1,498 research outputs found

    Investigating Churn in Physical Activity Challenges: Evidence from a U.S. Online Social Network

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    Physical activities have been found to be positively contagious, as active exercisers tend to motivate their friends to do more exercise. However, it is not clearly understood if inactive exercising behaviors are also socially contagious. As insufficient physical activity is a huge threat to people's health, understanding the potential negative contagion in physical activities is crucial. We approach this problem by studying the effect of individuals' churn of the online physical activity challenges relying on the physical activity and a large social network data from a renowned U.S. fitness platform. The underexplored online physical activity challenges provide a natural setup to measure churn and opportunities to study the contagion heterogeneities. Consistent with previous findings, we confirm that physical activity churn is socially contagious. Interestingly, unlike the inside-out positive contagion, our analyses reveal that the contagion of churn happens outside-in on the social network. Implications of such findings are discussed

    Human dynamics in the age of big data: a theory-data-driven approach

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    The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

    Privately Waiting – A Usability Analysis of the Tor Anonymity Network

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    As the Internet is increasingly absorbing information from the real world it becomes more important to prevent unauthorized collection and abuse of personalized information. At the same time, democratic societies should establish an environment helping not only their own people but also people who face repressive censorship to access public information without being identified or traced. Internet anonymization tools such as Tor offer functionalities to meet this demand. In practice, anonymization of Internet access can only be achieved by accepting higher latency, i.e., a longer waiting time before a Web site is displayed in the browser, and therefore reducing its usability significantly. Since many users may not be willing to accept this loss of usability, they may refrain from or stop using Tor – at the same time decreasing the anonymity of other users, which depends on shared resources in the Tor user community. In this paper1, we quantify the loss of usability by measuring the additional latency of the Tor software and combine our measurements with metrics of the existing Web usability and performance literature. Our findings indicate that there is still a major usability gap induced by Tor, leading to its possible disuse accompanied by a higher risk exposure of Internet users

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

    Enhanced web-based summary generation for search.

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    After a user types in a search query on a major search engine, they are presented with a number of search results. Each search result is made up of a title, brief text summary and a URL. It is then the user\u27s job to select documents for further review. Our research aims to improve the accuracy of users selecting relevant documents by improving the way these web pages are summarized. Improvements in accuracy will lead to time improvements and user experience improvements. We propose ReClose, a system for generating web document summaries. ReClose generates summary content through combining summarization techniques from query-biased and query-independent summary generation. Query-biased summaries generally provide query terms in context. Query-independent summaries focus on summarizing documents as a whole. Combining these summary techniques led to a 10% improvement in user decision making over Google generated summaries. Color-coded ReClose summaries provide keyword usage depth at a glance and also alert users to topic departures. Color-coding further enhanced ReClose results and led to a 20% improvement in user decision making over Google generated summaries. Many online documents include structure and multimedia of various forms such as tables, lists, forms and images. We propose to include this structure in web page summaries. We found that the expert user was insignificantly slowed in decision making while the majority of average users made decisions more quickly using summaries including structure without any decrease in decision accuracy. We additionally extended ReClose for use in summarizing large numbers of tweets in tracking flu outbreaks in social media. The resulting summaries have variable length and are effective at summarizing flu related trends. Users of the system obtained an accuracy of 0.86 labeling multi-tweet summaries. This showed that the basis of ReClose is effective outside of web documents and that variable length summaries can be more effective than fixed length. Overall the ReClose system provides unique summaries that contain more informative content than current search engines produce, highlight the results in a more meaningful way, and add structure when meaningful. The applications of ReClose extend far beyond search and have been demonstrated in summarizing pools of tweets

    Exploring Media Convergence: Evidence from Italy

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    The evolution of media and devices is enabling the ubiquitous and multi-device access to media and information, so that a media mutual contamination is in play. New forms of user interactions with media, in which different devices are used simultaneously in different contexts, have emerged. These new interactions are significantly impacting on users’ attitudes towards the media and their way of searching and generating content. Such a change, called “media convergence”, has a strong potential impact on marketing and communication processes, but as yet has not been deeply analysed in the literature. This paper presents the outcomes of several studies aimed at exploring media convergence on the demand-side to advance possible implications for marketers and managers

    Social group discovery using using co-location traces

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    Social information can be used to enhance existing applications and services or can be utilized to devise entirely new applications. Examples of such applications include recommendation systems, peer-to-peer networks, opportunistic data dissemination in ad hoc networks, or mobile friend finder. Social information can be collected from either online or mobile sources. This thesis focuses on identifying social groups based on data collected from mobile phones. These data can be either location or co-location traces. Unfortunately, location traces require a localization system for every mobile device, and users are reluctant to share absolute location due to privacy concerns. On the other hand, co- location can be collected using the embedded Bluetooth interface, present on almost all phones, and alleviates the privacy concerns as it does not collect user location. Existing graph algorithms, such as K-Clique and WNA, applied on co-location traces achieve low group detection accuracy because they focus on pair-wise ties, which cannot tell if multiple users spent time together simultaneously or how often they met. This thesis proposes the Group Discovery using Co-location (GDC) algorithm, which leverages the meeting frequency and meeting duration to accurately detect social groups. These parameters allow us to compare, categorize, and rank the groups discovered by GDC. This algorithm is tested and validated on data collected from 141 active users who carried mobile phones on our campus over the duration of one month. GDC received ratings that were 30% better than the K-Clique algorithm

    Quality of social housing in Metropolitan Lima

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    ABSTRACT This article studies the quality and the satisfaction variables of the current situation as the housing in Metropolitan Lima, the Peruvian city with the highest population number, the largest number of homes from self-construction and, the lack of land to build new houses. Two instruments were designed to measure each one of the variables, and research was required to have a quantitative, transversal, and non-experimental approach. This study verified the direct relationship that exists between the variables and had determined the current state of each one. Besides, they will rank the dimensions of satisfaction for housing quality
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