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Twitter Mining: The Case of 2014 Indonesian Legislative Elections

첫 페이지 보기
  • 발행기관
    보안공학연구지원센터(IJSEIA) 바로가기
  • 간행물
    International Journal of Software Engineering and Its Applications SCOPUS 바로가기
  • 통권
    Vol.8 No.10 (2014.10)바로가기
  • 페이지
    pp.191-202
  • 저자
    Rischan Mafrur, M Fiqri Muthohar, Gi Hyun Bang, Do Kyeong Lee, Kyungbaek Kim, Deokjai Choi
  • 언어
    영어(ENG)
  • URL
    https://www.earticle.net/Article/A233324

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원문정보

초록

영어
Twitter is an online micro blogging and social network which not only for communication with others but twitter can be used for business, administration, or political campaign. This paper concern about twitter for political campaign, we take one case in Indonesian legislative elections. In April 2014, Indonesia has held legislative elections. Fifteen political parties have been participated to this election. Each parties has unique strategic for campaign including social media campaign. In this paper we interested with one of political party which very active in social media campaign especially in Twitter, Partai Keadilan Sejahtera (PKS) or Prosperous Justice Party. This party has a lot of supporters and haters are active tweeting on Twitter about the goodness and badness of this party. This thing begs the question that "Who they are? It is really the voice of Indonesia or just tweets from twitter campaign accounts". This paper tried to answer above question by presenting the result of analysis with empirical data. We collected all tweets which related with this party and then extract the data and classify to two types of twitter accounts: real and campaign accounts. We use some features and Naïve Bayes as method for classification. We observe the difference between real and campaign accounts in terms of the tweeting behavior and account properties. We applied text mining methods to know what the meaning of the messages that they bring on their tweets.

목차

Abstract
 1. Introduction
 2. Previous Related Work
 3. Experiment Detail
  3.1. Data Collection and Extraction
  3.2. Ground Truth Creation
  3.3. Choosing Features and Classification Methods
 4. Result and Discussion
  4.1. Features Evaluation
  4.2. Classifier Performance Evaluation
  4.3. Who are Tweeting
  4.4. Text Mining
  4.5. Tweeting Devices Distribution
 5. Conclusion
 Acknowledgements
 References

키워드

Twitter mining social network classification text mining

저자

  • Rischan Mafrur [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]
  • M Fiqri Muthohar [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]
  • Gi Hyun Bang [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]
  • Do Kyeong Lee [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]
  • Kyungbaek Kim [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]
  • Deokjai Choi [ School of Electronics and Computer Engineering, Chonnam National University Gwangju, South Korea ]

참고문헌

자료제공 : 네이버학술정보

간행물 정보

발행기관

  • 발행기관명
    보안공학연구지원센터(IJSEIA) [Science & Engineering Research Support Center, Republic of Korea(IJSEIA)]
  • 설립연도
    2006
  • 분야
    공학>컴퓨터학
  • 소개
    1. 보안공학에 대한 각종 조사 및 연구 2. 보안공학에 대한 응용기술 연구 및 발표 3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최 4. 보안공학 기술의 상호 협조 및 정보교환 5. 보안공학에 관한 표준화 사업 및 규격의 제정 6. 보안공학에 관한 산학연 협동의 증진 7. 국제적 학술 교류 및 기술 협력 8. 보안공학에 관한 논문지 발간 9. 기타 본 회 목적 달성에 필요한 사업

간행물

  • 간행물명
    International Journal of Software Engineering and Its Applications
  • 간기
    월간
  • pISSN
    1738-9984
  • 수록기간
    2008~2016
  • 등재여부
    SCOPUS
  • 십진분류
    KDC 505 DDC 605

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