Advertisement

Information Retrieval Journal

, Volume 21, Issue 5, pp 410–448 | Cite as

An artist ranking system based on social media mining

  • Amalia F. Foka
Article
  • 278 Downloads

Abstract

Currently users on social media post their opinion and feelings about almost everything. This online behavior has led to numerous applications where social media data are used to measure public opinion in a similar way as a poll or a survey. In this paper, we will present an application of social media mining for the art market. To the best of our knowledge, this will be the first attempt to mine social media to extract quantitative and qualitative data for the art market. Although there are previous works on analyzing and predicting other markets, these methodologies cannot be applied directly to the art market. In our proposed methodology, artists will be treated as brands. That is, we will mine Twitter posts that mention specific artists’ names and attempt to rank artists in a similar manner as brand equity and awareness would be measured. The particularities of the art market are considered mainly in the construction of a topic-specific user network where user expertise and influence is evaluated and later used to rank artists. The proposed ranking system is evaluated against two other available systems to identify the advantages it can offer.

Keywords

Social media mining Art market Social media user network User expertise User influence Topic identification 

References

  1. Abbing, H. (2002). Why are artists poor? The exceptional economy of the arts. Amsterdam: Amsterdam University Press.Google Scholar
  2. AMMA & artpricecom. (2016). The art market in 2015. http://imgpublic.artprice.com/pdf/rama2016_en.pdf. November 1, 2016.
  3. Andéhn, M., Kazeminia, A., Lucarelli, A., & Sevin, E. (2014). User-generated place brand equity on twitter: The dynamics of brand associations in social media. Place Branding and Public Diplomacy, 10(2), 132–144.  https://doi.org/10.1057/pb.2014.8.CrossRefGoogle Scholar
  4. Arias, M., Arratia, A., & Xuriguera, R. (2014). Forecasting with twitter data. ACM Transactions on Intelligent Systems and Technology, 5(1), 8:1–8:24.  https://doi.org/10.1145/2542182.2542190.Google Scholar
  5. Artfacts. (2016a). Artfacts analysis page for frank stella accessible without a subscription. http://www.artfacts.net/index.php/pageType/career_analyser/artist/23/ lang/1. October 31, 2016.
  6. Artfacts. (2016b). Artfacts artist ranking—top 100. http://www.artfacts.net/en/ artists/ top100.html. October 31, 2016.
  7. Artfacts. (2016c). Artfacts homepage. http://www.artfacts.net/. October 31, 2016.
  8. Artnet. (2016). Artnet homepage. http://www.artnet.com. October 31, 2016.
  9. ArtNexus. (2016). Artnexus auction results. http://artnexus.com/Auctions.aspx. November 3, 2016.
  10. Artprice. (2016). artprice homepage. http://www.artprice.com. October 31, 2016.
  11. ArtReview. (2015). 2015 power 100. https://artreview.com/power_100/2015/. November 6, 2016.
  12. ArtReview. (2016). 2016 power 100. https://artreview.com/power_100/. November 6, 2016.
  13. Artsy. (2015). Artsy editorial: The 15 most influential art world cities of 2015. https://www.artsy.net/article/artsy-editorial-contemporary-art-s-most-influential-cities. November 6, 2016.
  14. Becker, H. S. (1982). Art worlds. Berkeley: University of California Press.Google Scholar
  15. Bier, A. (2016). Artsy editorial: The 20 most influential young curators in Europe. https://www.artsy.net/article/artsy-editorial-the-20-most-influential-young-curators-in-europe. November 6, 2016.
  16. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3(Jan), 993–1022.zbMATHGoogle Scholar
  17. BlouinArtinfo. (2016). Blouin art sales index. http://artsalesindex.artinfo.com/. November 3, 2016.
  18. Boll, D. (2011). Art for sale. Berlin: Hatje Cantz.Google Scholar
  19. Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Journal of Computational Science, 2(1), 1–8.  https://doi.org/10.1016/j.jocs.2010.12.007.CrossRefGoogle Scholar
  20. Cha, M., Haddadi, H., Benevenuto, F., Gummadi, K. P., (2010). Measuring user influence in twitter: The million follower fallacy. In ICWSM 10: Proceedings of international AAAI conference on weblogs and social.Google Scholar
  21. Cheng, X., Yan, X., Lan, Y., & Guo, J. (2014). Btm: Topic modeling over short texts. IEEE Transactions on Knowledge and Data Engineering, 26(12), 2928–2941.  https://doi.org/10.1109/TKDE.2014.2313872.CrossRefGoogle Scholar
  22. Chung, A. D. (2015). Brand personality research on twitter. Master thesis, The University of Texas at Austin.Google Scholar
  23. Culotta, A., & Cutler, J. (2016). Mining brand perceptions from twitter social networks. Marketing Science, 35(3), 343–362.CrossRefGoogle Scholar
  24. Danto, A. (1964). The artworld. The Journal of Philosophy, 61(19), 571–584. http://www.jstor.org/stable/2022937.
  25. FindArtInfo. (2016). Findartinfo homepage. http://www.findartinfo.com. November 3, 2016.
  26. Gayo-Avello, D. (2013). Nepotistic relationships in twitter and their impact on rank prestige algorithms. Information Processing & Management, 49(6), 1250–1280.  https://doi.org/10.1016/j.ipm.2013.06.003.CrossRefGoogle Scholar
  27. Gotthardt, A. (2016). Artsy editorial: The 20 most influential young curators in the united states. https://www.artsy.net/article/artsy-editorial-the-20-most-influential-young-curators-in-the-united-states. November 6, 2016.
  28. Grampp, W. D. (1989). Pricing the priceless: Art, artists and economics. New York: Basic Books.Google Scholar
  29. Graw, I. (2010). High price: Art between the market and cebrity culture. Berlin: Sternberg Press.Google Scholar
  30. He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464–472.  https://doi.org/10.1016/j.ijinfomgt.2013.01.001.CrossRefGoogle Scholar
  31. Hofmann, T. (1999). Probabilistic latent semantic indexing. In Proceedings of the 22nd annual international acm sigir conference on research and development in information retrieval, SIGIR ’99 (pp. 50–57). New York, NY: ACM.  https://doi.org/10.1145/312624.312649.
  32. Hong, L., Davison, B. D. (2010). Empirical study of topic modeling in twitter. In Proceedings of the first workshop on social media analytics, SOMA ’10 (pp. 80–88). New York, NY: ACM.  https://doi.org/10.1145/1964858.1964870.
  33. Jin, X., Gallagher, A., Cao, L., Luo, J., Han, J. (2010). The wisdom of social multimedia: Using flickr for prediction and forecast. In Proceedings of the 18th ACM international conference on multimedia, MM ’10 (pp. 1235–1244). New York, NY: ACM.  https://doi.org/10.1145/1873951.1874196.
  34. Kinsella, E. (2016). What does tefaf 2016 art market report tell us about the global art trade? https://news.artnet.com/market/tefaf-2016-art-market-report-443615. November 3, 2016.
  35. Leavitt, A., Burchard, E., Fisher, D., Gilbert. S. (2009). The influentials: New approaches for analyzing influence on twitter, technical report, web ecology project. http://www.webecologyproject.org/2009/09/ analyzing-influence-on-twitter/.
  36. Liu, L., Wu, J., Li, P., & Li, Q. (2015). A social-media-based approach to predicting stock comovement. Expert Systems with Applications, 42(8), 3893–3901.  https://doi.org/10.1016/j.eswa.2014.12.049.CrossRefGoogle Scholar
  37. Liu, Y., Kliman-Silver, C., Mislove, A. (2014). The tweets they are a-changin: Evolution of twitter users and behavior. In International AAAI conference on web and social media.Google Scholar
  38. Luo, X., Zhang, J., & Duan, W. (2013). Social media and firm equity value. Information Systems Research, 24(1), 146–163.  https://doi.org/10.1287/isre.1120.0462.CrossRefGoogle Scholar
  39. Degen, N. (Ed.). (2013). The market, whitechapel documents of contemporary art. Cambridge: The MIT Press.Google Scholar
  40. Oliveira, N., Cortez, P., Areal, N. (2013). On the predictability of stock market behavior using stocktwits sentiment and posting volume. In L. Correia, L. P. Reis & J. Cascalho (Eds.), Progress in artificial intelligence: 16th Portuguese conference on artificial intelligence, EPIA 2013, Angra do Heroísmo, Azores, Portugal, Proceedings, 9–12 September 2013 (pp. 355–365). Berlin Heidelberg: Springer .Google Scholar
  41. Peetz, M. H. (2015). Time-aware online reputation analysis. Phd thesis, University of Amsterdam.Google Scholar
  42. Periferic Biennial. (2008). Interview with Marek Claassen, the director of artfacts.net by Zsuzsa Laszlo. https://perifericbiennial.wordpress.com/2008/10/20/interview-with-with-marek-claassen-the-director-of-artfactsnet/. November 3, 2016.
  43. Rehurek, R., Sojka, P. (2010). Software framework for topic modelling with large corpora. In Proceedings of the LREC 2010 workshop on new challenges for NLP frameworks (pp. 45–50).Google Scholar
  44. Roesslein, J. (2016). Tweepy version 3.5.0. http://www.tweepy.org/. November 5, 2016.
  45. Smailovic, J., Grčar, M., Lavrač, N., Žnidaršič, M. (2013). Predictive sentiment analysis of tweets: A stock market application. In A. Holzinger, & G. Pasi (Eds.), Human–computer interaction and knowledge discovery in complex, unstructured, big data: Third international workshop, HCI-KDD 2013, Held at SouthCHI 2013, Maribor, Slovenia, Proceedings, July 1–3 2013 (pp. 77–88). Berlin, Heidelberg: Springer.Google Scholar
  46. Statista. (2016). Most famous social network sites worldwide as of september 2016, ranked by number of active users (in millions). https://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/. November 3, 2016.
  47. Thompson, D. (2010). The $12 million stuffed shark: The curious economics of contemporary art. New York: St. Martin’s Griffin.Google Scholar
  48. Thornton, S. (2008). Seven days in the art world. New York: W. W: Norton & Company.Google Scholar
  49. Tucker, C. S. (2015). Quantifying product favorability and extracting notable product features using large scale social media data. Journal of Computing and Information Science in Engineering, 15, 031003.CrossRefGoogle Scholar
  50. Twitter Inc. (2016a). Api rate limits. https://dev.twitter.com/rest/public/rate-limiting. November 5, 2016.
  51. Twitter Inc. (2016b). Get statuses/firehose. https://dev.twitter.com/streaming/reference/get/statuses/firehose. November 5, 2016.
  52. Twitter Inc. (2016c). Get statuses/sample. https://dev.twitter.com/rest/public/search. November 5, 2016.
  53. Twitter Inc. (2016d). The search api. https://dev.twitter.com/rest/public/search. November 5, 2016.
  54. Twitter Inc. (2016e). Tweets. https://dev.twitter.com/overview/api/tweets. November 5, 2016.
  55. Twitter Inc. (2016f) Working with timelines. https://dev.twitter.com/rest/public/timelines. November 5, 2016.
  56. Velthuis, O. (2007). Talking prices: Symbolic meanings of prices on the market for contemporary art (4th ed.). Princeton: Princeton University Press.Google Scholar
  57. Weng, J., Lim, E. P., Jiang, J., He, Q. (2010). Twitterrank: Finding topic-sensitive influential twitterers. In Proceedings of the third ACM international conference on web search and data mining, WSDM ’10 (pp. 261–270). New York, NY: ACM.  https://doi.org/10.1145/1718487.1718520.
  58. Wikipedia. (2016). English wikipedia dump on 01/10/2016. https://dumps.wikimedia.org/enwiki/20161001/. October 1, 2016.
  59. Yang, C. C., Yang, H., Jiang, L., Zhang, M. (2012). Social media mining for drug safety signal detection. In Proceedings of the 2012 international workshop on smart health and wellbeing, SHB ’12 (pp. 33–40). New York, NY: ACM.  https://doi.org/10.1145/2389707.2389714.
  60. Yu, Y., Duan, W., & Cao, Q. (2013). The impact of social and conventional media on firm equity value: A sentiment analysis approach. 1. Social Media research and applications 2. theory and applications of social networks. Decision Support Systems, 55(4), 919–926.  https://doi.org/10.1016/j.dss.2012.12.028.CrossRefGoogle Scholar
  61. Zhao, W. X., Jiang, J., Weng, J., He, J., Lim, E. P., Yan, H., Li, X. (2011). Comparing twitter and traditional media using topic models. In Proceedings of the 33rd European conference on advances in information retrieval, ECIR’11 (pp 338–349). Berlin, Heidelberg: Springer.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Fine Arts & of the Sciences of ArtUniversity of IoanninaIoanninaGreece

Personalised recommendations