Classifying and Summarizing Information from Microblogs During Epidemics
Identifieur interne : 000376 ( Pmc/Curation ); précédent : 000375; suivant : 000377Classifying and Summarizing Information from Microblogs During Epidemics
Auteurs : Koustav Rudra [Inde] ; Ashish Sharma [Inde] ; Niloy Ganguly [Inde] ; Muhammad Imran [Qatar]Source :
- Information Systems Frontiers [ 1387-3326 ] ; 2018.
Abstract
During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.
Url:
DOI: 10.1007/s10796-018-9844-9
PubMed: 32214879
PubMed Central: 7087635
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<front><div type="abstract" xml:lang="en"><p id="Par5">During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.</p>
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<title-group><article-title>Classifying and Summarizing Information from Microblogs During Epidemics</article-title>
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<name><surname>Rudra</surname>
<given-names>Koustav</given-names>
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<bio><sec id="d29e182"><title>Koustav Rudra</title>
<p id="Par1">received the B.E. degree in computer science from the Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India, and the M.Tech degree from IIT Kharagpur, India. He is currently pursuing the Ph.D. degree with the Department of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India. His current research interests include social networks, information retrieval, and data mining.</p>
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<contrib contrib-type="author"><name><surname>Sharma</surname>
<given-names>Ashish</given-names>
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<address><email>ashishshrma22@gmail.com</email>
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<xref ref-type="aff" rid="Aff1">1</xref>
<bio><sec id="d29e197"><title>Ashish Sharma</title>
<p id="Par2">is currently pursuing the Dual degree with the Department of Computer Science and Engineering, IIT Kharagpur, Kharagpur, India. His current research interests include social networks and information retrieval.</p>
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<contrib contrib-type="author"><name><surname>Ganguly</surname>
<given-names>Niloy</given-names>
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<xref ref-type="aff" rid="Aff1">1</xref>
<bio><sec id="d29e212"><title>Niloy Ganguly</title>
<p id="Par3">received the B.Tech. degree from IIT Kharagpur, Kharagpur, India, and the Ph.D. degree from the Indian Institute of Engineering Science and Technology, Shibpur, Howrah, India. He was a Post-Doctoral Fellow with Technical University, Dresden, Germany. He is currently a Professor with the Department of Computer Science and Engineering, IIT Kharagpur, where he leads the Complex Networks Research Group. His current research interests include complex networks, social networks, and mobile systems.</p>
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<contrib contrib-type="author"><name><surname>Imran</surname>
<given-names>Muhammad</given-names>
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<xref ref-type="aff" rid="Aff2">2</xref>
<bio><sec id="d29e227"><title>Muhammad Imran</title>
<p id="Par4">is a Scientist at the Qatar Computing Research Institute (QCRI) where he leads the Crisis Computing team. His interdisciplinary research focuses on natural language processing, text mining, human-computer interaction, applied machine learning, and stream processing areas. Dr. Imran has published over 50 research papers in top-tier international conferences and journals. Two of his papers have received the Best Paper Award. He has been serving as a Co-Chair of the Social Media Studies track of the ISCRAM international conference since 2014 and has served as Program Committee (PC) for many major conferences and workshops including SIGIR, ICWSM, ACM DH, ICWE, SWDM. Dr. Imran has worked as a Post-Doctoral researcher at QCRI (2013-2015). He received his Ph.D. in Computer Science from the University of Trento, Italy (2013), where he also used to co-teach various computer science courses (2009-2012).</p>
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<pub-date pub-type="epub"><day>20</day>
<month>3</month>
<year>2018</year>
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<pub-date pub-type="ppub"><year>2018</year>
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<volume>20</volume>
<issue>5</issue>
<fpage>933</fpage>
<lpage>948</lpage>
<permissions><copyright-statement>© Springer Science+Business Media, LLC, part of Springer Nature 2018</copyright-statement>
<license><license-p>This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.</license-p>
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<abstract id="Abs1"><p id="Par5">During a new disease outbreak, frustration and uncertainties among affected and vulnerable population increase. Affected communities look for known symptoms, prevention measures, and treatment strategies. On the other hand, health organizations try to get situational updates to assess the severity of the outbreak, known affected cases, and other details. Recent emergence of social media platforms such as Twitter provide convenient ways and fast access to disseminate and consume information to/from a wider audience. Research studies have shown potential of this online information to address information needs of concerned authorities during outbreaks, epidemics, and pandemics. In this work, we target three types of end-users (i) vulnerable population—people who are not yet affected and are looking for prevention related information (ii) affected population—people who are affected and looking for treatment related information, and (iii) health organizations—like WHO, who are interested in gaining situational awareness to make timely decisions. We use Twitter data from two recent outbreaks (Ebola and MERS) to build an automatic classification approach useful to categorize tweets into different disease related categories. Moreover, the classified messages are used to generate different kinds of summaries useful for affected and vulnerable communities as well as health organizations. Results obtained from extensive experimentation show the effectiveness of the proposed approach.</p>
</abstract>
<kwd-group xml:lang="en"><title>Keywords</title>
<kwd>Health crisis</kwd>
<kwd>Epidemic</kwd>
<kwd>Twitter</kwd>
<kwd>Classification</kwd>
<kwd>Summarization</kwd>
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<meta-value>© Springer Science+Business Media, LLC, part of Springer Nature 2018</meta-value>
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