3,238 research outputs found
Geotagging One Hundred Million Twitter Accounts with Total Variation Minimization
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
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
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
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
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
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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