{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:09:14Z","timestamp":1760710154242,"version":"3.41.0"},"reference-count":94,"publisher":"Association for Computing Machinery (ACM)","issue":"1","license":[{"start":{"date-parts":[[2020,2,29]],"date-time":"2020-02-29T00:00:00Z","timestamp":1582934400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"CRISP","award":["1541074"],"award-info":[{"award-number":["1541074"]}]},{"name":"CNS","award":["1421561"],"award-info":[{"award-number":["1421561"]}]},{"name":"National Science Foundation CISE's SAVI\/RCN","award":["1402266 and 1550379"],"award-info":[{"award-number":["1402266 and 1550379"]}]},{"DOI":"10.13039\/501100010287","name":"SaTC","doi-asserted-by":"crossref","award":["1564097"],"award-info":[{"award-number":["1564097"]}],"id":[{"id":"10.13039\/501100010287","id-type":"DOI","asserted-by":"crossref"}]},{"name":"REU","award":["1545173"],"award-info":[{"award-number":["1545173"]}]},{"name":"gifts, grants, or contracts from Fujitsu, HP, Intel, and Georgia Tech Foundation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Internet Technol."],"published-print":{"date-parts":[[2020,2,29]]},"abstract":"<jats:p>\n            With billions of active social media accounts and millions of live video cameras, live new big data offer many opportunities for smart applications. However, the main consumers of the new big data have been humans. We envision the research on\n            <jats:italic>live knowledge<\/jats:italic>\n            , to automatically acquire real-time, validated, and actionable information. Live knowledge presents two significant and diverging technical challenges: big noise and concept drift. We describe the EBKA (evidence-based knowledge acquisition) approach, illustrated by the LITMUS landslide information system. LITMUS achieves both high accuracy and wide coverage, demonstrating the feasibility and promise of EBKA approach to achieve live knowledge.\n          <\/jats:p>","DOI":"10.1145\/3374214","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T07:04:06Z","timestamp":1583219046000},"page":"1-21","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Beyond Artificial Reality"],"prefix":"10.1145","volume":"20","author":[{"given":"Calton","family":"Pu","sequence":"first","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Abhijit","family":"Suprem","sequence":"additional","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Rodrigo Alves","family":"Lima","sequence":"additional","affiliation":[{"name":"School of Computer Science, Georgia Institute of Technology, Atlanta, GA, USA"}]},{"given":"Aibek","family":"Musaev","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Alabama, Tuscaloosa, AL, USA"}]},{"given":"De","family":"Wang","sequence":"additional","affiliation":[{"name":"Sunmi US Inc, USA"}]},{"given":"Danesh","family":"Irani","sequence":"additional","affiliation":[{"name":"Google, USA"}]},{"given":"Steve","family":"Webb","sequence":"additional","affiliation":[{"name":"Web Gnomes, USA"}]},{"given":"Joao Eduardo","family":"Ferreira","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Sao Paulo, Sao Paulo, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2020,3,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature07634"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0023610"},{"key":"e_1_2_1_3_1","unstructured":"Google Flu Trends (GTF) failure story. [<https:\/\/en.wikipedia.org\/wiki\/Google_Flu_Trends>]. Retrieved November 9 2019.  Google Flu Trends (GTF) failure story. [<https:\/\/en.wikipedia.org\/wiki\/Google_Flu_Trends>]. Retrieved November 9 2019."},{"key":"e_1_2_1_4_1","volume-title":"When Google got flu wrong. Nature 494, 7436","author":"Butler Declan","year":"2013","unstructured":"Declan Butler . 2013. When Google got flu wrong. Nature 494, 7436 ( 2013 ), 155. Declan Butler. 2013. When Google got flu wrong. Nature 494, 7436 (2013), 155."},{"key":"e_1_2_1_5_1","volume-title":"The parable of Google flu: Traps in big data analysis. Science 343, 6176","author":"Lazer David","year":"2014","unstructured":"David Lazer , Ryan Kennedy , Gary King , and Alessandro Vespignani . 2014. The parable of Google flu: Traps in big data analysis. Science 343, 6176 ( 2014 ), 1203--1205. David Lazer, Ryan Kennedy, Gary King, and Alessandro Vespignani. 2014. The parable of Google flu: Traps in big data analysis. Science 343, 6176 (2014), 1203--1205."},{"key":"e_1_2_1_6_1","unstructured":"NTSB preliminary report on the Uber fatal accident in Tempe Arizona. [https:\/\/www.ntsb.gov\/investigations\/AccidentReports\/Reports\/HWY18MH010-prelim.pdf]. Retrieved November 9 2019.  NTSB preliminary report on the Uber fatal accident in Tempe Arizona. [https:\/\/www.ntsb.gov\/investigations\/AccidentReports\/Reports\/HWY18MH010-prelim.pdf]. Retrieved November 9 2019."},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/315207a0"},{"key":"e_1_2_1_8_1","unstructured":"Microsoft Tay chatbot. [<https:\/\/en.wikipedia.org\/wiki\/Tay_(bot)>]. Retrieved November 9 2019.  Microsoft Tay chatbot. [<https:\/\/en.wikipedia.org\/wiki\/Tay_(bot)>]. Retrieved November 9 2019."},{"key":"e_1_2_1_9_1","unstructured":"Array of Things project at Github [https:\/\/arrayofthings.github.io\/]. Retrieved November 9 2019.  Array of Things project at Github [https:\/\/arrayofthings.github.io\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_10_1","unstructured":"Guia USP and Campus USP: mobile apps for users to communicate with campus police and obtain other information. Available for iPhones (Apple Store) and Android devices (Google Play).  Guia USP and Campus USP: mobile apps for users to communicate with campus police and obtain other information. Available for iPhones (Apple Store) and Android devices (Google Play)."},{"volume-title":"Proceedings of the IEEE SmartWorld Congress.","author":"Ferreira J. E.","key":"e_1_2_1_11_1","unstructured":"J. E. Ferreira , J. A. Visintin , J. Okamoto , and C. Pu . 2017. Smart services: A case study on smarter public safety by a mobile app for University of S\u00e3o Paulo . In Proceedings of the IEEE SmartWorld Congress. J. E. Ferreira, J. A. Visintin, J. Okamoto, and C. Pu. 2017. Smart services: A case study on smarter public safety by a mobile app for University of S\u00e3o Paulo. In Proceedings of the IEEE SmartWorld Congress."},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/PERCOMW.2017.7917560"},{"key":"e_1_2_1_13_1","unstructured":"GRAIT-DM project and the RCN on Real-Time Big Data Analytics for Resilient Infrastructures in Smart and Connected Communities. [https:\/\/grait-dm.gatech.edu\/]. Retrieved November 9 2019.  GRAIT-DM project and the RCN on Real-Time Big Data Analytics for Resilient Infrastructures in Smart and Connected Communities. [https:\/\/grait-dm.gatech.edu\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_14_1","unstructured":"LITMUS landslide information service [https:\/\/grait-dm.gatech.edu\/demo-multi-source-integration\/]. Retrieved November 9 2019.  LITMUS landslide information service [https:\/\/grait-dm.gatech.edu\/demo-multi-source-integration\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_15_1","unstructured":"Open Set Recognition [<https:\/\/www.wjscheirer.com\/projects\/openset-recognition\/>]. Retrieved November 9 2019.  Open Set Recognition [<https:\/\/www.wjscheirer.com\/projects\/openset-recognition\/>]. Retrieved November 9 2019."},{"key":"e_1_2_1_16_1","unstructured":"Open World Machine Learning [<https:\/\/www.cs.uic.edu\/~liub\/open-classification.html>]. Retrieved November 9 2019.  Open World Machine Learning [<https:\/\/www.cs.uic.edu\/~liub\/open-classification.html>]. Retrieved November 9 2019."},{"key":"e_1_2_1_17_1","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1893--1902","author":"Abhijit Bendale","year":"2015","unstructured":"Bendale Abhijit and Terrance Boult . 2015 . Towards open world recognition . In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1893--1902 . Bendale Abhijit and Terrance Boult. 2015. Towards open world recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1893--1902."},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191513"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11704-016-6903-6"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191511"},{"key":"e_1_2_1_21_1","unstructured":"USGS Global Seismographic Network [http:\/\/earthquake.usgs.gov\/monitoring\/gsn\/]. Retrieved November 9 2019.  USGS Global Seismographic Network [http:\/\/earthquake.usgs.gov\/monitoring\/gsn\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_22_1","volume-title":"Tropical Rainfall Measuring Mission: Satellite monitoring of the intensity of rainfalls in the tropical and subtropical regions. Retrieved on","author":"NASA","year":"2019","unstructured":"NASA TRMM. Tropical Rainfall Measuring Mission: Satellite monitoring of the intensity of rainfalls in the tropical and subtropical regions. Retrieved on November 9, 2019 from http:\/\/trmm.gsfc.nasa.gov\/. NASA TRMM. Tropical Rainfall Measuring Mission: Satellite monitoring of the intensity of rainfalls in the tropical and subtropical regions. Retrieved on November 9, 2019 from http:\/\/trmm.gsfc.nasa.gov\/."},{"key":"e_1_2_1_23_1","volume-title":"[https:\/\/trmm.gsfc.nasa.gov\/trmm_rain\/Events\/latest_7_day_landslide.html]. Retrieved","author":"NOAA","year":"2019","unstructured":"NOAA landslide risk predictions for locations with 7-day rainfall : [https:\/\/trmm.gsfc.nasa.gov\/trmm_rain\/Events\/latest_7_day_landslide.html]. Retrieved November 9, 2019 . NOAA landslide risk predictions for locations with 7-day rainfall: [https:\/\/trmm.gsfc.nasa.gov\/trmm_rain\/Events\/latest_7_day_landslide.html]. Retrieved November 9, 2019."},{"key":"e_1_2_1_24_1","volume-title":"http:\/\/landslides.usgs.gov\/recent\/. Accessed on September 15, 2015. Discontinued","author":"Landslide Hazards Program USGS","year":"2016","unstructured":"USGS list of landslide events\u2014 Landslide Hazards Program . http:\/\/landslides.usgs.gov\/recent\/. Accessed on September 15, 2015. Discontinued in July 2016 and unavailable as of August 2019. Its previous content may have been preserved by the Internet Archive [http:\/\/www.archive.org\/]. USGS list of landslide events\u2014Landslide Hazards Program. http:\/\/landslides.usgs.gov\/recent\/. Accessed on September 15, 2015. Discontinued in July 2016 and unavailable as of August 2019. Its previous content may have been preserved by the Internet Archive [http:\/\/www.archive.org\/]."},{"key":"e_1_2_1_25_1","volume-title":"Accessed on","author":"CDC","year":"2019","unstructured":"CDC data on Ebola outbreaks [https:\/\/www.cdc.gov\/vhf\/ebola\/history\/chronology.html]. Accessed on August 8, 2019 . CDC data on Ebola outbreaks [https:\/\/www.cdc.gov\/vhf\/ebola\/history\/chronology.html]. Accessed on August 8, 2019."},{"key":"e_1_2_1_26_1","unstructured":"List of Most Trusted News Sources compiled by Pew Research Center [http:\/\/www.pewresearch.org\/fact-tank\/2014\/10\/30\/which-news-organization-is-the-most-trusted-the-answer-is-complicated\/]. Accessed on September 11 2015.  List of Most Trusted News Sources compiled by Pew Research Center [http:\/\/www.pewresearch.org\/fact-tank\/2014\/10\/30\/which-news-organization-is-the-most-trusted-the-answer-is-complicated\/]. Accessed on September 11 2015."},{"key":"e_1_2_1_27_1","volume-title":"Accessed on","author":"BBC","year":"2015","unstructured":"BBC poll on trusted news sources per country, [http:\/\/www.globescan.com\/news_archives\/bbcreut_country.html]. Accessed on September 15, 2015 . BBC poll on trusted news sources per country, [http:\/\/www.globescan.com\/news_archives\/bbcreut_country.html]. Accessed on September 15, 2015."},{"key":"e_1_2_1_28_1","unstructured":"Facebook data statistics. [https:\/\/www.brandwatch.com\/blog\/facebook-statistics\/] and [https:\/\/www.quora.com\/How-many-bytes-does-Facebook-store-every-day]. Retrieved July 25 2019.  Facebook data statistics. [https:\/\/www.brandwatch.com\/blog\/facebook-statistics\/] and [https:\/\/www.quora.com\/How-many-bytes-does-Facebook-store-every-day]. Retrieved July 25 2019."},{"key":"e_1_2_1_29_1","unstructured":"500M\/day tweets on Twitter. [https:\/\/www.internetlivestats.com\/twitter-statistics\/]. Retrieved July 25 2019.  500M\/day tweets on Twitter. [https:\/\/www.internetlivestats.com\/twitter-statistics\/]. Retrieved July 25 2019."},{"key":"e_1_2_1_30_1","unstructured":"Alexa's Top 500 Global Sites ranking [https:\/\/www.alexa.com\/topsites]. Retrieved November 9 2019.  Alexa's Top 500 Global Sites ranking [https:\/\/www.alexa.com\/topsites]. Retrieved November 9 2019."},{"key":"e_1_2_1_31_1","volume-title":"10 Key Marketing Trends for 2017","author":"IBM.","year":"2019","unstructured":"IBM. 2017. \u201c 10 Key Marketing Trends for 2017 \u201d [<https:\/\/www.ibm.com\/downloads\/cas\/XKBEABLN>]. Retrieved April 8, 2019 . IBM. 2017. \u201c10 Key Marketing Trends for 2017\u201d [<https:\/\/www.ibm.com\/downloads\/cas\/XKBEABLN>]. Retrieved April 8, 2019."},{"key":"e_1_2_1_32_1","volume-title":"Stanford CoreNLP","author":"The Stanford Natural Language Processing Group","year":"2015","unstructured":"The Stanford Natural Language Processing Group , \u201c Stanford CoreNLP ,\u201d [http:\/\/nlp.stanford.edu\/software\/corenlp.shtml]. Retrieved January 2, 2015 . The Stanford Natural Language Processing Group, \u201cStanford CoreNLP,\u201d [http:\/\/nlp.stanford.edu\/software\/corenlp.shtml]. Retrieved January 2, 2015."},{"key":"e_1_2_1_33_1","volume-title":"Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781","author":"Tomas Mikolov","year":"2013","unstructured":"Mikolov Tomas , Kai Chen , Greg Corrado , and Jeffrey Dean . 2013. Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781 ( 2013 ). Mikolov Tomas, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. ArXiv Preprint ArXiv:1301.3781 (2013)."},{"key":"e_1_2_1_34_1","unstructured":"TensorFlow project website [https:\/\/www.tensorflow.org\/]. Retrieved November 9 2019.  TensorFlow project website [https:\/\/www.tensorflow.org\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_35_1","unstructured":"Keras documentation website [https:\/\/keras.io\/]. Retrieved November 9 2019.  Keras documentation website [https:\/\/keras.io\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_36_1","unstructured":"WEKA project website [http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/]. Retrieved November 9 2019.  WEKA project website [http:\/\/www.cs.waikato.ac.nz\/ml\/weka\/]. Retrieved November 9 2019."},{"key":"e_1_2_1_37_1","unstructured":"DeepQA Project and Watson Q8A System created by the group at IBM Research [http:\/\/researcher.watson.ibm.com\/researcher\/view_group.php?id=2099]. Retrieved November 9 2019.  DeepQA Project and Watson Q8A System created by the group at IBM Research [http:\/\/researcher.watson.ibm.com\/researcher\/view_group.php?id=2099]. Retrieved November 9 2019."},{"key":"e_1_2_1_38_1","volume-title":"Text Retrieval Conference (TREC) English documents, 2001","author":"NIST","year":"2019","unstructured":"NIST Text Retrieval Conference (TREC) English documents, 2001 . http:\/\/trec.nist.gov\/data\/docs eng.html. Retrieved November 9, 2019 . NIST Text Retrieval Conference (TREC) English documents, 2001. http:\/\/trec.nist.gov\/data\/docs eng.html. Retrieved November 9, 2019."},{"key":"e_1_2_1_39_1","unstructured":"List of data sets for machine learning research [https:\/\/en.wikipedia.org\/wiki\/List_of_datasets_for_machine_learning_research]. Retrieved November 9 2019.  List of data sets for machine learning research [https:\/\/en.wikipedia.org\/wiki\/List_of_datasets_for_machine_learning_research]. Retrieved November 9 2019."},{"key":"e_1_2_1_40_1","unstructured":"MNIST (Modified National Institute of Standards and Technology database) [https:\/\/en.wikipedia.org\/wiki\/MNIST_database]. Retrieved November 9 2019.  MNIST (Modified National Institute of Standards and Technology database) [https:\/\/en.wikipedia.org\/wiki\/MNIST_database]. Retrieved November 9 2019."},{"key":"e_1_2_1_41_1","unstructured":"CIFAR-10 (Canadian Institute For Advanced Research) labeled subset (60 000 images) of the 80 million tiny images data set with 10 classes. [https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html]. The associated CIFAR-100 is a superset that contains 100 classes. Retrieved November 9 2019.  CIFAR-10 (Canadian Institute For Advanced Research) labeled subset (60 000 images) of the 80 million tiny images data set with 10 classes. [https:\/\/www.cs.toronto.edu\/~kriz\/cifar.html]. The associated CIFAR-100 is a superset that contains 100 classes. Retrieved November 9 2019."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/GRC.2006.1635746"},{"key":"e_1_2_1_43_1","article-title":"A perspective of evolution after five years: A large-scale study of web spam evolution","volume":"23","author":"Wang De","year":"2012","unstructured":"De Wang , Danesh Irani , Calton Pu . 2012 . A perspective of evolution after five years: A large-scale study of web spam evolution . Int. J. Coop. Inf. Syst. 23 , 2 (2014). De Wang, Danesh Irani, Calton Pu. 2012. A perspective of evolution after five years: A large-scale study of web spam evolution. Int. J. Coop. Inf. Syst. 23, 2 (2014).","journal-title":"Int. J. Coop. Inf. Syst."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871732"},{"key":"e_1_2_1_45_1","volume-title":"SPADE: A social-spam analytics and detection framework. Soc. Netw. Anal. Mining 4, 1","author":"Wang De","year":"2014","unstructured":"De Wang , Danesh Irani , and Calton Pu . 2014 . SPADE: A social-spam analytics and detection framework. Soc. Netw. Anal. Mining 4, 1 (2014). De Wang, Danesh Irani, and Calton Pu. 2014. SPADE: A social-spam analytics and detection framework. Soc. Netw. Anal. Mining 4, 1 (2014)."},{"key":"e_1_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Danesh Irani S. Webb K. Li and C. Pu. 2011. Modeling unintended personal information leakage from multiple online social networks IEEE Internet Comput. 15 3 (May--June 2011) 13--19.  Danesh Irani S. Webb K. Li and C. Pu. 2011. Modeling unintended personal information leakage from multiple online social networks IEEE Internet Comput. 15 3 (May--June 2011) 13--19.","DOI":"10.1109\/MIC.2011.25"},{"key":"e_1_2_1_47_1","volume-title":"How dirty is social data? An analysis of social spam. Netw. Insights (April 1","author":"Luebbe Jenny","year":"2015","unstructured":"Jenny Luebbe . 2015. How dirty is social data? An analysis of social spam. Netw. Insights (April 1 , 2015 ). [http:\/\/www.networkedinsights.com\/socialspam\/]. Jenny Luebbe. 2015. How dirty is social data? An analysis of social spam. Netw. Insights (April 1, 2015). [http:\/\/www.networkedinsights.com\/socialspam\/]."},{"key":"e_1_2_1_48_1","volume-title":"Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management.","author":"Musaev Aibek","year":"2014","unstructured":"Aibek Musaev , De Wang , and Calton Pu . 2014 . LITMUS: Landslide detection by integrating multiple sources . In Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management. Aibek Musaev, De Wang, and Calton Pu. 2014. LITMUS: Landslide detection by integrating multiple sources. In Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management."},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2014.26"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/IRI.2015.62"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICWS.2015.74"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2014.2376558"},{"volume-title":"Proceedings of the IEEE 2nd International Conference on Collaboration and Internet Computing.","author":"Wang D.","key":"e_1_2_1_53_1","unstructured":"D. Wang , A. Musaev , and C. Pu . 2016. Information diffusion analysis of rumor dynamics over a social-interaction based model . In Proceedings of the IEEE 2nd International Conference on Collaboration and Internet Computing. D. Wang, A. Musaev, and C. Pu. 2016. Information diffusion analysis of rumor dynamics over a social-interaction based model. In Proceedings of the IEEE 2nd International Conference on Collaboration and Internet Computing."},{"volume-title":"Proceedings of the International Conference on Internet of Things and Big Data. 435--440","author":"Tien I.","key":"e_1_2_1_54_1","unstructured":"I. Tien , A. Musaev , D. Benas , A. Ghadi , S. Goodman , and C. Pu . 2016. Detection of damage and failure events of critical public infrastructure using social sensor big data . In Proceedings of the International Conference on Internet of Things and Big Data. 435--440 . I. Tien, A. Musaev, D. Benas, A. Ghadi, S. Goodman, and C. Pu. 2016. Detection of damage and failure events of critical public infrastructure using social sensor big data. In Proceedings of the International Conference on Internet of Things and Big Data. 435--440."},{"volume-title":"Proceedings of the IEEE International Conference on Collaborative and Internet Computing.","author":"Hou Qixuan","key":"e_1_2_1_55_1","unstructured":"Qixuan Hou , A. Musaev , Y. Yang , and C. Pu . 2017. Towards multilingual support of landslides information service . In Proceedings of the IEEE International Conference on Collaborative and Internet Computing. Qixuan Hou, A. Musaev, Y. Yang, and C. Pu. 2017. Towards multilingual support of landslides information service. In Proceedings of the IEEE International Conference on Collaborative and Internet Computing."},{"volume-title":"Proceedings of the IEEE 37th International Conference on Distributed Computing Systems.","author":"Musaev A.","key":"e_1_2_1_56_1","unstructured":"A. Musaev and C. Pu . 2017. Towards multilingual automated classification systems . In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems. A. Musaev and C. Pu. 2017. Towards multilingual automated classification systems. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems."},{"volume-title":"Proceedings of the IEEE 37th International Conference on Distributed Computing Systems.","author":"Musaev A.","key":"e_1_2_1_57_1","unstructured":"A. Musaev , Q. Hou , Y. Yang , and C. Pu . 2017. LITMUS: Towards multilingual reporting of landslides . In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems. A. Musaev, Q. Hou, Y. Yang, and C. Pu. 2017. LITMUS: Towards multilingual reporting of landslides. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems."},{"volume-title":"Proceedings of the IEEE 37th International Conference on Distributed Computing Systems.","author":"Musaev A.","key":"e_1_2_1_58_1","unstructured":"A. Musaev , D. Wang , J. Xie , and C. Pu . 2017. REX: Rapid ensemble classification system for landslide detection using social media . In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems. A. Musaev, D. Wang, J. Xie, and C. Pu. 2017. REX: Rapid ensemble classification system for landslide detection using social media. In Proceedings of the IEEE 37th International Conference on Distributed Computing Systems."},{"key":"e_1_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2017.242"},{"key":"e_1_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3328905.3329510"},{"key":"e_1_2_1_61_1","doi-asserted-by":"crossref","unstructured":"A. Suprem A. Musaev and C. Pu. 2019. Concept drift adaptive physical event detection for social media streams. In Proceedings of the World Congress on Services. Lecture Notes in Computer Science Y. Xia L. J. Zhang (eds.). Springer Cham 11517.  A. Suprem A. Musaev and C. Pu. 2019. Concept drift adaptive physical event detection for social media streams. In Proceedings of the World Congress on Services. Lecture Notes in Computer Science Y. Xia L. J. Zhang (eds.). Springer Cham 11517.","DOI":"10.1007\/978-3-030-23381-5_7"},{"volume-title":"Proc. IEEE 86","author":"LeCun Yann","key":"e_1_2_1_62_1","unstructured":"Yann LeCun , L\u00e9on Bottou , Yoshua Bengio , and Patrick Haffner . Gradient-based learning applied to document recognition . Proc. IEEE 86 , 11 (1D998), 2278--2324. Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proc. IEEE 86, 11 (1D998), 2278--2324."},{"volume-title":"Proceedings of the 19th International Conference on World Wide Web. 851--860","author":"Sakaki T.","key":"e_1_2_1_63_1","unstructured":"T. Sakaki , M. Okazaki , and Y. Matsuo . 2010. Earthquake shakes Twitter users: Real-time event detection by social sensors . In Proceedings of the 19th International Conference on World Wide Web. 851--860 . T. Sakaki, M. Okazaki, and Y. Matsuo. 2010. Earthquake shakes Twitter users: Real-time event detection by social sensors. In Proceedings of the 19th International Conference on World Wide Web. 851--860."},{"key":"e_1_2_1_64_1","doi-asserted-by":"crossref","unstructured":"X.\n      Wang F.\n      Zhu J.\n      Jiang and \n      S.\n      Li\n  . \n  2013\n  . Real time event detection in Twitter. In Web-Age Information Management Vol. \n  7923 Lecture Notes in Computer Science 502--513. \n  Springer Berlin\n  .  X. Wang F. Zhu J. Jiang and S. Li. 2013. Real time event detection in Twitter. In Web-Age Information Management Vol. 7923 Lecture Notes in Computer Science 502--513. Springer Berlin.","DOI":"10.1007\/978-3-642-38562-9_51"},{"volume-title":"Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 255--264","author":"Radinsky K.","key":"e_1_2_1_65_1","unstructured":"K. Radinsky and E. Horvitz . 2013. Mining the web to predict future events . In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 255--264 . K. Radinsky and E. Horvitz. 2013. Mining the web to predict future events. In Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 255--264."},{"key":"e_1_2_1_66_1","doi-asserted-by":"crossref","unstructured":"M.\n      Kitsuregawa\n     and \n      M.\n      Toyoda\n  . \n  2011\n  . Analytics for info-plosion including information diffusion studies for the 3.11 disaster. In Web-Age Information Management Vol. 6897 Lecture Notes in Computer Science 1--1. \n  Springer Berlin\n  .  M. Kitsuregawa and M. Toyoda. 2011. Analytics for info-plosion including information diffusion studies for the 3.11 disaster. In Web-Age Information Management Vol. 6897 Lecture Notes in Computer Science 1--1. Springer Berlin.","DOI":"10.1007\/978-3-642-23535-1_1"},{"key":"e_1_2_1_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/2522968.2522981"},{"key":"e_1_2_1_68_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2017.01.078"},{"key":"e_1_2_1_69_1","doi-asserted-by":"publisher","DOI":"10.1111\/coin.12017"},{"key":"e_1_2_1_70_1","first-page":"1345","article-title":"A survey on transfer learning","volume":"22","author":"Jialin Pan Sinno","year":"2009","unstructured":"Pan Sinno Jialin and Qiang Yang . 2009 . A survey on transfer learning . IEEE Trans. Knowl. Data Eng. 22 , 10 (2009), 1345 -- 1359 . Pan Sinno Jialin and Qiang Yang. 2009. A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22, 10 (2009), 1345--1359.","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"e_1_2_1_71_1","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-016-0043-6"},{"key":"e_1_2_1_72_1","doi-asserted-by":"crossref","unstructured":"J. A. Gama I. \u017dliobait\u0117 A. Bifet M. Pechenizkiy and A. Bouchachia. 2014. A survey on concept drift adaptation. ACM Comput. Surv. 46 4 (2014) 44 1--37.  J. A. Gama I. \u017dliobait\u0117 A. Bifet M. Pechenizkiy and A. Bouchachia. 2014. A survey on concept drift adaptation. ACM Comput. Surv. 46 4 (2014) 44 1--37.","DOI":"10.1145\/2523813"},{"key":"e_1_2_1_73_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2017.2775225"},{"key":"e_1_2_1_74_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-018-0554-1"},{"key":"e_1_2_1_75_1","article-title":"Ensemble tracking","volume":"29","author":"Shai Avidan","year":"2007","unstructured":"Avidan Shai . 2007 . Ensemble tracking . IEEE Trans. Pattern Anal. Mach. Intell. 29 , 2 (2007). Avidan Shai. 2007. Ensemble tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2 (2007).","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"e_1_2_1_76_1","doi-asserted-by":"publisher","DOI":"10.5244\/C.20.6"},{"key":"e_1_2_1_77_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2018.03.001"},{"key":"e_1_2_1_78_1","doi-asserted-by":"publisher","DOI":"10.1177\/0165551517698564"},{"key":"e_1_2_1_79_1","doi-asserted-by":"publisher","DOI":"10.1145\/3066166"},{"key":"e_1_2_1_80_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.239"},{"key":"e_1_2_1_81_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2013.255"},{"key":"e_1_2_1_82_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.004"},{"key":"e_1_2_1_83_1","doi-asserted-by":"crossref","unstructured":"Cha Zhang and Yunqian Ma (eds.). 2012. Ensemble Machine Learning: Methods and Applications. Springer Science 8 Business Media.  Cha Zhang and Yunqian Ma (eds.). 2012. Ensemble Machine Learning: Methods and Applications. Springer Science 8 Business Media.","DOI":"10.1007\/978-1-4419-9326-7"},{"key":"e_1_2_1_84_1","unstructured":"K-means clustering. [<https:\/\/en.wikipedia.org\/wiki\/K-means_clustering>].  K-means clustering. [<https:\/\/en.wikipedia.org\/wiki\/K-means_clustering>]."},{"volume-title":"Active Learning Literature Survey. Technical report","author":"Settles Burr","key":"e_1_2_1_85_1","unstructured":"Burr Settles . 2009. Active Learning Literature Survey. Technical report . University of Wisconsin-Madison Department of Computer Sciences. Burr Settles. 2009. Active Learning Literature Survey. Technical report. University of Wisconsin-Madison Department of Computer Sciences."},{"volume-title":"Proceedings of the 23rd International Conference on World Wide Web. 143--154","author":"Panagiotis","key":"e_1_2_1_86_1","unstructured":"Panagiotis G. Ipeirotis and Evgeniy Gabrilovich. 2014. Quizz: Targeted crowdsourcing with a billion (potential) users . In Proceedings of the 23rd International Conference on World Wide Web. 143--154 . Panagiotis G. Ipeirotis and Evgeniy Gabrilovich. 2014. Quizz: Targeted crowdsourcing with a billion (potential) users. In Proceedings of the 23rd International Conference on World Wide Web. 143--154."},{"key":"e_1_2_1_87_1","volume-title":"Boyd","author":"Josang Audun","year":"2007","unstructured":"Audun Josang , Roslan Ismail , and Colin A . Boyd . 2007 . A survey of trust and reputation systems for online service provisioning. Dec. Supp. Syst. 43, 2 (Mar. 2007), 618--644. Elsevier . Audun Josang, Roslan Ismail, and Colin A. Boyd. 2007. A survey of trust and reputation systems for online service provisioning. Dec. Supp. Syst. 43, 2 (Mar. 2007), 618--644. Elsevier."},{"key":"e_1_2_1_88_1","doi-asserted-by":"publisher","DOI":"10.20533\/ijicr.2042.4655.2010.0007"},{"volume-title":"Proceedings of the 19th International Conference on World Wide Web,. 751--760","author":"Pan S. J.","key":"e_1_2_1_89_1","unstructured":"S. J. Pan , X. Ni , J.-T. Sun , Q. Yang , and Z. Chen . Cross-domain sentiment classification via spectral feature alignment . In Proceedings of the 19th International Conference on World Wide Web,. 751--760 . S. J. Pan, X. Ni, J.-T. Sun, Q. Yang, and Z. Chen. Cross-domain sentiment classification via spectral feature alignment. In Proceedings of the 19th International Conference on World Wide Web,. 751--760."},{"key":"e_1_2_1_90_1","doi-asserted-by":"crossref","unstructured":"Y.\n      Zhen\n     and \n      C.\n      Li\n  . \n  2008\n  . Cross-domain knowledge transfer using semi-supervised classification. In AI 2008: Advances in Artificial Intelligence Vol. \n  5360 Lecture Notes in Computer Science 362--371. \n  Springer Berlin\n  .  Y. Zhen and C. Li. 2008. Cross-domain knowledge transfer using semi-supervised classification. In AI 2008: Advances in Artificial Intelligence Vol. 5360 Lecture Notes in Computer Science 362--371. Springer Berlin.","DOI":"10.1007\/978-3-540-89378-3_36"},{"key":"e_1_2_1_91_1","volume-title":"Barto","author":"Sutton Richard S.","year":"2018","unstructured":"Richard S. Sutton and Andrew G . Barto . 2018 . Reinforcement Learning : An Introduction. MIT press . Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. MIT press."},{"key":"e_1_2_1_92_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2016.7471613"},{"volume-title":"Automated machine learning-methods, systems, challenges. Autom. Mach. Learn","author":"Frank Hutter","key":"e_1_2_1_93_1","unstructured":"Hutter Frank , Lars Kotthoff , and Joaquin Vanschoren . 2019. Automated machine learning-methods, systems, challenges. Autom. Mach. Learn . Springer, New York, NY , USA. Hutter Frank, Lars Kotthoff, and Joaquin Vanschoren. 2019. Automated machine learning-methods, systems, challenges. Autom. Mach. Learn. Springer, New York, NY, USA."},{"key":"e_1_2_1_94_1","volume-title":"Retrieved on","author":"ImageNet","year":"2019","unstructured":"ImageNet data set. Retrieved on November 9, 2019 from http:\/\/www.image-net.org\/. ImageNet data set. Retrieved on November 9, 2019 from http:\/\/www.image-net.org\/."}],"container-title":["ACM Transactions on Internet Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374214","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3374214","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:41:47Z","timestamp":1750200107000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3374214"}},"subtitle":["Finding and Monitoring Live Events from Social Sensors"],"short-title":[],"issued":{"date-parts":[[2020,2,29]]},"references-count":94,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,2,29]]}},"alternative-id":["10.1145\/3374214"],"URL":"https:\/\/doi.org\/10.1145\/3374214","relation":{},"ISSN":["1533-5399","1557-6051"],"issn-type":[{"type":"print","value":"1533-5399"},{"type":"electronic","value":"1557-6051"}],"subject":[],"published":{"date-parts":[[2020,2,29]]},"assertion":[{"value":"2019-08-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2019-11-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2020-03-02","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}