- 日時(Date)
- 2022年12月13日 17:00-18:00
- 場所(Venue)
- オンライン（Online via Zoom）
- 申込方法 (Registration)
- 以下の事前登録フォームから、12月12日17:00までに、参加者の情報をご記入ください。後ほど、ご登録いただいた電子メールアドレスに、Zoom URLをお送り致します。 (Please register your information via the following registration form by 17:00, Dec 12th. We will send Zoom URL to your e-mail address later.)
- https://docs.google.com/forms/d/e/1FAIpQLSeoQnokmaK_GcMZ5YwwfFcoS2pC-0PGulK8XooBuRGB80FHEA/viewform
題目(Title)： “Faster, Sooner, Cheaper: Can Social Media Reflect Real-World Phenomena?”
講演者 (Speaker)： Suppawong Tuarob (Mahidol University, Thailand)
概要 (Abstract)：
Globally, online communities generate enormous volumes of data every day. A common use of social networks is to give a timely and cost-effective means of reflecting on real-world events. Although the applications of information derived from social networks are numerous, only a tiny portion of them are committed to improving society. For instance, real-time analysis of Twitter data has been used to model earthquake warning detection systems, identify medical and emergency needs during recovery from natural disasters (such as the Haiti Earthquake), detect the spread of influenza-like illnesses, and uncover abusive behaviors in social networks. In addition, several studies have examined the use of social media to monitor population-wide and individual health in the real world, including drinking issues, epidemics, drug misuse, and mental illness. Numerous research has shown that social media might be utilized to track depression and public conversation during the COVID-19 epidemic. This talk will examine applications that establish online social networks as viable information sources for estimating real-world phenomena.
略歴 (Biography)：
Suppawong Tuarob received his PhD in computer science and engineering and MS in industrial engineering from the Pennsylvania State University and his BSE and MSE in computer science and engineering from the University of Michigan-Ann Arbor. Currently, he is an Associate Professor of Computer Science at Mahidol University, Thailand. His research involves data mining in large-scale scholarly, social media, and healthcare domains.
対象(Target Audience)：
ACM SIGIR 東京支部会員、一般 (Member of Tokyo ACM SIGIR Chapter, Public audience)
主催(Organizer)：
本セミナーは、ACM SIGIR 東京支部の主催です。上記のURLから、事前登録をしてくだされば、どなたでも無料で参加できます。 (This seminar is organized by Tokyo ACM SIGIR Chapter. Anyone can join if you register your information via the above registration form URL.)
