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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ³í¹®Áö D : µ¥ÀÌŸº£À̽º

Á¤º¸°úÇÐȸ ³í¹®Áö D : µ¥ÀÌŸº£À̽º

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) Æ®À§ÅÍ µ¥ÀÌÅÍ ¼öÁýÀ» À§ÇÑ µ¿Àû ½Ãµå ¼±ÅÃ
¿µ¹®Á¦¸ñ(English Title) Dynamic Seed Selection for Twitter Data Collection
ÀúÀÚ(Author) ÀÌÇöö   º¯Ã¢Çö   ±è¾ç°ï   ÀÌ»óÈ£   Hyoenchoel Lee   Changhyun Byun   Yanggon Kim   Sang Ho Lee  
¿ø¹®¼ö·Ïó(Citation) VOL 41 NO. 04 PP. 0217 ~ 0225 (2014. 08)
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(Korean Abstract)
Æ®À§ÅÍ¿Í °°Àº ¼Ò¼È³×Æ®¿öÅ© ºÐ¼®Àº Àΰ£ÀÇ ÇൿÀ» ÀÌÇØÇϰųª, È­Á¦°¡ µÇ´Â ÁÖÁ¦¸¦ ŽÁöÇϰųª, ¿µÇâ·Â ÀÖ´Â »ç¶÷À» ½Äº°Çϰųª, Ä¿¹Â´ÏƼ³ª ±×·ìÀ» ¹ß°ßÇϴµ¥ Èï¹Ì·Î¿î ½Ã°¢À» Á¦°øÇÒ ¼ö ÀÖ´Ù. ÇÏÁö¸¸ ¼Ò¼È³×Æ®¿öÅ©°¡ °¡Áö´Â Ư¼º(Áï µ¥ÀÌÅÍ°¡ ¹æ´ëÇÏ°í, Á¤±³ÇÏÁö ¾ÊÀ¸¸ç ¶ÇÇÑ µ¿ÀûÀΠƯ¼º)À¸·Î ÀÎÇÏ¿© ¼Ò¼È³×Æ®¿öÅ©¿¡¼­ ÁÖÁ¦¿Í ¿¬°üÀÌ Àִµ¥ÀÌÅ͸¦ ¼öÁýÇÏ´Â °ÍÀº ¾î·Á¿î ÀÏÀÌ´Ù. º» ³í¹®Àº ÁÖ¾îÁø ÁÖÁ¦¿Í °ü·ÃÀÖ´Â Æ®À­À» È¿°úÀûÀ¸·Î ¼öÁýÇϱâ À§ÇÏ¿© ½Ãµå ³ëµå¸¦ µ¿ÀûÀ¸·Î ¼±ÅÃÇÏ´Â ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÑ´Ù. º» ¾Ë°í¸®ÁòÀº »ç¿ëÀÚÀÇ ¿µÇâ·ÂÀ» ÃøÁ¤Çϱâ À§ÇÏ¿© »ç¿ëÀÚ ¼Ó¼ºÀ» È°¿ëÇϸç, ¼öÁý ÇÁ·Î¼¼½º Áß¿¡½Ãµå ³ëµå¸¦ µ¿ÀûÀ¸·Î ÇÒ´çÇÑ´Ù. ¿ì¸®´Â Á¦¾ÈÇÑ ¾Ë°í¸®ÁòÀ» ½ÇÁ¦Æ® À­µ¥ÀÌÅÍ¿¡ Àû¿ëÇÏ¿´À¸¸ç, ¸¸Á·ÇÒ ¸¸ÇÑ ¼º´É °á°ú¸¦ ¾ò¾ú´Ù.
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(English Abstract)
Analysis of social media such as Twitter can yield interesting perspectives to understanding human behavior, detecting hot issues, identifying influential people, or discovering a group and community. However, it is difficult to gather the data relevant to specific topics due to the main characteristics of social media data; data is large, noisy, and dynamic. This paper proposes a new algorithm that dynamically selects the seed nodes to efficiently collect tweets relevant to topics. The algorithm utilizes attributes of users to evaluate the user influence, and dynamically selects the seed nodes during the collection process. We evaluate the proposed algorithm with real tweet data, and get satisfactory performance results.
Å°¿öµå(Keyword) Æ®À§ÅÍ   Æ®À­¼öÁý   ½ÃµåºÐ¼®   Æ®À­µ¥ÀÌŸº£À̽º   twitter   tweet crawling   seed analysis   tweet databases  
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