3,961 research outputs found

    Negative Statements Considered Useful

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    Knowledge bases (KBs), pragmatic collections of knowledge about notable entities, are an important asset in applications such as search, question answering and dialogue. Rooted in a long tradition in knowledge representation, all popular KBs only store positive information, while they abstain from taking any stance towards statements not contained in them. In this paper, we make the case for explicitly stating interesting statements which are not true. Negative statements would be important to overcome current limitations of question answering, yet due to their potential abundance, any effort towards compiling them needs a tight coupling with ranking. We introduce two approaches towards compiling negative statements. (i) In peer-based statistical inferences, we compare entities with highly related entities in order to derive potential negative statements, which we then rank using supervised and unsupervised features. (ii) In query-log-based text extraction, we use a pattern-based approach for harvesting search engine query logs. Experimental results show that both approaches hold promising and complementary potential. Along with this paper, we publish the first datasets on interesting negative information, containing over 1.1M statements for 100K popular Wikidata entities

    Events and Controversies: Influences of a Shocking News Event on Information Seeking

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    It has been suggested that online search and retrieval contributes to the intellectual isolation of users within their preexisting ideologies, where people's prior views are strengthened and alternative viewpoints are infrequently encountered. This so-called "filter bubble" phenomenon has been called out as especially detrimental when it comes to dialog among people on controversial, emotionally charged topics, such as the labeling of genetically modified food, the right to bear arms, the death penalty, and online privacy. We seek to identify and study information-seeking behavior and access to alternative versus reinforcing viewpoints following shocking, emotional, and large-scale news events. We choose for a case study to analyze search and browsing on gun control/rights, a strongly polarizing topic for both citizens and leaders of the United States. We study the period of time preceding and following a mass shooting to understand how its occurrence, follow-on discussions, and debate may have been linked to changes in the patterns of searching and browsing. We employ information-theoretic measures to quantify the diversity of Web domains of interest to users and understand the browsing patterns of users. We use these measures to characterize the influence of news events on these web search and browsing patterns

    GAUGING PUBLIC INTEREST FROM SERVER LOGS, SURVEYS AND INLINKS A Multi-Method Approach to Analyze News Websites

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    As the World Wide Web (the Web) has turned into a full-fledged medium to disseminate news, it is very important for journalism and information science researchers to investigate how Web users access online news reports and how to interpret such usage patterns. This doctoral thesis collected and analyzed Web server log statistics, online surveys results, online reprints of the top 50 news reports, as well as external inlinks data of a leading comprehensive online newspaper (the People\u27s Daily Online) in China, one of the biggest Web/information markets in today\u27s world. The aim of the thesis was to explore various methods to gauge the public interest from a Webometrics perspective. A total of 129 days of Web server log statistics, including the top 50 Chinese and English news stories with the highest daily pageview numbers, the comments attracted by these news items and the emailed frequencies of the same stories were collected from October 2007 to September 2008. These top 50 news items’positions on the Chinese and English homepages and the top 50 queries submitted to the website search engine of the People’s Daily Online were also retrieved. Results of the two online surveys launched in March 2008 and March 2009 were collected after their respective closing dates. The external inlinks to the People’s Daily Online were retrieved by Yahoo! (Chinese and English versions), and the online reprints were retrieved by Google. Besides the general usage patterns identified from the top 50 news stories, this study, by conducting statistical tests on the data sets, also reveals the following findings. First, the editors’ choices and the readers’ favorites do not always match each other; thus content of news title is more important than its homepage position in attracting online visits. Second, the Chinese and English readers’ interests in the same events are different. Third, the pageview numbers and comments posted to the news items reflect the unfavorable attitudes of the Chinese people toward the United States and Japan, which might offer us a method to investigate the public interest in some other issues or nations after necessary modifications. More importantly, some publicly available data, such as the comments posted to the news stories and online survey results, further show that the pageview measure does reflect readers’ interests/needs truthfully, as proved by the strong correlations between the top news reports and relevant top queries. The external ininks to the news websites and the online reprints of the top news items help us examine readers\u27 interests from other perspectives, as well as establish online profiles of the news websites. Such publicly accessible information could be an alternative data source for researchers to study readers\u27 interests when the Web server log data are not available. This doctoral thesis not only shows the usefulness of Web server log statistics, survey results, and other publicly accessible data in studying Web user’s information needs, but also offers practical suggestions for online news sites to improve their contents and homepage designs. However, no single method can draw a complete picture of the online news readers’ interests. The above mentioned research methodologies should be employed together, in order to make more comprehensive conclusions. Future research is especially needed to investigate the continuously rapid growth of the “Mobile News Readers,” which poses both challenges and opportunities to the press industry in the 21st century

    GAUGING PUBLIC INTEREST FROM SERVER LOGS, SURVEYS AND INLINKS

    Get PDF
    As the World Wide Web (the Web) has turned into a full-fledged medium to disseminate news, it is very important for journalism and information science researchers to investigate how Web users access online news reports and how to interpret such usage patterns. This doctoral thesis collected and analyzed Web server log statistics, online surveys results, online reprints of the top 50 news reports, as well as external inlinks data of a leading comprehensive online newspaper (the People’s Daily Online) in China, one of the biggest Web/information markets in today’s world. The aim of the thesis was to explore various methods to gauge the public interest from a Webometrics perspective. A total of 129 days of Web server log statistics, including the top 50 Chinese and English news stories with the highest daily pageview numbers, the comments attracted by these news items and the emailed frequencies of the same stories were collected from October 2007 to September 2008. These top 50 news items’positions on the Chinese and English homepages and the top 50 queries submitted to the website search engine of the People’s Daily Online were also retrieved. Results of the two online surveys launched in March 2008 and March 2009 were collected after their respective closing dates. The external inlinks to the People’s Daily Online were retrieved by Yahoo! (Chinese and English versions), and the online reprints were retrieved by Google. Besides the general usage patterns identified from the top 50 news stories, this study, by conducting statistical tests on the data sets, also reveals the following findings. First, the editors’ choices and the readers’ favorites do not always match each other; thus content of news title is more important than its homepage position in attracting online visits. Second, the Chinese and English readers’ interests in the same events are different. Third, the pageview numbers and comments posted to the news items reflect the unfavorable attitudes of the Chinese people toward the United States and Japan, which might offer us a method to investigate the public interest in some other issues or nations after necessary modifications. More importantly, some publicly available data, such as the comments posted to the news stories and online survey results, further show that the pageview measure does reflect readers’ interests/needs truthfully, as proved by the strong correlations between the top news reports and relevant top queries. The external ininks to the news websites and the online reprints of the top news items help us examine readers\u27 interests from other perspectives, as well as establish online profiles of the news websites. Such publicly accessible information could be an alternative data source for researchers to study readers\u27 interests when the Web server log data are not available. This doctoral thesis not only shows the usefulness of Web server log statistics, survey results, and other publicly accessible data in studying Web user’s information needs, but also offers practical suggestions for online news sites to improve their contents and homepage designs. However, no single method can draw a complete picture of the online news readers’ interests. The above mentioned research methodologies should be employed together, in order to make more comprehensive conclusions. Future research is especially needed to investigate the continuously rapid growth of the “Mobile News Readers,” which poses both challenges and opportunities to the press industry in the 21st century

    FogGIS: Fog Computing for Geospatial Big Data Analytics

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    Cloud Geographic Information Systems (GIS) has emerged as a tool for analysis, processing and transmission of geospatial data. The Fog computing is a paradigm where Fog devices help to increase throughput and reduce latency at the edge of the client. This paper developed a Fog-based framework named Fog GIS for mining analytics from geospatial data. We built a prototype using Intel Edison, an embedded microprocessor. We validated the FogGIS by doing preliminary analysis. including compression, and overlay analysis. Results showed that Fog computing hold a great promise for analysis of geospatial data. We used several open source compression techniques for reducing the transmission to the cloud.Comment: 6 pages, 4 figures, 1 table, 3rd IEEE Uttar Pradesh Section International Conference on Electrical, Computer and Electronics (09-11 December, 2016) Indian Institute of Technology (Banaras Hindu University) Varanasi, Indi

    Global disease monitoring and forecasting with Wikipedia

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    Infectious disease is a leading threat to public health, economic stability, and other key social structures. Efforts to mitigate these impacts depend on accurate and timely monitoring to measure the risk and progress of disease. Traditional, biologically-focused monitoring techniques are accurate but costly and slow; in response, new techniques based on social internet data such as social media and search queries are emerging. These efforts are promising, but important challenges in the areas of scientific peer review, breadth of diseases and countries, and forecasting hamper their operational usefulness. We examine a freely available, open data source for this use: access logs from the online encyclopedia Wikipedia. Using linear models, language as a proxy for location, and a systematic yet simple article selection procedure, we tested 14 location-disease combinations and demonstrate that these data feasibly support an approach that overcomes these challenges. Specifically, our proof-of-concept yields models with r2r^2 up to 0.92, forecasting value up to the 28 days tested, and several pairs of models similar enough to suggest that transferring models from one location to another without re-training is feasible. Based on these preliminary results, we close with a research agenda designed to overcome these challenges and produce a disease monitoring and forecasting system that is significantly more effective, robust, and globally comprehensive than the current state of the art.Comment: 27 pages; 4 figures; 4 tables. Version 2: Cite McIver & Brownstein and adjust novelty claims accordingly; revise title; various revisions for clarit

    Recent Development in Information Science: Implications for Information Systems Research

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    Over past several decades, the management information systems (MIS) community has adopted theories, methodologies, philosophical bases, and assumptions from sister disciplines. This paper reports the changing nature of information science (IS) towards multi-disciplinarity and its development over the past decade. It also examines the contribution of informetrics to MIS research in delineating the intellectual structure of information systems, comparing cumulative research traditions, demonstrating theoretical differences between competing approaches, tracing a paradigm shift. Development in IS provides MIS researchers with ample opportunities for cross-disciplinary research, new research tools, new theories to understand information systems phenomena, etc
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