[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-1-en-105":3,"doc-seo-195223-105":53,"doc-detail-195223-en":126},{"code":4,"msg":5,"data":6},0,"success",[7,14,19,24,29,34,39,44,49],{"id":8,"doc_module":9,"doc_module_name":10,"category_name":11,"show_sort_weight":12,"slug":13},11,1,"Template","Presentations",90,"presentations",{"id":15,"doc_module":9,"doc_module_name":10,"category_name":16,"show_sort_weight":17,"slug":18},12,"Resumes",80,"resumes",{"id":20,"doc_module":9,"doc_module_name":10,"category_name":21,"show_sort_weight":22,"slug":23},14,"Invoices",70,"invoices",{"id":25,"doc_module":9,"doc_module_name":10,"category_name":26,"show_sort_weight":27,"slug":28},15,"Posters",60,"posters",{"id":30,"doc_module":9,"doc_module_name":10,"category_name":31,"show_sort_weight":32,"slug":33},16,"Social Media",50,"social-media",{"id":35,"doc_module":9,"doc_module_name":10,"category_name":36,"show_sort_weight":37,"slug":38},17,"Forms",40,"forms",{"id":40,"doc_module":9,"doc_module_name":10,"category_name":41,"show_sort_weight":42,"slug":43},18,"Letters",30,"letters",{"id":45,"doc_module":9,"doc_module_name":10,"category_name":46,"show_sort_weight":47,"slug":48},21,"Paper Templates",5,"papers-templates",{"id":50,"doc_module":9,"doc_module_name":10,"category_name":51,"show_sort_weight":4,"slug":52},158,"General","general-158",{"code":4,"msg":54,"data":55},"ok",{"site_id":56,"language":57,"slug":58,"title":59,"keywords":60,"description":61,"schema_data":62,"social_meta":119,"head_meta":121,"extra_data":123,"updated_unix":125},105,"en","icwsm-2013-geli-burst","ICWSM-2013-Geli-burst","","This document presents a study on detecting and classifying spam and non-spam reviews, focusing on a method called \"Geli-burst\". The study evaluates the performance of different classification algorithms, including k-means, and presents results in various tables and figures. The key metrics reported are precision, recall, F-score, and accuracy, applied to both general reviews and specifically \"burst\" reviews, which are assumed to be reviews posted in a short, concentrated period. The tables detail the performance of \"spammer\", \"non-spammer\", and \"mixed\" categories against these algorithms, with a particular focus on the \"With local\" approach showing impressive spam classification. The Kappa statistic is also provided to measure inter-rater agreement. The research appears to be a technical paper from the ICWSM 2013 conference, likely detailing a novel approach to review analysis and spam detection within online platforms, with an emphasis on computational methods and quantitative 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is the primary focus of this document?","Question",{"text":108,"@type":109},"The document focuses on detecting and classifying spam and non-spam reviews using a method called \"Geli-burst\" and evaluating different classification algorithms.","Answer",{"name":111,"@type":106,"acceptedAnswer":112},"What metrics are used to evaluate the performance of the classification methods?",{"text":113,"@type":109},"The document reports on precision, recall, F-score, and accuracy for various scenarios, and also includes the Kappa statistic for inter-rater agreement.",{"name":115,"@type":106,"acceptedAnswer":116},"Which approach showed the most effective spam classification according to the tables?",{"text":117,"@type":109},"The \"With local\" approach, particularly in the k-means clustering results, demonstrated the most effective spam classification, with a high number of correctly identified spam reviews and a low number of non-spam reviews.","https://schema.org",{"og:url":78,"og:type":120,"og:title":59,"og:site_name":91,"og:description":61},"article",{"robots":122,"canonical":78},"index,follow",{"doc_id":124,"site_id":56},195223,1790304240,{"code":4,"msg":5,"data":127},{"doc_id":124,"user_id":128,"nickname":88,"user_avatar":129,"doc_module":9,"category_id":50,"category_name":51,"doc_title":59,"doc_description":61,"doc_content":130,"file_id":131,"file_url":132,"file_type":133,"file_size":134,"view_count":76,"is_deleted":4,"is_public":9,"is_downloadable":9,"audit_status":9,"page_count":135,"language":136,"language_code":57,"site_id":56,"html_lang":57,"table_of_contents":137,"faqs":138,"seo_title":139,"seo_description":61,"update_tm":140,"read_time":79},13056712833777,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","|  | spammer | non-spammer | mixed |\n| --- | --- | --- | --- |\n| spammer | 0.4 | 0.25 | 0.35 |\n| non-spammer | 0.25 | 0.4 | 0.35 |\n| mixed | 1/3 | 1/3 | 1/3 |\n\n\n|  | precision | recall | F-score | accuracy |\n| --- | --- | --- | --- | --- |\n| all reviews | 53.2% | 66.0% | 58.9% | 53.9% |\n| burst reviews | 55.9% | 71.4% | 62.7% | 57.5% |\n\n\n|  | Precision | recall | F-score | accuracy |\n| --- | --- | --- | --- | --- |\n| all reviews | 57.3% | 59.3% | 58.3% | 57.5% |\n| burst reviews | 61.2% | 55.3% | 57.9% | 59.6% |\n\n\n|  | precision | recall | F-score | accuracy |\n| --- | --- | --- | --- | --- |\n| all reviews | 77.8% | 61.5% | 68.7% | 71.2% |\n| burst reviews | 83.7% | 68.6% | 75.4% | 77.6% |\n\n\n|  | k-means |  | Without local |  | With local |  |\n| --- | --- | --- | --- | --- | --- | --- |\n|  | spam | Non\u003Cbr>spam | spam | Non\u003Cbr>spam | spam | Non\u003Cbr>spam |\n| J1 | 16 | 14 | 29 | 5 | 41 | 2 |\n| J2 | 13 | 9 | 27 | 4 | 36 | 0 |\n| J3 | 14 | 12 | 28 | 3 | 37 | 1 |\n| Avg | 14.33 | 11.67 | 28 | 4 | 38 | 1 |\n| Kappa | 0.72 | 0.70 | 0.69 | 0.78 | 0.71 | 0.84 |","cbCaijSAVLhbVApb","https://ap.wps.com/l/cbCaijSAVLhbVApb","pdf",382711,10,"English","# Results\n## Table 1: Spam vs. Non-Spam Classification by Algorithm\n## Table 2: Performance Metrics for All Reviews and Burst Reviews (Scenario 1)\n## Table 3: Performance Metrics for All Reviews and Burst Reviews (Scenario 2)\n## Table 4: Performance Metrics for All Reviews and Burst Reviews (Scenario 3)\n## Table 5: K-Means Clustering Results with Different Approaches","[{\"question\":\"What is the primary focus of this document?\",\"answer\":\"The document focuses on detecting and classifying spam and non-spam reviews using a method called \\\"Geli-burst\\\" and evaluating different classification algorithms.\"},{\"question\":\"What metrics are used to evaluate the performance of the classification methods?\",\"answer\":\"The document reports on precision, recall, F-score, and accuracy for various scenarios, and also includes the Kappa statistic for inter-rater agreement.\"},{\"question\":\"Which approach showed the most effective spam classification according to the tables?\",\"answer\":\"The \\\"With local\\\" approach, particularly in the k-means clustering results, demonstrated the most effective spam classification, with a high number of correctly identified spam reviews and a low number of non-spam reviews.\"}]","ICWSM-2013-Geli-burst | PDF",1788446474]