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고성안

Ko, Sungahn
Intelligent Visual Analysis and Data Exploration Research
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dc.citation.conferencePlace US -
dc.citation.conferencePlace Waltham, MA, USA -
dc.citation.title IEEE Symposium on Technologies for Homeland Security -
dc.contributor.author Chae, Junghoon -
dc.contributor.author Zhang, Jiawei -
dc.contributor.author Ko, Sungahn -
dc.contributor.author Malik, Abish -
dc.contributor.author Connell, Heather -
dc.contributor.author Ebert, David S. -
dc.date.accessioned 2023-12-19T20:41:05Z -
dc.date.available 2023-12-19T20:41:05Z -
dc.date.created 2019-08-06 -
dc.date.issued 2016-05-01 -
dc.description.abstract A hoax distress call is a serious concern for the U.S. Coast Guard. Hoax calls not only put the Coast Guard rescue personnel in potentially dangerous situations, but also waste valuable assets that should be used for real emergency situations. However, conventional approaches do not provide enough information for investigating hoax calls and callers. As social media has played a pervasive role in the way people communicate, such data opens new opportunities and solutions to a wide range of challenges. In this paper, we present social media visual analytics solutions for supporting the investigation for hoax distress calls. We not only provide a set of comprehensive keyword collections, but also resolve the lack of social media data for the investigation. Our framework allows investigators to identify suspicious Twitter users and provide a visual analytics environment designed to examine geo-tagged tweets and Instagram messages in the context of hoax distress calls. -
dc.identifier.bibliographicCitation IEEE Symposium on Technologies for Homeland Security -
dc.identifier.doi 10.1109/ths.2016.7568903 -
dc.identifier.scopusid 2-s2.0-84991795143 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/32613 -
dc.identifier.url https://ieeexplore.ieee.org/document/7568903 -
dc.language 영어 -
dc.publisher IEEE -
dc.title Visual analytics for investigative analysis of hoax distress calls using social media -
dc.type Conference Paper -
dc.date.conferenceDate 2016-05-10 -

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