{"id":328298,"date":"2016-11-28T17:14:48","date_gmt":"2016-11-29T01:14:48","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=328298"},"modified":"2018-10-16T20:25:32","modified_gmt":"2018-10-17T03:25:32","slug":"exploring-topical-lead-lag-across-corpora","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/exploring-topical-lead-lag-across-corpora\/","title":{"rendered":"Exploring Topical Lead-Lag across Corpora"},"content":{"rendered":"
Identifying which text corpus leads in the context of a topic presents a great challenge of considerable interest to researchers. Recent research into lead-lag analysis has mainly focused on estimating the overall leads and lags between two corpora. However, real-world applications have a dire need to understand lead-lag patterns both globally and locally. In this paper, we introduce TextPioneer, an interactive visual analytics tool for investigating lead-lag across corpora from the global level to the local level. In particular, we extend an existing lead-lag analysis approach to derive two-level results. To convey multiple perspectives of the results, we have designed two visualizations, a novel hybrid tree visualization that couples a radial space-filling tree with a node-link diagram and a twisted-ladder-like visualization. We have applied our method to several corpora and the evaluation shows promise, especially in support of text comparison at different levels of detail.<\/p>\n","protected":false},"excerpt":{"rendered":"
Identifying which text corpus leads in the context of a topic presents a great challenge of considerable interest to researchers. Recent research into lead-lag analysis has mainly focused on estimating the overall leads and lags between two corpora. However, real-world applications have a dire need to understand lead-lag patterns both globally and locally. In this 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Transactions on Knowledge and Data Engineering","msr_volume":"27","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"1","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"","msr_publicationurl":"http:\/\/ieeexplore.ieee.org\/document\/6816054\/?reload=true","msr_doi":"10.1109\/TKDE.2014.2324581","msr_publication_uploader":[{"type":"url","title":"http:\/\/ieeexplore.ieee.org\/document\/6816054\/?reload=true","viewUrl":false,"id":false,"label_id":0},{"type":"doi","title":"10.1109\/TKDE.2014.2324581","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":0,"url":"http:\/\/ieeexplore.ieee.org\/document\/6816054\/?reload=true"}],"msr-author-ordering":[{"type":"text","value":"Shixia 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