The Journal of the Korea Contents Association (한국콘텐츠학회논문지)

The Korea Contents Association (한국콘텐츠학회)
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Analysis of Research Trends on Social Network Service: focusing on the Studies of Twitter

소셜 네트워크 서비스의 연구경향 분석: Twitter관련 연구 중심

  • 하일규 (영남대학교 컴퓨터공학과)
  • Received : 2014.07.31
  • Accepted : 2014.08.12
  • Published : 2014.09.28
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Abstract

Recently, with the introduction of social network services, studies that try to make use of them for the various purposes have been actively developed. In order to proceed with the research that takes advantage of social network services, it is necessary to review the relevant literature and to identify trends in researches. However, the researches of social network service since 2007 are massive amount, so to review the huge amount of relevant research literature is a very difficult task. Therefore, in this study, we analyze systematically the tendency of research related to social network service focusing on Twitter. Especially, we use the SLR(Systematic Literature Review) technique for systematic literature survey and analysis. For the literature survey, we select five well-known literature resource sites and 128 studies of literature that are surveyed. In order to identify various research trends, we conduct the survey with two research groups: researches since 2007 and researches since 2011. As a result of the investigation, since 2007, the researches associated with "Application", "User Activity" and "User Content Analysis" main study topics have been mostly carried out. In addition to the result, the trend of secondary study topics in a main study topic, trends in research based on the number of citations and the scale of the experimental data and characteristics of the author are analyzed from a variety of perspectives.

최근 들어, 소셜 네트워크 서비스(Social Network Service)의 도입과 함께 이를 다양한 목적으로 활용하고자 하는 연구가 활발하게 진행되고 있다. 소셜 네트워크 서비스 활용을 위한 연구를 진행하기 위해서는 우선 관련 문헌을 검토하고 연구 경향을 파악하는 것이 필요하다. 하지만, 소셜 네트워크 서비스가 보편화된 2007년 이후부터 진행된 관련 연구는 그 양이 방대하여 많은 양의 관련 연구 문헌을 조사하는 것은 상당히 어려운 작업이다. 따라서 본 연구에서는 소셜 네트워크 서비스 중 트위터를 중심으로 관련 연구들을 체계적으로 분석하여 연구의 경향성을 밝힌다. 특히 체계적인 문헌 조사와 분석을 위해 SLR(Systematic Literature Review) 기법을 이용한다. 문헌 조사를 위해 5개의 잘 알려진 문헌 제공처를 선정하고, 128개의 문헌 조사 대상 연구를 선정하였다. 다양한 경향성을 파악하기 위하여 2007년 이후 연구들의 그룹과 2011년 이후 연구들의 그룹으로 나누어 조사를 실시하였다. 조사 결과 2007년 이후 트위터 관련 연구는 "Application", "User Content Analysis" 및 "User Activity" 주요 연구주제와 관련한 연구가 가장 많이 이루어지고 있었다. 이외에도 주요 연구주제별 세부 연구주제의 경향, 피인용수에 따른 연구 경향, 실험자료의 규모에 따른 연구경향 및 저자의 특성 등 다양한 관점에서 결과를 분석한다.

Keywords

References

  1. WIKIPEDIA: http://www.wikipedia.org/ (accessed May 2, 2014)
  2. H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, a social network or a news media?," in Proceeding of the 19th international conference on World wide web, pp.591-600, 2010.
  3. B. Kitchenham, "Procedures for Undertaking Systematic Reviews," Joint Technical Report, Computer Science Department, Keele University (TR/SE-0401) and National ICT Australia Ltd. (0400011T.1), 2004.
  4. F. Lucas, F. Molina, and A. Toval, "A systematic review of UML model consistency management," Information and Software Technology, Vol.51, pp.1631-1645, 2009. https://doi.org/10.1016/j.infsof.2009.04.009
  5. D. Budgen, A. J. Burn, O. P. Brereton, B. A. Kitchenham, and R. Pretorius, "Empirical evidence about the UML: a systematic literature review," Software: Practice and Experience, Vol.41, Iss.4, pp.363-392, 2011. https://doi.org/10.1002/spe.1009
  6. A. Java, X. Song, T. Finin, and B. Tseng, "Why we twitter: understanding microblogging usage and communities," in Proceedings of the 9th WebKDD, pp.56-65, 2007.
  7. A. Tumasjan, T. Sprenger, P. Sandner, and I. Welpe, "Predicting elections with twitter: What 140 characters reveal about political sentiment," in Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp.178-185, 2010.
  8. M. Conover, J. Ratkiewicz, M. Francisco, B. Goncalves, A. Flammini, and F. Menczer, "Political polarization on twitter," in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp.89-96, 2011.
  9. J. Bollen, H. Mao, and X. Zeng, "Twitter mood predicts the stock market," Journal of Computational Science, Vol.2, Iss.1, pp.1-8, 2011. https://doi.org/10.1016/j.jocs.2010.12.007
  10. J. Yoon and B. Hwang, "Design of a Productivity Measuring System for Twitter Marketing Reflecting User"s Sentiment in Social Networks," Journal of KISS: Database, Vol.40, No.6, pp.377-385, 2013.
  11. R. Junco, G. Heiberger, and E. Loken, "The effect of Twitter on college student engagement and grades," Journal of Computer Assited Learning, Vol.27, Iss.2, pp.119-132, 2011. https://doi.org/10.1111/j.1365-2729.2010.00387.x
  12. C. Hawn, "Take two aspirin and tweet me in the morning: how Twitter, Facebook, and other social media are reshaping health care," Health Affairs, Vol.28, No.2, pp.361-368, 2009. https://doi.org/10.1377/hlthaff.28.2.361
  13. C. Castillo, M. Mendoza, B. Poblete, "Information credibility on twitter," in Proceedings of the 20th international conference on World wide web, pp.675-684, 2011.
  14. A. Teutle, "Twitter: Network properties analysis," in Proceeding of 20th International Conference on Electronics, Communications and Computer(CONIELECOMP), pp.180-186, 2010.
  15. M. Cataldi, L. Caro, and C. Schifanella, "Emerging topic detection on twitter based on temporal and social terms evaluation," in Proceedings of the Tenth International Workshop on Multimedia Data Mining, 2010.
  16. D. Boyd, S. Golder, and G. Lotan, "Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter," in Proceeding of 43rd Hawaii International Conference on System Sciences (HICSS), pp.1-10, 2010.
  17. H. Kwak, H. Chun, and S. Moon, "Fragile online relationship: a first look at unfollow dynamics in twitter," in Proceeding of the SIGCHI Conference on Human Factors in Computing Systems, pp.1091-1100, 2011.
  18. M. Mathioudaks, and N. Koudas, "TwitterMonitor: trend detection over the twitter stream," in Proceeding of the 2010 ACM SIGMOD International Conference on Management of data, pp.1155-1158, 2010.
  19. H. Becker, M. Naaman, and L. Gravano, "Beyond trending topics: Real-world event identification on Twitter," in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp.438-441, 2011.
  20. Z. Cheng, J. Caverlee, and K. Lee, "You are where you tweet: a content-based approach to geo-locating twitter users," in Proceedings of the 19th ACM international conference on Information and knowledge management, pp.759-768, 2010.
  21. A. Pak, and P. Paroubek, "Twitter as a corpus for sentiment analysis and opinion mining," in Proceeding of 7th international conference on Language Resources and Evaluation (LREC 2010), pp.1320-1326, 2010.
  22. J. Bollen, H. Mao, and A. Pepe, "Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena," in Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media, pp.450-453, 2011.
  23. S. Yardi, D. Romero, G. Schoenebeck, and D. boyd, "Detecting spam in a twitter network," First Monday, Vol.15, No.1-4, 2010.
  24. D. Romero, B. Meeder, and J. Kleinberg, "Differences in the mechanics of information diffusion across topics: idioms, political hashtags, and complex contagion on twitter," in Proceedings of the 20th international conference on World wide web, pp.695-704, 2011.
  25. X. Liu, S. Zhang, F. Wei, and M. Zhou, "Recognizing named entities in tweets," in Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp.359-367, 2011.
  26. M. Perc, "Modeling Users' Activity on Twitter Networks: Validation of Dunbar's Number," PLoS ONE, Vol.6, No.8, e22656, 2011. https://doi.org/10.1371/journal.pone.0022656
  27. K. Lerman and R. Ghosh, "Information contagion: An empirical study of the spread of news on Digg and Twitter social networks," in Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, pp.90-97, 2010.
  28. G. Lotan, E. Graeff, M. Ananny, D. Gaffney, I. Pearce, and D. Boyd, "The Revolutions Were Tweeted: Information Flows During the 2011 Tunisian and Egyptian Revolutions," International Journal of Communication, Vol.5, pp.1375-1405, 2011.
  29. W. Reinhardt, M. Ebner, G. Beham, C. Costa, "How people are using Twitter during conferences," in Proceeding of 5. EduMedia conference, pp.145-156, 2009.
  30. E. Bakshy, J. Hofman, W. Mason, and D. Watts, "Everyone's an influencer: quantifying influence on twitter," in Proceedings of the fourth ACM international conference on Web search and data mining, pp.65-74, 2011.
  31. S. Wu, J. Hofman, W. Mason, and D. Watts, "Who says what to whom on twitter," in Proceedings of the 20th international conference on World wide web, pp.705-714, 2011.
  32. Y. Takhteyev, A. Gruzd, and B. Wellman, "Geography of Twitter networks," Social Networks, Vol.34, Iss.1, pp.73-81, 2012. https://doi.org/10.1016/j.socnet.2011.05.006
  33. G. Lee and K. Lee, "Twitter Sentiment Analysis for the Recent Trend Extracted from the Newspaper Article," KIPS transactions o software and data engineering, Vol.2, No.10, pp.731-738, 2013 https://doi.org/10.3745/KTSDE.2013.2.10.731
  34. IEEE Xplore: http://ieeexplore.ieee.org/(accessed May 2, 2014)
  35. ACM Digital Library:http://dl.acm.org/(accesse d May 2, 2014)
  36. Google Scholar: http://scholar.google.co.kr/(acc essed May 2, 2014)
  37. Science Direct: http://www.sciencedirect.com/(acc essed May 2, 2014)
  38. Web of Science: http://apps.webofknowledge.com/(accessed May 2, 2014)
  39. O. Phelan, K. McCarthy, and B. Smyth, "Using Twitter to Recommend Real-Time Topical News," in Proceedings of the third ACM conference on Recommender systems, pp.385-388, 2009
  40. T. Sakaki, M. Okazaki, and Y. Matsuo, "Earthquake shakes Twitter users: real-time event detection by social sensors," in Proceedings of the 19th international conference on world wide web, pp.851-860, 2010