dbjapanメーリングリストアーカイブ(2014年)
[dbjapan] SIGMOD-J 講演会 Dongwon Lee 教授
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- Subject: [dbjapan] SIGMOD-J 講演会 Dongwon Lee 教授
- From: Masashi Toyoda <toyoda [at] tkl.iis.u-tokyo.ac.jp>
- Date: Thu, 03 Jul 2014 19:58:05 +0900
日本データベース学会の皆様 東大生産研の豊田です。 来る7月16日に、 Penn. State UniversityのDongwon Lee教授による講演会を SIGMOD日本支部主催で行います。 ソーシャルメディアにおけるLikeボタンの挙動解析に関する講演です。 お誘いあわせの上お越しください。 ------------------------------------------------------------- 講演者: Prof. Dongwon Lee 主催: ACM SIGMOD 日本支部 日時: 2014年7月16日 18:00〜 場所: 東京大学生産技術研究所 E棟5階 Ew-501室 参加費:無料 概要: Analyzing the “LIKE” Activities in Social Media: As one of popular activities in social media, pressing the LIKE button toward published contents can be interpreted as an indication of one's shared interests to the contents or the original posters. Therefore, such LIKE activities form relationships and networks among people, raising interesting questions about their unique characteristics and implications. However, there has been very little research that solely explored the LIKE information in social media. To address this lack of understanding, we present our recent study: (1) an analysis on the LIKE activities, especially focusing on their structural, influential, and contextual aspects, built from 2.3 billion Likes between 130K posters and 44.6 million photos, in Instagram; (2) unique characteristics of “teens” in their LIKE activities in Instagram, indicating the so-called “generation LIKE”; and (3) a preliminary study on accurately predicting user attributes using their LIKE activities in Flickr. Bio: Dongwon Lee is currently an associate professor in the College of Information Sciences and Technology (a.k.a. iSchool) of the Pennsylvania State University, USA. He obtained his Ph.D. in Computer Science from UCLA in 2002. From 1995 to 1997, he has also worked at AT&T Bell Labs. Working mostly on the issues arising in the management and mining of diverse forms of data (e.g., relational records, documents, XML, and social media), he has (co-)authored over 130+ scholarly articles in selective publication outlets in Databases and Data Mining. Further details of his research can be found at: http://pike.psu.edu/
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