10,371 research outputs found

    Robust Estimation of Google Counts for Social Network Extraction

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    Abstract Various studies within NLP and Semantic Web use the so-called Google count, which is the hit count on a query returned by a search engine (not only Google). However, sometimes the Google count is unreliable, especially when the count is large, or when advanced operators such as OR and NOT are used. In this paper, we propose a novel algorithm that estimates the Google count robustly. It (i) uses the co-occurrence of terms as evidence to estimate the occurrence of a given word, and (ii) integrates multiple evidence for robust estimation. We evaluated our algorithm for more than 2000 queries on three datasets using Google, Yahoo! and MSN search engine. Our algorithm also provides estimate counts for any classifier that judges a web page as positive or negative. Consequently, we can estimate the number of documents with included references of a particular person (among namesakes) on the entire web

    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
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