Search (53 results, page 1 of 3)

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Source

Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker

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Abstract

This study investigates the interplay between online news, reader comments, and social networks to detect and characterize comments leading to the revelation of censored information. Censorship of identity occurs in different contexts-for example, the military censors the identity of personnel and the judiciary censors the identity of minors and victims. We address three objectives: (a) assess the relevance of identity censorship in the presence of user-generated comments, (b) understand the fashion of censorship circumvention (what people say and how), and (c) determine how comment analysis can aid in identifying decensorship and information leakage through comments. After examining 3,582 comments made on 48 articles containing obfuscated terms, we find that a systematic examination of comments can compromise identity censorship. We identify and categorize information leakage in comments indicative of knowledge of censored information that may result in information decensorship. We show that the majority of censored articles contained at least one comment leading to censorship circumvention.

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Abstract

The Internet has become the first source of consumer health information. Most theoretical and empirical studies are centered on information needs and seeking, rather than on information outcomes. This review's purpose is to explore and explain health outcomes of Online Consumer Health Information (OCHI) in primary care. A participatory systematic mixed studies review with a framework synthesis was undertaken. Starting from an initial conceptual framework, our specific objectives were to (a) identify types of OCHI outcomes in primary care, (b) identify factors associated with these outcomes, and (c) integrate these factors and outcomes into a comprehensive revised framework combining an information theory and a psychosocial theory of behavior. The results of 65 included studies were synthesized using a qualitative thematic data analysis. The themes derived from the literature underwent a harmonization process that produced a comprehensive typology of OCHI outcomes. The revised conceptual framework specifies four individual and one organizational level of OCHI outcomes, while including factors such as consumers' information needs and four interdependent contextual factors. It contributes to theoretical knowledge about OCHI health outcomes, and informs future research, information assessment methods, and tools to help consumers find and use health information.

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Date

18.11.2014 13:22:03

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Date

22. 1.2011 14:16:14

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Date

22. 1.2016 11:25:47

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Abstract

User-generated content is one of the most interesting phenomena of current published media, as users are now able not only to consume, but also to produce content in a much faster and easier manner. However, such freedom also carries concerns about content quality. In this work, we propose an automatic framework to assess the quality of collaboratively generated content. Quality is addressed as a multidimensional concept, modeled as a combination of independent assessments, each regarding different quality dimensions. Accordingly, we adopt a machine-learning (ML)-based multiview approach to assess content quality. We perform a thorough analysis of our framework on two different domains: Questions and Answer Forums and Collaborative Encyclopedias. This allowed us to better understand when and how the proposed multiview approach is able to provide accurate quality assessments. Our main contributions are: (a) a general ML multiview framework that takes advantage of different views of quality indicators; (b) the improvement (up to 30%) in quality assessment over the best state-of-the-art baseline methods; (c) a thorough feature and view analysis regarding impact, informativeness, and correlation, based on two distinct domains.

Date

16.11.2017 13:04:22

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Abstract

Although people increasingly rely on online services to maintain their relationships, we know relatively little about what drives their use. To address this, I analyze data from a social networking site that started charging its users for an app that populates their e-mail address books with updated contact information. I find that purchase rates of the app were higher for users with large, structurally diverse networks - which contain several distinct social groups. Moreover, personal ties (i.e., family members and friends) increased purchase rates more than professional ties. I attribute the first effect to the difficulty of obtaining information about a large, diverse social network, which the app reduces, and the second effect to the regularity with which people use information about their personal ties.

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Abstract

Drawing on identity theory and privacy research, this article argues that the need for self-identity is a key factor affecting people's privacy behavior in social networking sites. I first unpack the mainstream, autonomy-centric discourse of privacy, and then present a research model that illustrates a possible new theorization of the relationship between self-identity and information privacy. An empirical study with Facebook users confirms the main hypotheses. In particular, the data show that the need for self-identity is positively related to privacy management behaviors, which in turn result in increased self-disclosure in online social networks. I subsequently argue that the so-called "privacy paradox" is not a paradox per se in the context of online social networking; rather, privacy concerns reflect the ideology of an autonomous self, whereas social construction of self-identity explains voluntary self-disclosure.

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Abstract

Recent research has involved identifying communities in networks. Traditional methods of community detection usually assume that the network's structural information is fully known, which is not the case in many practical networks. Moreover, most previous community detection algorithms do not differentiate multiple relationships between objects or persons in the real world. In this article, we propose a new approach that utilizes social interaction data (e.g., users' posts on Facebook) to address the community detection problem in Facebook and to find the multiple social groups of a Facebook user. Some advantages to our approach are (a) it does not depend on structural information, (b) it differentiates the various relationships that exist among friends, and (c) it can discover a target user's multiple communities. In the experiment, we detect the community distribution of Facebook users using the proposed method. The experiment shows that our method can achieve the result of having the average scores of Total-Community-Purity and Total-Cluster-Purity both at approximately 0.8.

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Date

19. 1.2019 17:42:22

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Source

Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker

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Date

22. 1.2016 14:37:14

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Abstract

Today, people perform many types of tasks on the web, including those that require multiple web sessions. In this article, we build on research about web tasks and present an in-depth evaluation of the types of tasks people perform on the web over multiple web sessions. Multisession web tasks are goal-based tasks that often contain subtasks requiring more than one web session to complete. We will detail the results of two longitudinal studies that we conducted to explore this topic. The first study was a weeklong web-diary study where participants self-reported information on their own multisession tasks. The second study was a monthlong field study where participants used a customized version of Firefox, which logged their interactions for both their own multisession tasks and their other web activity. The results from both studies found that people perform eight different types of multisession tasks, that these tasks often consist of several subtasks, that these lasted different lengths of time, and that users have unique strategies to help continue the tasks which involved a variety of web and browser tools such as search engines and bookmarks and external applications such as Notepad or Word. Using the results from these studies, we have suggested three guidelines for developers to consider when designing browser-tool features to help people perform these types of tasks: (a) to maintain a list of current multisession tasks, (b) to support multitasking, and (c) to manage task-related information between sessions.

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Abstract

Given the task of finding influencers (opinion makers) for a given domain in a social network, we investigate (a) what is the relative importance of domain and authority signals, (b) what is the most effective way of combining signals (voting, classification, learning to rank, etc.) and how best to model the vocabulary signal, and (c) how large is the gap between supervised and unsupervised methods and what are the practical consequences. Our best results on the RepLab dataset (which improves the state of the art) uses language models to learn the domain-specific vocabulary used by influencers and combines domain and authority models using a Learning to Rank algorithm. Our experiments show that (a) both authority and domain evidence can be trained from the vocabulary of influencers; (b) once the language of influencers is modeled as a likelihood signal, further supervised learning and additional network-based signals only provide marginal improvements; and (c) the availability of training data sets is crucial to obtain competitive results in the task. Our most remarkable finding is that influencers do use a distinctive vocabulary, which is a more reliable signal than nontextual network indicators such as the number of followers, retweets, and so on.