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Twitaddons mobilization(15mar2011)jy
- 1. 트위터는사회운동 미디어인가? :트윗애드온즈 사용 사례를 중심으로Sujin ChoiDept. of Radio-Television-FilmUniversity of Texas at Austin, U.S.A.Ji-Young ParkMaster’s degree candidate Han Woo Park (corresponding author) Associate Professor, Dept. of Media & CommunicationYeungnam University, South Korea
- 2. Why Twitter?단문메시지/ 이동성 / 접속의 확장성/ 세상을 중계(Zhao & Rosson, 2009)
- 3. 새로운 청자들에게 RT를통해 트윗을 확산, 공개적 지지, 다른 이들의 생각을 지지(boyd et al., 2010).
- 4. 보다 개인적 상태나대인 커뮤니케이션 관계에 집중됨.
- 5. 최근의 사회 운동의예들(이란에서는 2009년 대선에 대한 저항의 매체로 트위터를 사용) 2
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- 7. 트위터에 사회 모임의기능을 넣음. 주제별로 모임들이 생성, ‘당’ 108,876 tweets Twitaddons.com, 313,992 tweets Twitkr.com (2010년 7-8월)
- 8. 효과적인 커뮤니케이션을 위해당별로 자동으로 해쉬태그를트윗에 넣는 기능이 있음.
- 9. 한국에서 트위터 사용시해쉬태그 사용의 불편함 자동 해쉬태그삽입을 통해 효과적인 커무니케이션 추구. 3
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- 12. Java et al.(2007), Krishnamurthy et al. (2008), Huberman et al. (2008) , Honeycutt and Herring (2009), Zhao and Rosson (2009), boyd et al. (2010)
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- 14. Jansen et al.(2009), Hughes and Palen (2010)
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- 16. Literature ReviewSocialMobilization and the Internet정보와 조직 측면에서 사회 운동의 긍정적 효과 (Klein, 1999; Van Aelst & Walgrave, 2002)
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- 18. Negative: Wilhelm (1998), Sunstein (2007)
- 19. Positive: Kellner (2004),Langman (2005), Bennett & Iyengar (2008)
- 20. 사회 변동의 측면i)정보의 확산ii) 사회운동의 조직iii) 같은 주제(?)의 공유iv) 집단적 행동의 실행 (Porta and Diani, 2006; Van Aelst and Walgrave, 2002; Shirky, 2008)6
Editor's Notes
- #16 <보고서 관련내용 중 일부>Semantic network analysis was conducted against “Chopae,” “Blackberry,” and “Volunteer,” all of which topped within the groups respectively in terms of the number of members. Semantic network analysis is useful for identifying “thematically shared” cognitive structure among party members. During this analysis, CONCOR was adopted to cluster tweets and the top 20 words were identified. Collecting all the tweets of the three parties from November 5 to 25, 2010, the present research examined how many clusters of discourses were generated and whether they were in accordance to the mission statements of each party.
- #17 <보고서 관련내용 중 일부>Through a semantic network analysis of all the tweets generated by “Chopae,” “Blackberry,” and “Volunteer” from November 5 to 25, 2010, the present research found six to seven clusters of discourse for each party, mainly containing two large clusters respectively (see Table 4). Along with the close examination on the top 20 words and their occurring frequencies (see Table 5), this analysis suggests that party members formed shared values with their mission statements, having two main discussions and a few sub-clusters of relevant topics during the studied period.