3,238 research outputs found

    Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization

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    Geographically annotated social media is extremely valuable for modern information retrieval. However, when researchers can only access publicly-visible data, one quickly finds that social media users rarely publish location information. In this work, we provide a method which can geolocate the overwhelming majority of active Twitter users, independent of their location sharing preferences, using only publicly-visible Twitter data. Our method infers an unknown user's location by examining their friend's locations. We frame the geotagging problem as an optimization over a social network with a total variation-based objective and provide a scalable and distributed algorithm for its solution. Furthermore, we show how a robust estimate of the geographic dispersion of each user's ego network can be used as a per-user accuracy measure which is effective at removing outlying errors. Leave-many-out evaluation shows that our method is able to infer location for 101,846,236 Twitter users at a median error of 6.38 km, allowing us to geotag over 80\% of public tweets.Comment: 9 pages, 8 figures, accepted to IEEE BigData 2014, Compton, Ryan, David Jurgens, and David Allen. "Geotagging one hundred million twitter accounts with total variation minimization." Big Data (Big Data), 2014 IEEE International Conference on. IEEE, 201

    GeoIntelligence: Data Mining Locational Social Media Content for Profiling and Information Gathering

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    The current social media landscape has resulted in a situation where people are encouraged to share a greater amount of information about their day-to-day lives than ever before. In this environment a large amount of personal data is disclosed in a public forum with little to no regard for the potential privacy impacts. This paper focuses on the presence of geographic data within images, metadata and individual postings. The GeoIntelligence project aims to aggregate this information to educate users on the possible implications of the utilisation of these services as well as providing service to law enforcement and business. This paper demonstrates the ability to profile users on an individual and group basis from data posted openly to social networking services

    Using Contexts and Constraints for Improved Geotagging of Human Trafficking Webpages

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    Extracting geographical tags from webpages is a well-motivated application in many domains. In illicit domains with unusual language models, like human trafficking, extracting geotags with both high precision and recall is a challenging problem. In this paper, we describe a geotag extraction framework in which context, constraints and the openly available Geonames knowledge base work in tandem in an Integer Linear Programming (ILP) model to achieve good performance. In preliminary empirical investigations, the framework improves precision by 28.57% and F-measure by 36.9% on a difficult human trafficking geotagging task compared to a machine learning-based baseline. The method is already being integrated into an existing knowledge base construction system widely used by US law enforcement agencies to combat human trafficking.Comment: 6 pages, GeoRich 2017 workshop at ACM SIGMOD conferenc

    PENGEMBANGAN MODEL PEMBELAJARAN SERVICE LEARNING BERBANTUAN WEB BASED GEOTAGGING UNTUK MENINGKATKAN EFEKTIVITAS BLENDED LEARNING

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    Tujuan penelitian yaitu mengembangkan model pembelajaran service learning berbantuan web based geotagging untuk meningkatkan efektivitas blended learning pada bimbel yang di kelola mahasiswa, atau guru. Penelitian menggunakan metode R&D dilaksanakan empat tahap, yaitu studi pendahuluan, perencanaan, pengembangan dan uji coba. Validasi pakar dinyatakan valid dengan skor rata-rata ≥ 4,0. Ujicoba terbatas menujukan hasil mahasiswa dan guru bimbel dapat membuat vendor dengan model service learning berbantuan web based geotagging yang di kembangkan. Namun, perlu ada perbaikan dalam keterbacaan mengakses guru terdekat, perbaikan ini selanjutnya dilakukan revisi. Ujicoba lapangan bersama mitra menujukkan hasil revisi pada uji coba terbatas yang sudah di perbaiki, selanjutnya di terapkan kepada mitra SMK Pasundan 3 Kota Cimahi meliputi mahasiswa dan guru bimbel. Menujukkan efektivitas model service learning berbantuan web based geotagging di kembangkan  dilihat dari ketercapaian indikator yaitu pembelajaran online dan offline pada sistem web based geotagging diperoleh rerata 4,00, managerial tugas pada blended learning dengan rerata 3,78 dan model service learning berbantuan web based geotagging diperoleh rerata sebesar 3,98. Besarnya pengaruh penerapan model service learning berbantuan web based geotagging untukk meningkatkan efektifitas pembelajaran blended learning sebesar 17%. Hasil akhir menunjukkan bahwa model service learning berbantuan web based geotagging yang dikembangkan memenuhi kriteria valid, efektif dan praktis.Tujuan penelitian yaitu mengembangkan model pembelajaran service learning berbantuan web based geotagging untuk meningkatkan efektivitas blended learning pada bimbel yang di kelola mahasiswa, atau guru. Penelitian menggunakan metode R&D dilaksanakan empat tahap, yaitu studi pendahuluan, perencanaan, pengembangan dan uji coba. Validasi pakar dinyatakan valid dengan skor rata-rata ≥ 4,0. Ujicoba terbatas menujukan hasil mahasiswa dan guru bimbel dapat membuat vendor dengan model service learning berbantuan web based geotagging yang di kembangkan. Namun, perlu ada perbaikan dalam keterbacaan mengakses guru terdekat, perbaikan ini selanjutnya dilakukan revisi. Ujicoba lapangan bersama mitra menujukkan hasil revisi pada uji coba terbatas yang sudah di perbaiki, selanjutnya di terapkan kepada mitra SMK Pasundan 3 Kota Cimahi meliputi mahasiswa dan guru bimbel. Menujukkan efektivitas model service learning berbantuan web based geotagging di kembangkan  dilihat dari ketercapaian indikator yaitu pembelajaran online dan offline pada sistem web based geotagging diperoleh rerata 4,00, managerial tugas pada blended learning dengan rerata 3,78 dan model service learning berbantuan web based geotagging diperoleh rerata sebesar 3,98. Besarnya pengaruh penerapan model service learning berbantuan web based geotagging untukk meningkatkan efektifitas pembelajaran blended learning sebesar 17%. Hasil akhir menunjukkan bahwa model service learning berbantuan web based geotagging yang dikembangkan memenuhi kriteria valid, efektif dan praktis

    Automatic tagging and geotagging in video collections and communities

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    Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

    Automatic Geotagging of Russian Web Sites

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    The poster describes a fast, simple, yet accurate method to associate large amounts of web resources stored in a search engine database with geographic locations. The method uses location-by-IP data, domain names, and content-related features: ZIP and area codes. The novelty of the approach lies in building location-by-IP database by using continuous IP blocks method. Another contribution is domain name analysis. The method uses search engine infrastructure and makes it possible to effectively associate large amounts of search engine data with geography on a regular basis. Experiments ran on Yandex search engine index; evaluation has proved the efficacy of the approach.ACM Special Interest Group on Hypertext, Hypermedia, and We
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