题的关联信息；2）观点间信息：我们提出了一种权重计算方法来度量词对间的相关程度，从而获取词对与词对之间的关系。最后，我们集成了观点内信息和观点间信息并提出了潜在情感关联模型来解决观点检索这一问题。在标准数据集上的实验结果表明，基于词对的表示方法可以有效地描述主观信息，同时潜在情感关联模型能够获取词与词之间的关联信息，从而实现了利用语境信息提高观点检索的效果。 / 此外，我们将词对应用于观点摘要和观点问答中，标准数据集上的评测结果显示基于词对的主观信息表示方法对于其它面向观点应用也同样有效。 / There is a growing interest for users to express their opinions about products, films, politics, by using on-line tools such as forums, blogs, facebooks, etc. These opinions cannot only help users make decisions, e.g., whether to buy a product, but also to ob-tain valuable feedback for business and social events. Today, research on opin-ion-oriented applications (OOAs) including opinion retrieval, opinion summarization and opinion question and answering is attracting much attention. The difference be-tween fact-based and opinion-oriented applications lies in users‘ information need. The former requires objective information and the latter subjective, which comprises of opinions or comments expressed on a specific target. To meet the need of subjective information, both opinionatedness and relevance together with the association between them should be taken into account. Existing systems represent documents in bag-of-word. However, this representation fails to distinguish opinionatedness from relevance. Moreover, due to the ignorance of word sequence, words associations are lost. For this reason, bag-of-word representation is ineffective for subjective information, and affects the performance of OOAs seriously. / In this thesis, we try to answer the following challenging questions arose in subjective information representation. Since word is no longer the basic semantic unit, how would subjective information be represented? Subjective information is a combination of opinionatedness and relevance, so how would the association between them be modeled? How would subjective information be measured for the purpose of document ranking, retrieval, and analysis? How would opinion-oriented applications benefit from subjective information? / We start from solving the problem of opinion retrieval whose results can directly influence the performance of other opinion-oriented applications. We first present a sentence-based approach to analyze the limitation of bag-of-word representation and define a semantically richer representation, namely word pair for subjective infor-mation. A word pair is constructed by a sentiment word and its associated target co-occurring in a sentence. We then propose techniques to capture two kinds of con-textual information. 1) Intra-opinion information: three methods are proposed to ex-tract the word pair. 2) Inter-opinion information: a weighting scheme is present to measure the weight of individual word pair. Finally, we devise an algorithm to integrate both intra-opinion and inter-opinion information into a latent sentimental association model for opinion retrieval. The evaluation on three benchmark datasets suggests the effectiveness of word pair and the latent sentimental association retrieval model provide insight into the words association to support opinion retrieval beneficial from pairwise representation. We also apply word pair to opinion summarization and opinion question answering. The evaluation on two benchmark datasets shows that word pair performs effectively in the applications. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Detailed summary in vernacular field only. / Li, Binyang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves [96]-103). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract also in Chinese. / Abstract --- p.ii / Abstract in Chinese --- p.iv / Acknowledgements --- p.vi / Contents --- p.viii / List of Tables --- p.xi / List of Figures --- p.xiii / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Problem and Challenges --- p.3 / Chapter 1.1.1 --- Subjective Information Representation --- p.3 / Chapter 1.1.2 --- Associative Information in an Opinion Expression --- p.4 / Chapter 1.1.3 --- Opinion Expression Measurement --- p.5 / Chapter 1.1.4 --- Applications of Subjective Information Representation to Different OOAs --- p.6 / Chapter 1.2. --- Contributions --- p.6 / Chapter 1.3. --- Chapter Summary --- p.7 / Chapter 2. --- Pairwise Representation --- p.9 / Chapter 2.1 --- Related Woks on Opinion Retrieval --- p.10 / Chapter 2.1.1 --- Opinion Retrieval Models --- p.10 / Chapter 2.1.2 --- Lexicon-based Opinion Identification --- p.12 / Chapter 2.2 --- Sentence-based Approach for Opinion Retrieval --- p.13 / Chapter 2.2.1 --- The Limitations of Document-based Approaches for Opinion Retrieval --- p.13 / Chapter 2.2.2 --- Sentence-based Approach for Opinion Retrieval --- p.16 / Chapter 2.2.3 --- Evaluation and Results --- p.21 / Chapter 2.2.4 --- Summary --- p.26 / Chapter 2.3 --- Pairwise Representation --- p.28 / Chapter 2.3.1 --- Definition of Word Pair --- p.28 / Chapter 2.3.2 --- Sentiment Lexicon Construction --- p.29 / Chapter 2.3.3 --- Topic Term Lexicon Construction --- p.30 / Chapter 2.3.4 --- Word Pair Construction --- p.31 / Chapter 2.4 --- Graph-based Model for Opinion Retrieval --- p.33 / Chapter 2.4.1 --- HITS Model for Opinion Retrieval --- p.34 / Chapter 2.4.2 --- PageRank Model for Opinion Retrieval --- p.37 / Chapter 2.4.3 --- Evaluation and Results --- p.40 / Chapter 2.5 --- Chapter Summary --- p.50 / Chapter 3. --- Pairwise Representation Measurement --- p.51 / Chapter 3.1 --- Word Pair Weighting Scheme --- p.52 / Chapter 3.1.1 --- PMI-based Weighting Scheme --- p.52 / Chapter 3.1.2 --- Evaluation and Results --- p.56 / Chapter 3.1.3 --- Summary --- p.60 / Chapter 3.2 --- Latent Sentimental Association --- p.61 / Chapter 3.2.1 --- Problem Formulation --- p.61 / Chapter 3.2.2 --- LSA Integrated Generative Model --- p.62 / Chapter 3.2.3 --- Modeling the Dependency between Q and d --- p.64 / Chapter 3.2.4 --- Modeling the Dependency between O and d --- p.67 / Chapter 3.3 --- Parameter Estimation --- p.67 / Chapter 3.3.1 --- Estimating P(Q --- p.67 / Chapter 3.3.2 --- Estimating MI(Q,O --- p.69 / Chapter 3.4 --- Evaluation and Results --- p.69 / Chapter 3.5 --- Chapter Summary --- p.72 / Chapter 4. --- Pairwise Representation in Opinion-oriented Application --- p.75 / Chapter 4.1. --- Opinion Questioning and Answering --- p.76 / Chapter 4.1.1 --- Problem Statement --- p.76 / Chapter 4.1.2 --- Existing Solution --- p.78 / Chapter 4.1.3 --- A Word Pair based Approach for Sentence Ranking --- p.79 / Chapter 4.1.4 --- Answer Generation --- p.82 / Chapter 4.1.5 --- Evaluation and Results --- p.82 / Chapter 4.2. --- Opinion Summarization --- p.86 / Chapter 4.2.1 --- Problem Statement --- p.86 / Chapter 4.2.2 --- Existing Solution --- p.87 / Chapter 4.2.3 --- Sentence Ranking --- p.88 / Chapter 4.2.4 --- Summary Generation --- p.88 / Chapter 4.2.5 --- Evaluation and Results --- p.89 / Chapter 4.3. --- Chapter Summary --- p.91 / Chapter 5. --- Conclusions and Future Works --- p.93 / Bibliography --- p.97

The idea that daily life overflows with data has entered our common sense. Digital sensors placed in phones, clothing, or household appliances to track how we walk, how much we sleep, or where we travel have heightened the sense that everything about our lives is rapidly being translated into data. Theorists writing about data overload have largely converged around questions of privacy and agency, focusing on the feelings of impotence produced by large quantities of data that now let corporations effortlessly monitor and regulate people’s lives. By contrast, I am interested in moments of friction. Scholars point to real issues, but they overstate the efficacy of data gathering and discount the professional dynamics that motivate the proliferation of data. As I evaluate how data discourse operates and builds, I concentrate on the experiences of those involved in the business of self-tracking, and mainly on the work of U.S.-based developers of wearable computing and the technology professionals who participate in the international forum for data enthusiasts called the Quantified Self. As I analyze how digital entrepreneurialism configures notions of data and transforms digital self-monitoring into meaningful work, I examine how the relationship of technology professionals to data opens onto wider debates about the politics of digital representation. Ultimately, by applying an anthropological lens to explore how the practices, beliefs, and views of marketers, engineers, and developers of self-tracking tools shape digital knowledge, this research challenges accounts of data based purely on transparency, anxiety, and fear and reveals just how precarious the control exerted by digital companies and self-monitoring tools really is.

<p>IPTV is a growing form of distribution for TV and media. Reports show that the market will grow from the current 20-30 million subscribers to almost 100 million 2012. IPTV extends the traditional TV viewing with new services like renting movies from your TV. It could also be seen as a bridge between the traditional broadcast approach and the new on demand approach the users are used to from internet.</p><p>Since there are many actors in the IPTV market that all deliver the same basic functionality, companies must deliver better products that separate them from the competitors. This can be done either through doing things better than the others and/or delivering functionality that others can’t deliver.</p><p>This thesis project presents the development of a prototype system for serving user generated content in the IPTV middleware Dreamgallery. The developed prototype is a fully working system that includes (1) a fully automated system for transcoding, of video content. (2) A web portal presented with solutions for problems related to user content uploading and administration. (3) Seamless integration with the Dreamgallery middleware and end user GUI, with two different ways of viewing content. One way for easy exploration of new content and a second more structured way of browsing the content.</p><p>A study of three open source encoding softwares is also presented. The three encoders were subjects to tests of: speed, agility (file format support) and how well they handle files with corrupted data.</p>

<p>Uppkomsten av dagens Web 2.0 har skapat möjligheter till större interaktivitet hos användarna. Denna utveckling har även följts av en möjlighet att skapa användargenererat innehåll, ett fenomen som benämns User Generated Content (UGC). En av de viktigaste aspekterna inom UGC är att det måste uppnå en god användbarhet, men likväl måste tjänsterna också erbjuda en rik subjektiv upplevelse. Denna subjektiva upplevelse benämns som User Experience och är ett uttryck för den upplevelse och tillfredställelse en användare känner då den ställs inför ett interaktivt gränssnitt. Syftet med uppsatsen var att identifiera User Experience av UGC-tjänster. Uppsatsen karaktäriseras av en kvalitativ ansats och grundar sig i en explorativ undersökning med loggböcker och intervjuer, där nio respondenters upplevelser ligger till grund för uppsatsens resultat. Uppsatsen bidrar med en modell över User Experience mål för UGC-tjänster. Modellen bidrar till en ökad förståelse för vad som utgör en god User Experience av en UGC-tjänst och kan på så vis vara vägledande för de som designar dessa typer av tjänster.</p>

The proliferation of social media, such as blogs, micro-blogs and social networks, has led to a plethora of readily available user-generated content. The latter offers a unique, uncensored window into emerging stories and events, ranging from politics and revolutions to product perception and the zeitgeist. Importantly, structured information is available for user-generated content, by dint of its metadata, or can be surfaced via recently commoditized information extraction tools. This wealth of information, in the form of real-world entities and facts mentioned in a document, author demographics, and so on, provides exciting opportunities for mining insights from this content. Capitalizing upon these, we develop Grapevine, an online system that distills information from the social media collective on a daily basis, and facilitates its interactive exploration. To further this goal, we address important research problems, which are also of independent interest. The sheer scale of the data being processed, necessitates that our solutions be highly efficient. We propose efficient techniques for mining important stories, on a per-user-demographic basis, based on named entity co-occurrences in user-generated content. Building upon these, we propose efficient techniques for identifying emerging stories as-they-happen, by identifying dense structures in an evolving entity graph. To facilitate the exploration of these stories, we propose efficient techniques for filtering them, based on users’ textual descriptions of the entities involved. These gathered insights need to be presented to users in a useful manner, via a diverse set of representative documents; we thus propose efficient techniques for addressing this problem. Recommending related stories to users is important for navigation purposes. As the way in which these are related to the story being explored is not always clear, we propose efficient techniques for generating recommendation explanations via entity relatedness queries.

The proliferation of social media, such as blogs, micro-blogs and social networks, has led to a plethora of readily available user-generated content. The latter offers a unique, uncensored window into emerging stories and events, ranging from politics and revolutions to product perception and the zeitgeist. Importantly, structured information is available for user-generated content, by dint of its metadata, or can be surfaced via recently commoditized information extraction tools. This wealth of information, in the form of real-world entities and facts mentioned in a document, author demographics, and so on, provides exciting opportunities for mining insights from this content. Capitalizing upon these, we develop Grapevine, an online system that distills information from the social media collective on a daily basis, and facilitates its interactive exploration. To further this goal, we address important research problems, which are also of independent interest. The sheer scale of the data being processed, necessitates that our solutions be highly efficient. We propose efficient techniques for mining important stories, on a per-user-demographic basis, based on named entity co-occurrences in user-generated content. Building upon these, we propose efficient techniques for identifying emerging stories as-they-happen, by identifying dense structures in an evolving entity graph. To facilitate the exploration of these stories, we propose efficient techniques for filtering them, based on users’ textual descriptions of the entities involved. These gathered insights need to be presented to users in a useful manner, via a diverse set of representative documents; we thus propose efficient techniques for addressing this problem. Recommending related stories to users is important for navigation purposes. As the way in which these are related to the story being explored is not always clear, we propose efficient techniques for generating recommendation explanations via entity relatedness queries.

Uppkomsten av dagens Web 2.0 har skapat möjligheter till större interaktivitet hos användarna. Denna utveckling har även följts av en möjlighet att skapa användargenererat innehåll, ett fenomen som benämns User Generated Content (UGC). En av de viktigaste aspekterna inom UGC är att det måste uppnå en god användbarhet, men likväl måste tjänsterna också erbjuda en rik subjektiv upplevelse. Denna subjektiva upplevelse benämns som User Experience och är ett uttryck för den upplevelse och tillfredställelse en användare känner då den ställs inför ett interaktivt gränssnitt. Syftet med uppsatsen var att identifiera User Experience av UGC-tjänster. Uppsatsen karaktäriseras av en kvalitativ ansats och grundar sig i en explorativ undersökning med loggböcker och intervjuer, där nio respondenters upplevelser ligger till grund för uppsatsens resultat. Uppsatsen bidrar med en modell över User Experience mål för UGC-tjänster. Modellen bidrar till en ökad förståelse för vad som utgör en god User Experience av en UGC-tjänst och kan på så vis vara vägledande för de som designar dessa typer av tjänster.