본문 바로가기
KERD
논문KCI 등재

Relations between Reputation and Social Media Marketing Communication in Cryptocurrency Markets: Visual Analytics using Tableau

0

조회

0

다운로드

0

인용

0

참고문헌

키워드

주제분류컴퓨터학

초록

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins’ prices were high. Emotional language strategies on social media did not affect cryptocurrencies’ reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

Abstract

Visual analytics is an emerging research field that combines the strength of electronic data processing and human intuition-based social background knowledge. This study demonstrates useful visual analytics with Tableau in conjunction with semantic network analysis using examples of sentiment flow and strategic communication strategies via Twitter in a blockchain domain. We comparatively investigated the sentiment flow over time and language usage patterns between companies with a good reputation and firms with a poor reputation. In addition, this study explored the relations between reputation and marketing communication strategies. We found that cryptocurrency firms more actively produced information when there was an increased public demand and increased transactions and when the coins’ prices were high. Emotional language strategies on social media did not affect cryptocurrencies’ reputations. The pattern in semantic representations of keywords was similar between companies with a good reputation and firms with a poor reputation. However, the reputable firms communicated on a wide range of topics and used more culturally focused strategies, and took more advantages of social media marketing by expanding their outreach to other social media networks. The visual big data analytics provides insights into business intelligence that helps informed policies.

참고문헌 (37)

  1. Mike Savage, "The Coming Crisis of Empirical Sociology", Sociology, 41(5), pp.885~899 (2007), SAGE Publications[DOI]
  2. Anthony G. Greenwald, "There Is Nothing So Theoretical as a Good Method", Perspectives on Psychological Science, 7(2), pp.99~108 (2012), SAGE Publications[DOI]
  3. D. A. Keim, "Visual analytics: Scope and challenges, Visual data mining", pp.76~90 (2008), Springer
  4. Han Woo Park, "How Are Twitter Activities Related to Top Cryptocurrencies' Performance? Evidence from Social Media Network and Sentiment Analysis", Drustvena istrazivanja, 28(3), pp.435~460 (2019), Institute of Social Sciences Ivo Pilar[DOI]
  5. Franz Wanner, "Integrated visual analysis of patterns in time series and text data - Workflow and application to financial data analysis", Information Visualization, 15(1), pp.75~90 (2016), SAGE Publications[DOI]
  6. D. G. Murray, "Tableau your data!: Fast and easy visual analysis with tableau software" (2013)
  7. Christian Chabot, "Demystifying Visual Analytics", IEEE Computer Graphics and Applications, 29(2), pp.84~87 (2009), Institute of Electrical and Electronics Engineers (IEEE)[DOI]
  8. R. J. Shiller, "Irrational exuberance: Revised and expanded" (2015), Princeton university press
  9. Soosung Hwang, "Market stress and herding", Journal of Empirical Finance, 11(4), pp.585~616 (2004), Elsevier BV[DOI]
  10. E. Bouri, "Herding behavior in the cryptocurrency market" (2018), University of Pretoria
  11. Young Bin Kim, "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies", PLOS ONE, 11(8), pp.e0161197~ (2016), Public Library of Science (PLoS)[DOI]
  12. D. Kahneman, "Prospect Theory: An Analysis of Decision under Risk", Econometrica, 47(2), pp.263~292 (1979)
  13. R. J. Dolan, "Emotion, Cognition, and Behavior", Science, 298(5596), pp.1191~1194 (2002), American Association for the Advancement of Science (AAAS)[DOI]
  14. J. Abraham, "Cryptocurrency price prediction using tweet volumes and sentiment analysis", SMU Data Science Review, 1(3), pp.1~22 (2018)
  15. F. Mai, "The impacts of social media on Bitcoin performance", ICIS2015 Proceedings, pp.1~46 (2015)
  16. M. Matta, "Bitcoin spread prediction using social and web search media", UMAP Workshops (2015)
  17. Olivier Kraaijeveld, "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices", Journal of International Financial Markets, Institutions and Money, 65(C), pp.101188~ (2020), Elsevier BV[DOI]
  18. Sejung Park, "Diffusion of cryptocurrencies: web traffic and social network attributes as indicators of cryptocurrency performance", Quality & Quantity, 54(1), pp.297~314 (2020), Springer Science and Business Media LLC[DOI]
  19. James R. Barth, "Cryptocurrency valuation and ethics: a text analytic approach", Journal of Management Analytics, 7(3), pp.367~388 (2020), Informa UK Limited[DOI]
  20. David Garcia, "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy", Journal of The Royal Society Interface, 11(99), pp.20140623~ (2014), The Royal Society[DOI]
  21. Eaman Jahani, "ScamCoins, S*** Posters, and the Search for the Next Bitcoin TM", Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), pp.1~28 (2018), Association for Computing Machinery (ACM)[DOI]
  22. N. Stråle Johansson, "The Price Volatility of Bitcoin: A search for the drivers affecting the price volatility of this digital currency", Master, pp.2014
  23. Tim Loughran, "IPO first-day returns, offer price revisions, volatility, and form S-1 language", Journal of Financial Economics, 109(2), pp.307~326 (2013), Elsevier BV[DOI]
  24. M. Risius, "Differential emotions and the stock market-the case of company-specific trading", Proceedings of the European Conference on Information Systems (2015)
  25. John R. Nofsinger, "Social Mood and Financial Economics", Journal of Behavioral Finance, 6(3), pp.144~160 (2005), Informa UK Limited[DOI]
  26. J. Zhang, "Do cryptocurrency markets react to issuer sentiments? Evidence from Twitter", Evidence from Twitter SSRN, 1~29 (2020)
  27. Sejung Park, "A webometric network analysis of electronic word of mouth (eWOM) characteristics and machine learning approach to consumer comments during a crisis", El profesional de la información, 29(5), pp.1~14 (2020), Ediciones Profesionales de la Informacion SL[DOI]
  28. "Weiss cryptocurrency ratings explained. 1"[URL]
  29. "Big data and social web research methods" (2014)[URL]
  30. Mike Thelwall, "Sentiment strength detection for the social web", Journal of the American Society for Information Science and Technology, 63(1), pp.163~173 (2012), Wiley[DOI]
  31. S. Park, "Expanding the presidential debate by tweeting : The 2012presidential election debate in South Korea", Telematics and Informatics, 33(2), pp.557~569 (2016)[DOI]
  32. "Textstat" (2002)[URL]
  33. Loet Leydesdorff, "The semantic mapping of words and co-words in contexts", Journal of Informetrics, 5(3), pp.469~475 (2011), Elsevier BV[DOI]
  34. Stephen P Borgatti, "Models of core", periphery structures, 2000, pp.21(4), 375~395[DOI]
  35. N. Jerdack, "Understanding what drives bitcoin trading activities", Proceedings of the 2018 Annual Meeting of the Decision Sciences Institute, pp.1864~1872 (2018)
  36. Sejung Park, "Tweeting about mental health to honor Carrie Fisher: How #InHonorOfCarrie reinforced the social influence of celebrity advocacy", Computers in Human Behavior, 110, pp.106353~ (2020), Elsevier BV[DOI]
  37. "Top 5 Marijuana cryptocurrencies" (2019)[URL]

관련 논문AI추천

이 간행물의 다른 논문 (6건)