{"id":166370,"date":"2019-01-17T09:56:43","date_gmt":"2019-01-17T17:56:43","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/discussion-graphs-putting-social-media-analysis-in-context\/"},"modified":"2019-01-17T09:56:43","modified_gmt":"2019-01-17T17:56:43","slug":"discussion-graphs-putting-social-media-analysis-in-context","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/discussion-graphs-putting-social-media-analysis-in-context\/","title":{"rendered":"Discussion Graphs: Putting Social Media Analysis in Context"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Much research has focused on studying complex phenomena through its reflection in social media, from drawing neighborhood boundaries to inferring relationships between medicines and diseases. While it is generally recognized in the social sciences that such studies should be conditioned on gender, time and other confounding factors, few of the studies that attempt to extract information from social media actually condition on such factors.<\/p>\n<p>In this paper, we present a simple framework for specifying and implementing common social media analyses that makes it trivial to inspect and condition on such contextual information. Our data model, discussion graphs, capture both the structural features of relationships inferred from social media as well as the context of the discussions from which they are derived, such as who is participating in the discussions, when and where the discussions are occurring, and what else is being discussed in conjunction. We implement our framework in a tool called Q, and present case studies on its use. In particular, we show how analyses of neighborhoods and their boundaries based on geo-located social media data can have drastically varying results when conditioned on gender and time (day\/night and weekend\/weekday).<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Much research has focused on studying complex phenomena through its reflection in social media, from drawing neighborhood boundaries to inferring relationships between medicines and diseases. While it is generally recognized in the social sciences that such studies should be conditioned on gender, time and other confounding factors, few of the studies that attempt to extract [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13545],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-166370","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-language-technologies","msr-locale-en_us"],"msr_publishername":"AAAI","msr_edition":"Intl. Conf. on Weblogs and Social Media (ICWSM-14)","msr_affiliation":"","msr_published_date":"2014-06-02","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","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":"204837","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"kiciman-icwsm-discussiongraphs_final.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/kiciman-icwsm-discussiongraphs_final.pdf","id":204837,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":204837,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/kiciman-icwsm-discussiongraphs_final.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"emrek","user_id":31739,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=emrek"},{"type":"user_nicename","value":"counts","user_id":31471,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=counts"},{"type":"user_nicename","value":"mgamon","user_id":32888,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mgamon"},{"type":"user_nicename","value":"mudechou","user_id":33030,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=mudechou"},{"type":"user_nicename","value":"thiesson","user_id":34026,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=thiesson"}],"msr_impact_theme":[],"msr_research_lab":[],"msr_event":[],"msr_group":[144672,144736,493619],"msr_project":[212075,171346],"publication":[],"video":[],"download":[],"msr_publication_type":"inproceedings","related_content":{"projects":[{"ID":212075,"post_title":"DSoAP - Distributed Social Analytics Platform","post_name":"dsoap-distributed-social-analytics-platform","post_type":"msr-project","post_date":"2015-06-01 09:00:53","post_modified":"2023-03-30 12:01:13","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/dsoap-distributed-social-analytics-platform\/","post_excerpt":"The Distributed Social Analytics Platform (DSoAP) project is focused on the \u201cHuge Data\u201d problem in social policy research caused by the breadth of data involved. Using aggregate social media data to investigate and validate social issues (such as employment, health and fiscal policy) requires analyzing many months or years of data. DSoAP is applying intelligent compaction, pre-indexing and distribution of data across a server cluster to achieve responsive query times for online data exploration. Twitter&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/212075"}]}},{"ID":171346,"post_title":"Discussion Graph Tool","post_name":"discussion-graph-tool","post_type":"msr-project","post_date":"2014-04-25 23:43:54","post_modified":"2020-03-13 17:22:16","post_status":"publish","permalink":"https:\/\/www.microsoft.com\/en-us\/research\/project\/discussion-graph-tool\/","post_excerpt":"Discussion Graph Tool (DGT) is an easy-to-use analysis tool that provides a domain-speci\ufb01c language extracting co-occurrence relationships from social media and automates the tasks of tracking the context of relationships and other best practices. DGT provides a single-machine implementation, and also generates map-reduce-like programs for distributed, scalable analyses. DGT simplifies social media analysis by making it easy to extract high-level features and co-occurrence relationships from raw data. With just 3-4 simple lines of script, you&hellip;","_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/171346"}]}}]},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/166370"}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-research-item"}],"version-history":[{"count":3,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/166370\/revisions"}],"predecessor-version":[{"id":562485,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-item\/166370\/revisions\/562485"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=166370"}],"wp:term":[{"taxonomy":"msr-content-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-content-type?post=166370"},{"taxonomy":"msr-research-highlight","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-research-highlight?post=166370"},{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=166370"},{"taxonomy":"msr-publication-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-publication-type?post=166370"},{"taxonomy":"msr-product-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-product-type?post=166370"},{"taxonomy":"msr-focus-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-focus-area?post=166370"},{"taxonomy":"msr-platform","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-platform?post=166370"},{"taxonomy":"msr-download-source","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-download-source?post=166370"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=166370"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=166370"},{"taxonomy":"msr-field-of-study","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-field-of-study?post=166370"},{"taxonomy":"msr-conference","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-conference?post=166370"},{"taxonomy":"msr-journal","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-journal?post=166370"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=166370"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=166370"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}