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Impact of Social Media on Real Estate Sales

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The Ecosystem of e-Business: Technologies, Stakeholders, and Connections (WEB 2018)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 357))

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Abstract

More and more businesses are using social media to promote services and increase sales. This paper explores the impact of Facebook on real estate sales. First, we examine how Facebook activities are associated with real estate sales. Then, we include time lags in our analysis, because a time lag can be expected between the activates on Facebook and a resulting real estate transaction. The results suggest that: (1) The total numbers of Facebook Likes, links, and stories are positively associated with real estate sales; (2) The sentiment score of Facebook posts is negatively associated with real estate sales; (3) Time lag affects the impact of Facebook activities on real estate sales. The results reveal the predicting value of social media and the power of selected Facebook variables on real estate sales. The research findings can be used to promote sale and forecasting.

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References

  1. He, W., Zha, S., Li, L.: Social media competitive analysis and text mining: a case study in the pizza industry. Int. J. Inf. Manag. 33(3), 464–472 (2013)

    Article  Google Scholar 

  2. Andzulis, J.M., Panagopoulos, N.G., Rapp, A.: A review of social media and implications for the sales process. J. Pers. Selling Sales Manag. 32(3), 305–316 (2012)

    Article  Google Scholar 

  3. Wijnhoven, F., Plant, O.: Sentiment analysis and google trends data for predicting car sales. In: 38th International Conference on Information Systems 2017 (2017)

    Google Scholar 

  4. Kapoor, K.K., Tamilmani, K., Rana, N.P., Patil, P., Dwivedi, Y.K., Nerur, S.: Advances in social media research: past, present and future. Inf. Syst. Frontiers 20(3), 531–558 (2018)

    Article  Google Scholar 

  5. Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of twitter data. In: Proceedings of the Workshop on Languages in Social Media, pp. 30–38. Association for Computational Linguistics, Stroudsburg, PA (2011)

    Google Scholar 

  6. Asur, S., Huberman, B.A.: Predicting the future with social media. In: Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 01, pp. 492–499. IEEE Computer Society, Washington, DC (2010)

    Google Scholar 

  7. Gruhl, D., Guha, R., Kumar, R., Novak, J., Tomkins, A.: The predictive power of online chatter. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 78–87. ACM, New York (2005)

    Google Scholar 

  8. Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2(1), 1–8 (2011)

    Article  Google Scholar 

  9. Bartov, E., Faurel, L., Mohanram, P.S.: Can Twitter help predict firm-level earnings and stock returns? Acc. Rev. 93(3), 25–57 (2017)

    Article  Google Scholar 

  10. Joshi, M., Das, D., Gimpel, K., Smith, N.A.: Movie reviews and revenues: an experiment in text regression. In: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 293–296. Association for Computational Linguistics, Stroudsburg, PA (2010)

    Google Scholar 

  11. Wu, L., Brynjolfsson, E.: The future of prediction: how Google searches foreshadow housing prices and sales. Economic Analysis of the Digital Economy, pp. 89–118. University of Chicago Press, Chicago (2015)

    Chapter  Google Scholar 

  12. Schoen, H., Gayo-Avello, D., Takis Metaxas, P., Mustafaraj, E., Strohmaier, M., Gloor, P.: The power of prediction with social media. Internet Res. 23(5), 528–543 (2013)

    Article  Google Scholar 

  13. Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends® Inf. Retrieval 2((1–2)), 1–135 (2008)

    Google Scholar 

  14. Liu, B.: Sentiment analysis and opinion mining. Synth. Lect. Hum. Lang. Technol. 5(1), 1–167 (2012)

    Article  Google Scholar 

  15. Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5(4), 1093–1113 (2014)

    Article  Google Scholar 

  16. Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, vol. 10, pp. 79–86. Association for Computational Linguistics, Stroudsburg, PA (2002)

    Google Scholar 

  17. Naik, M.V., Vasumathi, D., Siva Kumar, A.P.: An enhanced unsupervised learning approach for sentiment analysis using extraction of Tri-Co-Occurrence words phrases. In: Bhateja, V., Tavares, J.M.R.S., Rani, B.P., Prasad, V.K., Raju, K.S. (eds.) Proceedings of the Second International Conference on Computational Intelligence and Informatics. AISC, vol. 712, pp. 17–26. Springer, Singapore (2018). https://doi.org/10.1007/978-981-10-8228-3_3

    Chapter  Google Scholar 

  18. Pak, A., Paroubek, P.: Twitter as a corpus for sentiment analysis and opinion mining. In: Proceedings of the Seventh Conference on International Language Resources and Evaluation (LREC 2010), pp. 1320–1326. European Languages Resources Association (2010)

    Google Scholar 

  19. Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)

    Article  Google Scholar 

  20. Nakov, P., Ritter, A., Rosenthal, S., Sebastiani, F., Stoyanov, V.: SemEval-2016 task 4: sentiment analysis in Twitter. In: Proceedings of the 10th International Workshop on Semantic Evaluation, pp. 1–18, Association for Computational Linguistics (2016)

    Google Scholar 

  21. Severyn, A., Moschitti, A.: Twitter sentiment analysis with deep convolutional neural networks. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 959–962. ACM, New York (2015)

    Google Scholar 

  22. Saif, H., He, Y., Fernandez, M., Alani, H.: Contextual semantics for sentiment analysis of Twitter. Inf. Proc. Manag. 52(1), 5–19 (2016)

    Article  Google Scholar 

  23. Pandey, A.C., Rajpoot, D.S., Saraswat, M.: Twitter sentiment analysis using hybrid cuckoo search method. Inf. Proc. Manag. 53(4), 764–779 (2017)

    Article  Google Scholar 

  24. Ortigosa, A., Martín, J.M., Carro, R.M.: Sentiment analysis in Facebook and its application to e-learning. Comput. Hum. Behav. 31, 527–541 (2014)

    Article  Google Scholar 

  25. Meire, M., Ballings, M., Van den Poel, D.: The added value of social media data in B2B customer acquisition systems: A real-life experiment. Decis. Support Syst. 104, 26–37 (2017)

    Article  Google Scholar 

  26. Cambria, E.: Affective computing and sentiment analysis. IEEE Intell. Syst. 31(2), 102–107 (2016)

    Article  Google Scholar 

  27. Poecze, F., Ebster, C., Strauss, C.: Social media metrics and sentiment analysis to evaluate the effectiveness of social media posts. Procedia Comput. Sci. 130((C)), 660–666 (2018)

    Article  Google Scholar 

  28. Nguyen, T.H., Shirai, K., Velcin, J.: Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42(24), 9603–9611 (2015)

    Article  Google Scholar 

  29. Fang, X., Zhan, J.: Sentiment analysis using product review data. J. Big Data 2(1), 5 (2015)

    Article  Google Scholar 

  30. Boldt, L.C., et al.: Forecasting nike’s sales using Facebook data. In: 2016 IEEE International Conference on Big Data, pp. 2447–2456. IEEE (2016)

    Google Scholar 

  31. Lee, K., Lee, B., Oh, W.: Thumbs up, sales up? the contingent effect of Facebook likes on sales performance in social commerce. J. Manag. Inf. Syst. 32(4), 109–143 (2015)

    Article  Google Scholar 

  32. Mazzucchelli, A., Chierici, R., Ceruti, F., Chiacchierini, C., Godey, B., Pederzoli, D.: Affecting brand loyalty intention: the effects of UGC and shopping searches via Facebook. J. Glob. Fashion Mark. 9(3), 270–286 (2018)

    Article  Google Scholar 

  33. Geetha, M., Singha, P., Sinha, S.: Relationship between customer sentiment and online customer ratings for hotels-an empirical analysis. Tourism Manag. 61, 43–54 (2017)

    Article  Google Scholar 

  34. Dini, L., Bittar, A., Robin, C., Segond, F., Montaner, M.: SOMA: The Smart Social Customer Relationship Management Tool: Handling Semantic Variability of Emotion Analysis With Hybrid Technologies. Sentiment Analysis in Social Networks, pp. 197–209 (2017)

    Google Scholar 

  35. He, W., Chen, Y.: Using blog mining as an analytical method to study the use of social media by small businesses. J. Inf. Technol. Case Appl. Res. 16(2), 91–104 (2014)

    Google Scholar 

  36. Pedhazur, E.J., Kerlinger, F.N.: Multiple Regression in Behavioral Research. Holt, Rinehart and Winston, New York (1973)

    Google Scholar 

  37. Bing, L., Chan, K.C.C., Ou, C.: Public sentiment analysis in Twitter data for prediction of a company’s stock price movements. In: 2014 IEEE 11th International Conference on E-Business Engineering, pp. 232–239. IEEE (2014)

    Google Scholar 

  38. Kotler, P.J.: Marketing Management: Analysis, Planning, Implementation, and Control, 8th edn. Prentice Hall, Englewood Cliffs (1994)

    Google Scholar 

  39. Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., McClosky, D.: The stanford coreNLP natural language processing toolkit. In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 55–60. Association for Computational Linguistics, Stroudsburg, PA (2014)

    Google Scholar 

  40. Myers, R.H., Myers, R.H.: Classical and Modern Regression With Applications, vol. 2. Duxbury Press, Belmont (1990)

    MATH  Google Scholar 

  41. Studenmund, A.H.: Using Econometrics: A Practical Guide (5 th). Pearson Education Inc, Boston (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. California State Polytechnic University, Pomona, USA

    Hui Shi & Zhongming Ma

  2. California Lutheran University, Thousand Oaks, USA

    Dazhi Chong

  3. Old Dominion University, Nofrolk, USA

    Wu He

Authors
  1. Hui Shi
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  2. Zhongming Ma
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  3. Dazhi Chong
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  4. Wu He
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Corresponding author

Correspondence to Hui Shi .

Editor information

Editors and Affiliations

  1. Bentley University, Waltham, MA, USA

    Jennifer J. Xu

  2. Oregon State University, Corvallis, OR, USA

    Bin Zhu

  3. University of Utah, Salt Lake City, UT, USA

    Xiao Liu

  4. University of Illinois, Urbana-Champaign, USA

    Michael J. Shaw

  5. Georgia Institute of Technology, Atlanta, GA, USA

    Han Zhang

  6. University of Washington, Seattle, WA, USA

    Ming Fan

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Shi, H., Ma, Z., Chong, D., He, W. (2019). Impact of Social Media on Real Estate Sales. In: Xu, J., Zhu, B., Liu, X., Shaw, M., Zhang, H., Fan, M. (eds) The Ecosystem of e-Business: Technologies, Stakeholders, and Connections. WEB 2018. Lecture Notes in Business Information Processing, vol 357. Springer, Cham. https://doi.org/10.1007/978-3-030-22784-5_1

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  • DOI: https://doi.org/10.1007/978-3-030-22784-5_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22783-8

  • Online ISBN: 978-3-030-22784-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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