{"id":739651,"date":"2021-04-12T13:40:20","date_gmt":"2021-04-12T20:40:20","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-event&#038;p=739651"},"modified":"2025-08-06T11:51:29","modified_gmt":"2025-08-06T18:51:29","slug":"kdd-2021-truefact-workshop-making-a-credible-web-for-tomorrow","status":"publish","type":"msr-event","link":"https:\/\/www.microsoft.com\/en-us\/research\/event\/kdd-2021-truefact-workshop-making-a-credible-web-for-tomorrow\/","title":{"rendered":"KDD 2021 TrueFact Workshop: Making a Credible Web for Tomorrow"},"content":{"rendered":"\n\n<p>Held virtually, in conjunction with <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.kdd.org\/kdd2021\/\" target=\"_blank\" rel=\"noopener\">ACM SIGKDD 2021<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n<p>[<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/virtual.2021.kdd.org\/workshop_WS-33.html\">Join event here<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>]<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>The Third International Workshop on <em>Truth Discovery and Fact Checking: Making a Credible Web for Tomorrow<\/em> will provide a forum where researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in resolving conflicts, fact-checking and ascertaining credibility of claims.<\/p>\n<p>In recent times, the explosion of information from a variety of sources and cutting-edge techniques such as Deepfake have made it increasingly important to check the credibility and reliability of the data. Large volumes of data generated from diverse information channels like social media, online news outlets, and crowd-sourcing contribute valuable knowledge; however, this comes with additional challenges to ascertain the credibility of user-generated and machine-generated information, resolving conflicts among heterogeneous data sources, identifying misinformation, rumors and bias, etc.<\/p>\n<ul>\n<li>Given diverse information about an object (e.g., a natural language claim text, an entity, structured triples and social network context) from heterogeneous and multi-modal sources, how do we identify high quality and trustworthy information and information sources?<\/li>\n<li>How can we leverage Knowledge Bases and external evidence sources from the Web for reasoning, explaining, and validating claims?<\/li>\n<li>How can we generate human-interpretable explanations for the models\u2019 verdict?<\/li>\n<li>How can we design robust fake information (e.g., reviews and news) detection mechanisms to withstand adversarial generation strategies, as spammers and content generators are co-evolving with the advanced detectors?<\/li>\n<\/ul>\n<p>In order to answer these questions, this workshop encourages submissions to focus on big ideas \u2013 for resolving conflicts, fact-checking, ascertaining credibility of claims, explaining predictions from deep fake detectors, developing robust adversarial mechanisms for fake content detection, manipulation and safeguards, and making detection algorithms fair and unbiased to the involved participants \u2013 in heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks and knowledge graphs.<\/p>\n<h3>Organizers<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/submukhe\/\">Subhabrata Mukherjee<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Microsoft Research)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.cs.iastate.edu\/people\/qi-li\" target=\"_blank\" rel=\"noopener\">Qi Li<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Iowa State University)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.cse.lehigh.edu\/~sxie\/\" target=\"_blank\" rel=\"noopener\">Sihong Xie<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Lehigh University)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.cs.uic.edu\/~psyu\/\" target=\"_blank\" rel=\"noopener\">Philip S. Yu<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UIC)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/cse.buffalo.edu\/~jing\/\" target=\"_blank\" rel=\"noopener\">Jing Gao<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Purdue University)<\/li>\n<\/ul>\n<h3>Board Member<\/h3>\n<ul>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/hanj.cs.illinois.edu\/\" target=\"_blank\" rel=\"noopener\">Jiawei Han<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UIUC)<\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>Our workshop on truth discovery and fact-checking is motivated by the need for new research, tools and techniques to advance the field with the following focus areas:<\/p>\n<ul>\n<li><strong>Multi-modality<\/strong> \u2013 Modern data are multi-modal encompassing relational data, social networks, natural language text, structured logs, images, video, etc. One family of techniques geared towards a particular data source may not work for the other. One of the major focus areas of this workshop is data heterogeneity, multi modality, novel applications and data sources related to truth discovery, fact-checking, and rumor detection.<\/li>\n<li><strong>Interpretability<\/strong> \u2013 Another focus area is to explore tools and methods that can generate human-interpretable explanations as opposed to black box methods, as transparency and lack of explanations remain a concern for industry to readily deploy these techniques in practise. Robustness. The third focus of the workshop will be security and robustness of fake content detection under a known or unknown environment where adversaries can learn and commit arbitrary attack strategies for data poisoning or evading detectors.<\/li>\n<li><strong>Resource-efficient Learning<\/strong> \u2013\u00a0Finally, lack of labeled training data and scarce annotation resources remain a serious challenge for truth discovery and credibility analysis \u2013 making it difficult to train state-of-the-art methods like deep neural networks. In this context, we encourage submissions focusing on techniques like few-shot learning, weak supervision from user interactions and distant supervision from auxiliary knowledge sources for resource-efficient learning.<\/li>\n<\/ul>\n<h3>Topics of interest include, but are not limited to:<\/h3>\n<ul>\n<li class=\"\">Truth finding and discovery<\/li>\n<li>Fact-checking, rumor, and misinformation<\/li>\n<li>Credibility analysis and spam detection<\/li>\n<li>Fake reviews and reviewers<\/li>\n<li>Leveraging knowledge bases for reasoning, validating and explaining contentious claims<\/li>\n<li>Transparency, fairness, bias, privacy and ethics of information systems<\/li>\n<li>Emerging applications, novel data sources, and case studies<\/li>\n<li>Explainable and interpretable models<\/li>\n<li>Robustness detection under adversarial and unknown data poisoning and evasion attacks.<\/li>\n<li>Heterogeneous and multi-modal information including relational data, natural language text, search logs, images, video, etc.<\/li>\n<\/ul>\n<h3 id=\"submission-guidelines\">Submission guidelines<\/h3>\n<p>We invite submissions for original research papers both theory and application-oriented as well as submissions from the research track and applied data science track of the main KDD conference. We encourage the participants to submit papers on novel datasets and release them to advance the field. Papers must be submitted in PDF according to the ACM Proceedings Template <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.acm.org\/publications\/proceedings-template\" target=\"_blank\" rel=\"noopener\">https:\/\/www.acm.org\/publications\/proceedings-template<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> in a single-blind format (including author names and affiliations). We welcome both long papers (maximum length of 9 pages) and short papers (maximum length of 5 pages). The accepted papers will be published on the workshop\u2019s website, and will not be considered archival for resubmission purposes. Please submit your papers at the EasyChair submission link <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/easychair.org\/my\/conference?conf=truefact21\" target=\"_blank\" rel=\"noopener\">https:\/\/easychair.org\/my\/conference?conf=truefact21<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n<h3 id=\"important-dates\">Important dates<\/h3>\n<ul>\n<li>All deadlines are 11:59 PM Pacific Standard Time<\/li>\n<li>Workshop paper submissions due: June 1, 2021<\/li>\n<li>Workshop paper notifications: June 20, 2021<\/li>\n<li>Workshop date: August 14-18, 2021<\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>Please join the event in the following [<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/virtual.2021.kdd.org\/workshop_WS-33.html\">link<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>].<\/p>\n<table dir=\"ltr\" style=\"border-spacing: 0px;border-collapse: separate;width: 624px;height: 820px\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"100\" \/>\n<col width=\"100\" \/>\n<col width=\"100\" \/><\/colgroup>\n<tbody>\n<tr style=\"height: 74px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"Speaker\"}\"><strong>Speaker<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"Title\"}\"><strong>Title<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"Time (Pacific Standard Time \/ AM)\"}\">\n<div>\n<div><strong>Time (Pacific Standard Time \/ AM)<\/strong><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{\"1\":2,\"2\":\"Keynotes\"}\"><strong>Keynotes<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 74px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"Xiangliang Zhang (Notre Dame)\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.kaust.edu.sa\/en\/study\/faculty\/xiangliang-zhang\">Prof. Xiangliang Zhang (Notre Dame)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"Characterizing the Attackability of Machine Learning Models against Evasion Attacks\"}\">Characterizing the Attackability of Machine Learning Models against Evasion Attacks<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{\"1\":2,\"2\":\"8:00 - 8:40\"}\">8:00 &#8211; 8:40<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Stephan Guennemann\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.professoren.tum.de\/en\/guennemann-stephan\">Prof. Stephan Guennemann (TU Munich)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Adversarial Robustness of Graph Neural Networks\"}\">Adversarial Robustness of Graph Neural Networks<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"8:40 - 9:20\"}\">8:40 &#8211; 9:20<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Matthew Lease (UTA)\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease (University of Texas at Austin)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Explainable Fact Checking with a Human-in-the-loop\"}\">Explainable Fact Checking with a Human-in-the-loop<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"9:20 - 10:00\"}\">9:20 &#8211; 10:00<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{\"1\":2,\"2\":\"Contributed Talks\"}\"><strong>Contributed Talks<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Xi Zhang\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/mekltdcs.bupt.edu.cn\/\">Prof. Xi Zhang (BUPT)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Rumor detection on social media with multimodal information fusion\"}\">Rumor detection on social media with multimodal information fusion<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"10:00 - 10:15\"}\">10:00 &#8211; 10:15<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Yichuan Li\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/sites.google.com\/view\/yichuanli\/\">Yichuan Li (WPI)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Fact-Enhanced Synthetic News Generation (AAAI 2021)\"}\">Fact-Enhanced Synthetic News Generation (AAAI 2021)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"10:15 - 10:35\"}\">10:15 &#8211; 10:35<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Alex Braylan \"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.cs.utexas.edu\/~braylan\/\">Alex Braylan (UTA)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"Modeling and Aggregation of Complex Annotations via Annotation Distances\"}\">Modeling and Aggregation of Complex Annotations via Annotation Distances<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{\"1\":2,\"2\":\"10:35 - 10:55\"}\">10:35 &#8211; 10:55<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 480px;text-align: center;height: 26px\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{\"1\":2,\"2\":\"Break\"}\"><strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Break<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 26px\" data-sheets-value=\"{\"1\":2,\"2\":\"10:55 - 11:00\"}\">10:55 &#8211; 11:00<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 480px;height: 26px;text-align: center\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{\"1\":2,\"2\":\"Panel\"}\"><strong>Discussion Panel: Challenges in Fair Machine Learning<\/strong><\/p>\n<p>Panelists: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease <span class=\"sr-only\"> (opens in new tab)<\/span><\/a><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">(University of Texas at Austin)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/cse.buffalo.edu\/~jing\/index.html\">Prof. Jing Gao (Purdue University)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<p>Host: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/faculty.sites.iastate.edu\/qli\/\">Prof. Qi Li (Iowa State University)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 26px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"11:00 - 11:30\"}\">11:00 &#8211; 11:30<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;height: 26px;text-align: center\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{\"1\":2,\"2\":\"Workshop Papers\"}\"><strong>Workshop Papers<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 146px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"Xuan Wang\"}\">Xuan Wang, Vivian Hu, Xiangchen Song, Qi Li and\u00a0Jiawei Han<\/p>\n<p>(UIUC, IOWA)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"EvidenceMiner: Textual Evidence Mining in Scientific Literature\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/drive.google.com\/file\/d\/15vkfr7Wpkq8cXCnERHiArRPvL7yYL4kV\/view?usp=sharing\">EvidenceMiner: Textual Evidence Mining in Scientific Literature<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"11:30 - 11:45\"}\">11:30 &#8211; 11:45<\/td>\n<\/tr>\n<tr style=\"height: 146px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\">Tarik Arici, Kushal Kumar, Hayreddin Ceker, Anoop Saladi and Ismail Tutar<\/p>\n<p>(Amazon)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering\"}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/drive.google.com\/file\/d\/15xe1R9EkgH_CPHXVjV9A9mdtZkYNI6Hd\/view?usp=sharing\">Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{\"1\":2,\"2\":\"11:45 - 12:00\"}\">11:45 &#8211; 12:00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Third International TrueFact Workshop: Making a Credible Web for Tomorrow will provide a forum where researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in resolving conflicts, fact-checking and ascertaining credibility of claims. The workshop will be held virtually in conjunction with ACM SIGKDD 2021 on August 14-18th.<\/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":"","msr_startdate":"2021-08-15","msr_enddate":"2021-08-15","msr_location":"Virtual","msr_expirationdate":"","msr_event_recording_link":"","msr_event_link":"","msr_event_link_redirect":false,"msr_event_time":"","msr_hide_region":true,"msr_private_event":false,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[13556,13545,13555],"msr-region":[256048],"msr-event-type":[210063],"msr-video-type":[],"msr-locale":[268875],"msr-program-audience":[],"msr-post-option":[],"msr-impact-theme":[],"class_list":["post-739651","msr-event","type-msr-event","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-human-language-technologies","msr-research-area-search-information-retrieval","msr-region-global","msr-event-type-workshop","msr-locale-en_us"],"msr_about":"<!-- wp:msr\/event-details {\"title\":\"KDD 2021 TrueFact Workshop: Making a Credible Web for Tomorrow\",\"backgroundColor\":\"grey\",\"imageType\":\"full-bleed\"} \/-->\n\n<!-- wp:msr\/content-tabs --><!-- wp:msr\/content-tab {\"title\":\"About\"} --><!-- wp:freeform --><p>Held virtually, in conjunction with <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.kdd.org\/kdd2021\/\" target=\"_blank\" rel=\"noopener\">ACM SIGKDD 2021<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n<p>[<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/virtual.2021.kdd.org\/workshop_WS-33.html\">Join event here<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>]<span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<p>The Third International Workshop on <em>Truth Discovery and Fact Checking: Making a Credible Web for Tomorrow<\/em> will provide a forum where researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in resolving conflicts, fact-checking and ascertaining credibility of claims.<\/p>\n<p>In recent times, the explosion of information from a variety of sources and cutting-edge techniques such as Deepfake have made it increasingly important to check the credibility and reliability of the data. Large volumes of data generated from diverse information channels like social media, online news outlets, and crowd-sourcing contribute valuable knowledge; however, this comes with additional challenges to ascertain the credibility of user-generated and machine-generated information, resolving conflicts among heterogeneous data sources, identifying misinformation, rumors and bias, etc.<\/p>\n<ul>\n<li>Given diverse information about an object (e.g., a natural language claim text, an entity, structured triples and social network context) from heterogeneous and multi-modal sources, how do we identify high quality and trustworthy information and information sources?<\/li>\n<li>How can we leverage Knowledge Bases and external evidence sources from the Web for reasoning, explaining, and validating claims?<\/li>\n<li>How can we generate human-interpretable explanations for the models\u2019 verdict?<\/li>\n<li>How can we design robust fake information (e.g., reviews and news) detection mechanisms to withstand adversarial generation strategies, as spammers and content generators are co-evolving with the advanced detectors?<\/li>\n<\/ul>\n<p>In order to answer these questions, this workshop encourages submissions to focus on big ideas \u2013 for resolving conflicts, fact-checking, ascertaining credibility of claims, explaining predictions from deep fake detectors, developing robust adversarial mechanisms for fake content detection, manipulation and safeguards, and making detection algorithms fair and unbiased to the involved participants \u2013 in heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks and knowledge graphs.<\/p>\n<h3>Organizers<\/h3>\n<ul>\n<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/submukhe\/\">Subhabrata Mukherjee<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Microsoft Research)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.cs.iastate.edu\/people\/qi-li\" target=\"_blank\" rel=\"noopener\">Qi Li<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Iowa State University)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/www.cse.lehigh.edu\/~sxie\/\" target=\"_blank\" rel=\"noopener\">Sihong Xie<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Lehigh University)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.cs.uic.edu\/~psyu\/\" target=\"_blank\" rel=\"noopener\">Philip S. Yu<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UIC)<\/li>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/cse.buffalo.edu\/~jing\/\" target=\"_blank\" rel=\"noopener\">Jing Gao<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (Purdue University)<\/li>\n<\/ul>\n<h3>Board Member<\/h3>\n<ul>\n<li><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"http:\/\/hanj.cs.illinois.edu\/\" target=\"_blank\" rel=\"noopener\">Jiawei Han<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (UIUC)<\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Call for papers\"} --><!-- wp:freeform --><p>Our workshop on truth discovery and fact-checking is motivated by the need for new research, tools and techniques to advance the field with the following focus areas:<\/p>\n<ul>\n<li><strong>Multi-modality<\/strong> \u2013 Modern data are multi-modal encompassing relational data, social networks, natural language text, structured logs, images, video, etc. One family of techniques geared towards a particular data source may not work for the other. One of the major focus areas of this workshop is data heterogeneity, multi modality, novel applications and data sources related to truth discovery, fact-checking, and rumor detection.<\/li>\n<li><strong>Interpretability<\/strong> \u2013 Another focus area is to explore tools and methods that can generate human-interpretable explanations as opposed to black box methods, as transparency and lack of explanations remain a concern for industry to readily deploy these techniques in practise. Robustness. The third focus of the workshop will be security and robustness of fake content detection under a known or unknown environment where adversaries can learn and commit arbitrary attack strategies for data poisoning or evading detectors.<\/li>\n<li><strong>Resource-efficient Learning<\/strong> \u2013\u00a0Finally, lack of labeled training data and scarce annotation resources remain a serious challenge for truth discovery and credibility analysis \u2013 making it difficult to train state-of-the-art methods like deep neural networks. In this context, we encourage submissions focusing on techniques like few-shot learning, weak supervision from user interactions and distant supervision from auxiliary knowledge sources for resource-efficient learning.<\/li>\n<\/ul>\n<h3>Topics of interest include, but are not limited to:<\/h3>\n<ul>\n<li class=\"\">Truth finding and discovery<\/li>\n<li>Fact-checking, rumor, and misinformation<\/li>\n<li>Credibility analysis and spam detection<\/li>\n<li>Fake reviews and reviewers<\/li>\n<li>Leveraging knowledge bases for reasoning, validating and explaining contentious claims<\/li>\n<li>Transparency, fairness, bias, privacy and ethics of information systems<\/li>\n<li>Emerging applications, novel data sources, and case studies<\/li>\n<li>Explainable and interpretable models<\/li>\n<li>Robustness detection under adversarial and unknown data poisoning and evasion attacks.<\/li>\n<li>Heterogeneous and multi-modal information including relational data, natural language text, search logs, images, video, etc.<\/li>\n<\/ul>\n<h3 id=\"submission-guidelines\">Submission guidelines<\/h3>\n<p>We invite submissions for original research papers both theory and application-oriented as well as submissions from the research track and applied data science track of the main KDD conference. We encourage the participants to submit papers on novel datasets and release them to advance the field. Papers must be submitted in PDF according to the ACM Proceedings Template <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.acm.org\/publications\/proceedings-template\" target=\"_blank\" rel=\"noopener\">https:\/\/www.acm.org\/publications\/proceedings-template<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> in a single-blind format (including author names and affiliations). We welcome both long papers (maximum length of 9 pages) and short papers (maximum length of 5 pages). The accepted papers will be published on the workshop\u2019s website, and will not be considered archival for resubmission purposes. Please submit your papers at the EasyChair submission link <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/easychair.org\/my\/conference?conf=truefact21\" target=\"_blank\" rel=\"noopener\">https:\/\/easychair.org\/my\/conference?conf=truefact21<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>.<\/p>\n<h3 id=\"important-dates\">Important dates<\/h3>\n<ul>\n<li>All deadlines are 11:59 PM Pacific Standard Time<\/li>\n<li>Workshop paper submissions due: June 1, 2021<\/li>\n<li>Workshop paper notifications: June 20, 2021<\/li>\n<li>Workshop date: August 14-18, 2021<\/li>\n<\/ul>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- wp:msr\/content-tab {\"title\":\"Program Schedule\"} --><!-- wp:freeform --><p>Please join the event in the following [<a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/virtual.2021.kdd.org\/workshop_WS-33.html\">link<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>].<\/p>\n<table dir=\"ltr\" style=\"border-spacing: 0px;border-collapse: separate;width: 624px;height: 820px\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"100\" \/>\n<col width=\"100\" \/>\n<col width=\"100\" \/><\/colgroup>\n<tbody>\n<tr style=\"height: 74px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Speaker&quot;}\"><strong>Speaker<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Title&quot;}\"><strong>Title<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Time (Pacific Standard Time \/ AM)&quot;}\">\n<div>\n<div><strong>Time (Pacific Standard Time \/ AM)<\/strong><\/div>\n<\/div>\n<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Keynotes&quot;}\"><strong>Keynotes<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 74px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xiangliang Zhang (Notre Dame)&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.kaust.edu.sa\/en\/study\/faculty\/xiangliang-zhang\">Prof. Xiangliang Zhang (Notre Dame)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Characterizing the Attackability of Machine Learning Models against Evasion Attacks&quot;}\">Characterizing the Attackability of Machine Learning Models against Evasion Attacks<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;8:00 - 8:40&quot;}\">8:00 &#8211; 8:40<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Stephan Guennemann&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.professoren.tum.de\/en\/guennemann-stephan\">Prof. Stephan Guennemann (TU Munich)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Adversarial Robustness of Graph Neural Networks&quot;}\">Adversarial Robustness of Graph Neural Networks<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;8:40 - 9:20&quot;}\">8:40 &#8211; 9:20<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Matthew Lease (UTA)&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease (University of Texas at Austin)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Explainable Fact Checking with a Human-in-the-loop&quot;}\">Explainable Fact Checking with a Human-in-the-loop<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;9:20 - 10:00&quot;}\">9:20 &#8211; 10:00<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Contributed Talks&quot;}\"><strong>Contributed Talks<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xi Zhang&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/mekltdcs.bupt.edu.cn\/\">Prof. Xi Zhang (BUPT)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Rumor detection on social media with multimodal information fusion&quot;}\">Rumor detection on social media with multimodal information fusion<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:00 - 10:15&quot;}\">10:00 &#8211; 10:15<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Yichuan Li&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/sites.google.com\/view\/yichuanli\/\">Yichuan Li (WPI)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Fact-Enhanced Synthetic News Generation (AAAI 2021)&quot;}\">Fact-Enhanced Synthetic News Generation (AAAI 2021)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:15 - 10:35&quot;}\">10:15 &#8211; 10:35<\/td>\n<\/tr>\n<tr style=\"height: 50px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Alex Braylan &quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.cs.utexas.edu\/~braylan\/\">Alex Braylan (UTA)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Modeling and Aggregation of Complex Annotations via Annotation Distances&quot;}\">Modeling and Aggregation of Complex Annotations via Annotation Distances<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:35 - 10:55&quot;}\">10:35 &#8211; 10:55<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 480px;text-align: center;height: 26px\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Break&quot;}\"><strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Break<\/strong><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 26px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:55 - 11:00&quot;}\">10:55 &#8211; 11:00<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 480px;height: 26px;text-align: center\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Panel&quot;}\"><strong>Discussion Panel: Challenges in Fair Machine Learning<\/strong><\/p>\n<p>Panelists: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease <span class=\"sr-only\"> (opens in new tab)<\/span><\/a><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">(University of Texas at Austin)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>, <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/cse.buffalo.edu\/~jing\/index.html\">Prof. Jing Gao (Purdue University)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/p>\n<p>Host: <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/faculty.sites.iastate.edu\/qli\/\">Prof. Qi Li (Iowa State University)<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 26px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:00 - 11:30&quot;}\">11:00 &#8211; 11:30<\/td>\n<\/tr>\n<tr style=\"height: 26px\">\n<td style=\"padding: 0px;border: 1px solid;width: 624px;height: 26px;text-align: center\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Workshop Papers&quot;}\"><strong>Workshop Papers<\/strong><\/td>\n<\/tr>\n<tr style=\"height: 146px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xuan Wang&quot;}\">Xuan Wang, Vivian Hu, Xiangchen Song, Qi Li and\u00a0Jiawei Han<\/p>\n<p>(UIUC, IOWA)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;EvidenceMiner: Textual Evidence Mining in Scientific Literature&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/drive.google.com\/file\/d\/15vkfr7Wpkq8cXCnERHiArRPvL7yYL4kV\/view?usp=sharing\">EvidenceMiner: Textual Evidence Mining in Scientific Literature<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:30 - 11:45&quot;}\">11:30 &#8211; 11:45<\/td>\n<\/tr>\n<tr style=\"height: 146px\">\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\">Tarik Arici, Kushal Kumar, Hayreddin Ceker, Anoop Saladi and Ismail Tutar<\/p>\n<p>(Amazon)<\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering&quot;}\"><a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" target=\"_blank\" href=\"https:\/\/drive.google.com\/file\/d\/15xe1R9EkgH_CPHXVjV9A9mdtZkYNI6Hd\/view?usp=sharing\">Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering<span class=\"sr-only\"> (opens in new tab)<\/span><\/a><\/td>\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:45 - 12:00&quot;}\">11:45 &#8211; 12:00<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span id=\"label-external-link\" class=\"sr-only\" aria-hidden=\"true\">Opens in a new tab<\/span><\/p>\n<!-- \/wp:freeform --><!-- \/wp:msr\/content-tab --><!-- \/wp:msr\/content-tabs -->","tab-content":[{"id":0,"name":"About","content":"The Third International Workshop on <em>Truth Discovery and Fact Checking: Making a Credible Web for Tomorrow<\/em> will provide a forum where researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in resolving conflicts, fact-checking and ascertaining credibility of claims.\r\n\r\nIn recent times, the explosion of information from a variety of sources and cutting-edge techniques such as Deepfake have made it increasingly important to check the credibility and reliability of the data. Large volumes of data generated from diverse information channels like social media, online news outlets, and crowd-sourcing contribute valuable knowledge; however, this comes with additional challenges to ascertain the credibility of user-generated and machine-generated information, resolving conflicts among heterogeneous data sources, identifying misinformation, rumors and bias, etc.\r\n<ul>\r\n \t<li>Given diverse information about an object (e.g., a natural language claim text, an entity, structured triples and social network context) from heterogeneous and multi-modal sources, how do we identify high quality and trustworthy information and information sources?<\/li>\r\n \t<li>How can we leverage Knowledge Bases and external evidence sources from the Web for reasoning, explaining, and validating claims?<\/li>\r\n \t<li>How can we generate human-interpretable explanations for the models\u2019 verdict?<\/li>\r\n \t<li>How can we design robust fake information (e.g., reviews and news) detection mechanisms to withstand adversarial generation strategies, as spammers and content generators are co-evolving with the advanced detectors?<\/li>\r\n<\/ul>\r\nIn order to answer these questions, this workshop encourages submissions to focus on big ideas \u2013 for resolving conflicts, fact-checking, ascertaining credibility of claims, explaining predictions from deep fake detectors, developing robust adversarial mechanisms for fake content detection, manipulation and safeguards, and making detection algorithms fair and unbiased to the involved participants \u2013 in heterogeneous and multi-modal sources of information including texts, images, videos, relational data, social networks and knowledge graphs.\r\n<h3>Organizers<\/h3>\r\n<ul>\r\n \t<li><a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/submukhe\/\">Subhabrata Mukherjee<\/a> (Microsoft Research)<\/li>\r\n \t<li><a href=\"https:\/\/www.cs.iastate.edu\/people\/qi-li\" target=\"_blank\" rel=\"noopener\">Qi Li<\/a> (Iowa State University)<\/li>\r\n \t<li><a href=\"http:\/\/www.cse.lehigh.edu\/~sxie\/\" target=\"_blank\" rel=\"noopener\">Sihong Xie<\/a> (Lehigh University)<\/li>\r\n \t<li><a href=\"https:\/\/www.cs.uic.edu\/~psyu\/\" target=\"_blank\" rel=\"noopener\">Philip S. Yu<\/a> (UIC)<\/li>\r\n \t<li><a href=\"https:\/\/cse.buffalo.edu\/~jing\/\" target=\"_blank\" rel=\"noopener\">Jing Gao<\/a> (Purdue University)<\/li>\r\n<\/ul>\r\n<h3>Board Member<\/h3>\r\n<ul>\r\n \t<li><a href=\"http:\/\/hanj.cs.illinois.edu\/\" target=\"_blank\" rel=\"noopener\">Jiawei Han<\/a> (UIUC)<\/li>\r\n<\/ul>"},{"id":1,"name":"Call for papers","content":"Our workshop on truth discovery and fact-checking is motivated by the need for new research, tools and techniques to advance the field with the following focus areas:\r\n<ul>\r\n \t<li><strong>Multi-modality<\/strong> \u2013 Modern data are multi-modal encompassing relational data, social networks, natural language text, structured logs, images, video, etc. One family of techniques geared towards a particular data source may not work for the other. One of the major focus areas of this workshop is data heterogeneity, multi modality, novel applications and data sources related to truth discovery, fact-checking, and rumor detection.<\/li>\r\n \t<li><strong>Interpretability<\/strong> \u2013 Another focus area is to explore tools and methods that can generate human-interpretable explanations as opposed to black box methods, as transparency and lack of explanations remain a concern for industry to readily deploy these techniques in practise. Robustness. The third focus of the workshop will be security and robustness of fake content detection under a known or unknown environment where adversaries can learn and commit arbitrary attack strategies for data poisoning or evading detectors.<\/li>\r\n \t<li><strong>Resource-efficient Learning<\/strong> \u2013\u00a0Finally, lack of labeled training data and scarce annotation resources remain a serious challenge for truth discovery and credibility analysis \u2013 making it difficult to train state-of-the-art methods like deep neural networks. In this context, we encourage submissions focusing on techniques like few-shot learning, weak supervision from user interactions and distant supervision from auxiliary knowledge sources for resource-efficient learning.<\/li>\r\n<\/ul>\r\n<h3>Topics of interest include, but are not limited to:<\/h3>\r\n<ul>\r\n \t<li class=\"\">Truth finding and discovery<\/li>\r\n \t<li>Fact-checking, rumor, and misinformation<\/li>\r\n \t<li>Credibility analysis and spam detection<\/li>\r\n \t<li>Fake reviews and reviewers<\/li>\r\n \t<li>Leveraging knowledge bases for reasoning, validating and explaining contentious claims<\/li>\r\n \t<li>Transparency, fairness, bias, privacy and ethics of information systems<\/li>\r\n \t<li>Emerging applications, novel data sources, and case studies<\/li>\r\n \t<li>Explainable and interpretable models<\/li>\r\n \t<li>Robustness detection under adversarial and unknown data poisoning and evasion attacks.<\/li>\r\n \t<li>Heterogeneous and multi-modal information including relational data, natural language text, search logs, images, video, etc.<\/li>\r\n<\/ul>\r\n<h3 id=\"submission-guidelines\">Submission guidelines<\/h3>\r\nWe invite submissions for original research papers both theory and application-oriented as well as submissions from the research track and applied data science track of the main KDD conference. We encourage the participants to submit papers on novel datasets and release them to advance the field. Papers must be submitted in PDF according to the ACM Proceedings Template <a href=\"https:\/\/www.acm.org\/publications\/proceedings-template\" target=\"_blank\" rel=\"noopener\">https:\/\/www.acm.org\/publications\/proceedings-template<\/a> in a single-blind format (including author names and affiliations). We welcome both long papers (maximum length of 9 pages) and short papers (maximum length of 5 pages). The accepted papers will be published on the workshop\u2019s website, and will not be considered archival for resubmission purposes. Please submit your papers at the EasyChair submission link <a href=\"https:\/\/easychair.org\/my\/conference?conf=truefact21\" target=\"_blank\" rel=\"noopener\">https:\/\/easychair.org\/my\/conference?conf=truefact21<\/a>.\r\n<h3 id=\"important-dates\">Important dates<\/h3>\r\n<ul>\r\n \t<li>All deadlines are 11:59 PM Pacific Standard Time<\/li>\r\n \t<li>Workshop paper submissions due: June 1, 2021<\/li>\r\n \t<li>Workshop paper notifications: June 20, 2021<\/li>\r\n \t<li>Workshop date: August 14-18, 2021<\/li>\r\n<\/ul>"},{"id":2,"name":"Program Schedule","content":"Please join the event in the following [<a href=\"https:\/\/virtual.2021.kdd.org\/workshop_WS-33.html\">link<\/a>].\r\n<table dir=\"ltr\" style=\"border-spacing: 0px;border-collapse: separate;width: 624px;height: 820px\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\"><colgroup> <col width=\"100\" \/> <col width=\"100\" \/> <col width=\"100\" \/><\/colgroup>\r\n<tbody>\r\n<tr style=\"height: 74px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Speaker&quot;}\"><strong>Speaker<\/strong><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Title&quot;}\"><strong>Title<\/strong><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Time (Pacific Standard Time \/ AM)&quot;}\">\r\n<div>\r\n<div><strong>Time (Pacific Standard Time \/ AM)<\/strong><\/div>\r\n<\/div><\/td>\r\n<\/tr>\r\n<tr style=\"height: 26px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Keynotes&quot;}\"><strong>Keynotes<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"height: 74px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xiangliang Zhang (Notre Dame)&quot;}\"><a href=\"https:\/\/www.kaust.edu.sa\/en\/study\/faculty\/xiangliang-zhang\">Prof. Xiangliang Zhang (Notre Dame)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Characterizing the Attackability of Machine Learning Models against Evasion Attacks&quot;}\">Characterizing the Attackability of Machine Learning Models against Evasion Attacks<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 74px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;8:00 - 8:40&quot;}\">8:00 - 8:40<\/td>\r\n<\/tr>\r\n<tr style=\"height: 50px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Stephan Guennemann&quot;}\"><a href=\"https:\/\/www.professoren.tum.de\/en\/guennemann-stephan\">Prof. Stephan Guennemann (TU Munich)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Adversarial Robustness of Graph Neural Networks&quot;}\">Adversarial Robustness of Graph Neural Networks<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;8:40 - 9:20&quot;}\">8:40 - 9:20<\/td>\r\n<\/tr>\r\n<tr style=\"height: 50px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Matthew Lease (UTA)&quot;}\"><a href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease (University of Texas at Austin)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Explainable Fact Checking with a Human-in-the-loop&quot;}\">Explainable Fact Checking with a Human-in-the-loop<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;9:20 - 10:00&quot;}\">9:20 - 10:00<\/td>\r\n<\/tr>\r\n<tr style=\"height: 26px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 624px;text-align: center;height: 26px\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Contributed Talks&quot;}\"><strong>Contributed Talks<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"height: 50px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xi Zhang&quot;}\"><a href=\"https:\/\/mekltdcs.bupt.edu.cn\/\">Prof. Xi Zhang (BUPT)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Rumor detection on social media with multimodal information fusion&quot;}\">Rumor detection on social media with multimodal information fusion<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:00 - 10:15&quot;}\">10:00 - 10:15<\/td>\r\n<\/tr>\r\n<tr style=\"height: 50px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Yichuan Li&quot;}\"><a href=\"https:\/\/sites.google.com\/view\/yichuanli\/\">Yichuan Li (WPI)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Fact-Enhanced Synthetic News Generation (AAAI 2021)&quot;}\">Fact-Enhanced Synthetic News Generation (AAAI 2021)<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:15 - 10:35&quot;}\">10:15 - 10:35<\/td>\r\n<\/tr>\r\n<tr style=\"height: 50px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Alex Braylan &quot;}\"><a href=\"https:\/\/www.cs.utexas.edu\/~braylan\/\">Alex Braylan (UTA)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Modeling and Aggregation of Complex Annotations via Annotation Distances&quot;}\">Modeling and Aggregation of Complex Annotations via Annotation Distances<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 50px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:35 - 10:55&quot;}\">10:35 - 10:55<\/td>\r\n<\/tr>\r\n<tr style=\"height: 26px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 480px;text-align: center;height: 26px\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Break&quot;}\"><strong>\u00a0 \u00a0 \u00a0 \u00a0 \u00a0 \u00a0Break<\/strong><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;text-align: center;height: 26px\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;10:55 - 11:00&quot;}\">10:55 - 11:00<\/td>\r\n<\/tr>\r\n<tr style=\"height: 26px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 480px;height: 26px;text-align: center\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Panel&quot;}\"><strong>Discussion Panel: Challenges in Fair Machine Learning<\/strong>\r\n\r\nPanelists: <a href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">Prof. Matthew Lease <\/a><a href=\"https:\/\/www.ischool.utexas.edu\/~ml\/\">(University of Texas at Austin)<\/a>, <a href=\"https:\/\/cse.buffalo.edu\/~jing\/index.html\">Prof. Jing Gao (Purdue University)<\/a>\r\n\r\nHost: <a href=\"https:\/\/faculty.sites.iastate.edu\/qli\/\">Prof. Qi Li (Iowa State University)<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 26px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:00 - 11:30&quot;}\">11:00 - 11:30<\/td>\r\n<\/tr>\r\n<tr style=\"height: 26px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 624px;height: 26px;text-align: center\" colspan=\"3\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Workshop Papers&quot;}\"><strong>Workshop Papers<\/strong><\/td>\r\n<\/tr>\r\n<tr style=\"height: 146px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Xuan Wang&quot;}\">Xuan Wang, Vivian Hu, Xiangchen Song, Qi Li and\u00a0Jiawei Han\r\n\r\n(UIUC, IOWA)<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;EvidenceMiner: Textual Evidence Mining in Scientific Literature&quot;}\"><a href=\"https:\/\/drive.google.com\/file\/d\/15vkfr7Wpkq8cXCnERHiArRPvL7yYL4kV\/view?usp=sharing\">EvidenceMiner: Textual Evidence Mining in Scientific Literature<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:30 - 11:45&quot;}\">11:30 - 11:45<\/td>\r\n<\/tr>\r\n<tr style=\"height: 146px\">\r\n<td style=\"padding: 0px;border: 1px solid;width: 205px;height: 146px;text-align: center\">Tarik Arici, Kushal Kumar, Hayreddin Ceker, Anoop Saladi and Ismail Tutar\r\n\r\n(Amazon)<\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 275px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering&quot;}\"><a href=\"https:\/\/drive.google.com\/file\/d\/15xe1R9EkgH_CPHXVjV9A9mdtZkYNI6Hd\/view?usp=sharing\">Solving Price Per Unit Problem Around the World: Formulating Fact Extraction as Question Answering<\/a><\/td>\r\n<td style=\"padding: 0px;border: 1px solid;width: 144px;height: 146px;text-align: center\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;11:45 - 12:00&quot;}\">11:45 - 12:00<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>"}],"msr_startdate":"2021-08-15","msr_enddate":"2021-08-15","msr_event_time":"","msr_location":"Virtual","msr_event_link":"","msr_event_recording_link":"","msr_startdate_formatted":"August 15, 2021","msr_register_text":"Watch now","msr_cta_link":"","msr_cta_text":"","msr_cta_bi_name":"","featured_image_thumbnail":null,"event_excerpt":"The Third International TrueFact Workshop: Making a Credible Web for Tomorrow will provide a forum where researchers and practitioners from academia, government and industry can share insights and identify new challenges and opportunities in resolving conflicts, fact-checking and ascertaining credibility of claims. The workshop will be held virtually in conjunction with ACM SIGKDD 2021 on August 14-18th.","msr_research_lab":[199565],"related-researchers":[],"msr_impact_theme":[],"related-academic-programs":[],"related-groups":[392600,644373,702211],"related-projects":[675957],"related-opportunities":[],"related-publications":[],"related-videos":[],"related-posts":[],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/739651","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-event"}],"version-history":[{"count":9,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/739651\/revisions"}],"predecessor-version":[{"id":1146881,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event\/739651\/revisions\/1146881"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=739651"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=739651"},{"taxonomy":"msr-region","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-region?post=739651"},{"taxonomy":"msr-event-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-event-type?post=739651"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=739651"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=739651"},{"taxonomy":"msr-program-audience","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-program-audience?post=739651"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=739651"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=739651"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}