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Social Media Analytics

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Handbook of Marketing Decision Models

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 254))

Abstract

One of the most significant developments in the domain of marketing in recent years involves the proliferation of user-generated content, particularly online social media. Social media has created a power shift in the relationship between consumers and brands, providing consumers more power by allowing them to easily broadcast their views and opinions about brands to a large audience.

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Notes

  1. 1.

    For a review of research on word-of-mouth, we refer interested readers to Berger (2014).

  2. 2.

    F1 is measured as the harmonic mean of the levels of recall and precision, where recall is the proportion of instances that were identified, and precision is the proportion of correctly identified instances of the set of identified instances.

  3. 3.

    For more information about NLP, we refer interested readers to Manning and Schütze (1999).

  4. 4.

    If the researcher is interested in sentiment analysis or other types of output rather than relationship extraction, Step 5 could be replaced with the eventual goal of the text-mining task. For example, if the researcher is interested in understanding which topics were mention in a review, step 5 may be replaced with a topic modeling approach.

  5. 5.

    For a review of the impact of online WOM on sales, we refer readers to the meta analyses conducted by Babic et al. (2016) and You et al. (2015).

References

  • Anderson, E.W. 1998. Customer satisfaction and word of mouth. Journal of Service Research 1 (1): 5–17.

    Article  Google Scholar 

  • Anderson, E.T., and D.I. Simester. 2014. Reviews without a purchase: Low ratings, loyal customers, and deception. Journal of Marketing Research 51 (3): 249–269.

    Article  Google Scholar 

  • Anderson, M., and J. Magruder. 2012. Learning from the crowd: Regression discontinuity estimates of the effects of an online review database. The Economic Journal 122 (563): 957–989.

    Article  Google Scholar 

  • Archak, N., A. Ghose, and P.G. Ipeirotis. 2011. Deriving the pricing power of product features by mining consumer reviews. Management Science 57 (8): 1485–1509.

    Article  Google Scholar 

  • Babic, A., F. Sotgiu, K. de Valck, and T.H. Bijmolt. 2016. The effect of electronic word of mouth on sales: A meta-analytic review of platform, product, and metric factors. Journal of Marketing Research 53 (3): 297–318.

    Google Scholar 

  • Berger, J. 2014. Word of mouth and interpersonal communication: A review and directions for future research. Journal of Consumer Psychology 24 (4): 586–607.

    Article  Google Scholar 

  • Berger, J., and K.L. Milkman. 2012. What makes online content viral? Journal of Marketing Research 49 (2): 192–205.

    Article  Google Scholar 

  • Bird, S., E. Loper, and E. Klein. 2009. Natural language processing with Python. O’Reilly Media Inc.

    Google Scholar 

  • Blei, D.M., A.Y. Ng, and M.I. Jordan. 2003. Latent dirichlet allocation. The Journal of Machine Learning Research 3: 993–1022.

    Google Scholar 

  • Bollen, J., H. Mao, and X. Zeng. 2011. Twitter mood predicts the stock market. Journal of Computational Science 2 (1): 1–8.

    Article  Google Scholar 

  • Börner, K., C. Chen, and K.W. Boyack. 2003. Visualizing knowledge domains. Annual Review of Information Science and Technology 37 (1): 179–255.

    Article  Google Scholar 

  • Büschken, J., and G.M. Allenby. 2016. Sentence-based text analysis for customer reviews. Marketing Science 35 (6): 953–975.

    Google Scholar 

  • Chevalier, J.A., and D. Mayzlin. 2006. The effect of word of mouth on sales: Online book reviews. Journal of Marketing Research 43 (3): 345–354.

    Article  Google Scholar 

  • Culotta, A. 2010. Towards detecting influenza epidemics by analyzing Twitter messages. In Proceedings of the first workshop on social media analytics, 115–122. ACM.

    Google Scholar 

  • Das, S.R., and M.Y. Chen. 2007. Yahoo! for Amazon: Sentiment extraction from small talk on the web. Management Science 53 (9): 1375–1388.

    Article  Google Scholar 

  • Dave, K., S. Lawrence, and D.M. Pennock. 2003. Mining the peanut gallery: Opinion extraction and semantic classification of product reviews. In Proceedings of the 12th international conference on World Wide Web, 519–528. ACM.

    Google Scholar 

  • Decker, R., and M. Trusov. 2010. Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing 27 (4): 293–307.

    Article  Google Scholar 

  • Dellarocas, C. 2006. Strategic manipulation of internet opinion forums: Implications for consumers and firms. Management Science 52 (10): 1577–1593.

    Article  Google Scholar 

  • Dellarocas, C., and R. Narayan. 2006. A statistical measure of a population’s propensity to engage in post-purchase online word-of-mouth. Statistical Science 21 (2): 277–285.

    Article  Google Scholar 

  • Dellarocas, C., X.M. Zhang, and N.F. Awad. 2007. Exploring the value of online product reviews in forecasting sales: The case of motion pictures. Journal of Interactive Marketing 21 (4): 23–45.

    Article  Google Scholar 

  • Eliashberg, J., S.K. Hui, and Z.J. Zhang. 2007. From story line to box office: A new approach for green-lighting movie scripts. Management Science 53 (6): 881–893.

    Article  Google Scholar 

  • Fan, W., L. Wallace, S. Rich, and Z. Zhang. 2006. Tapping the power of text mining. Communications of the ACM 49 (9): 76–82.

    Article  Google Scholar 

  • Feinerer, I., and K. Hornik. 2015. tm: Text Mining Package. R package version 0.6-2. http://CRAN.R-project.org/package=tm.

  • Fellbaum, C. 1998. WordNet. Blackwell Publishing Ltd.

    Google Scholar 

  • Feldman, R., and J. Sanger. 2006. Information extraction. In The text mining handbook: Advanced approaches in analyzing unstructured data, 94–130.

    Google Scholar 

  • Feldman R., M. Fresko, J. Goldenberg, O. Netzer, and L. Ungar. 2007. Extracting product comparisons from discussion boards. In Proceedings of the 7th IEEE international conference data mining 2007, 469–474. Piscataway, NJ: IEEE.

    Google Scholar 

  • Feldman R., M. Fresko, Y. Kinar, Y. Lindell, O. Liphstat, M. Rajman, Y. Schler, and O. Zamir. 1998. Text mining at the term level. In Principles of data mining knowledge discovery, 65–73. Berlin: Springer.

    Google Scholar 

  • Feldman R., M. Fresko, J. Goldenberg, O. Netzer, and L. Ungar. 2008. Using text mining to analyze user forums. In Proceedings of the 2008 international conference on service systems service management, 1–5. Melbourne, VIC: IEEE Systems, Man, and Cybernetics Society.

    Google Scholar 

  • Feldman, R., O. Netzer, A. Peretz, and B. Rosenfeld. 2015. Utilizing text mining on online medical forums to predict label change due to adverse drug reactions. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1779–1788.

    Google Scholar 

  • Fleming, J.H., J.M. Darley, J.L. Hilton, and B.A. Kojetin. 1990. Multiple audience problem: A strategic communication perspective on social perception. Journal of Personality and Social Psychology 58 (4): 593.

    Article  Google Scholar 

  • Ghose, A., P.G. Ipeirotis, and B. Li. 2012. Designing ranking systems for hotels on travel search engines by mining user-generated and crowdsourced content. Marketing Science 31 (3): 493–520.

    Article  Google Scholar 

  • Godes, D., and D. Mayzlin. 2009. Firm-created word-of-mouth communication: Evidence from a field test. Marketing Science 28 (4): 721–739.

    Article  Google Scholar 

  • Godes, D., and D. Mayzlin. 2004. Using online conversations to study word-of-mouth communication. Marketing Science 23 (4): 545–560.

    Article  Google Scholar 

  • Godes, D., and J.C. Silva. 2012. Sequential and temporal dynamics of online opinion. Marketing Science 31 (3): 448–473.

    Article  Google Scholar 

  • Godes, D., D. Mayzlin, Y. Chen, S. Das, C. Dellarocas, B. Pfeiffer, B. Libai, S. Sen, M. Shi, and P. Verlegh. 2005. The firm’s management of social interactions. Marketing Letters 16 (3–4): 415–428.

    Google Scholar 

  • Gopinath, S., J.S. Thomas, and L. Krishnamurthi. 2014. Investigating the relationship between the content of online word of mouth, advertising, and brand performance. Marketing Science 33 (2): 241–258.

    Article  Google Scholar 

  • Hennig-Thurau, T., K.P. Gwinner, G. Walsh, and D.D. Gremler. 2004. Electronic word-of-mouth via consumer-opinion platforms: What motivates consumers to articulate themselves on the internet? Journal of Interactive Marketing 18 (1): 38–52.

    Article  Google Scholar 

  • Hu, M., and B. Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining, 168–177. ACM.

    Google Scholar 

  • Lee, T., and E.T. Bradlow. 2011. Automated marketing research using online customer reviews. Journal of Marketing Research 48 (5): 881–894.

    Article  Google Scholar 

  • Li, X., and L.M. Hitt. 2008. Self-selection and information role of online product reviews. Information Systems Research 19 (4): 456–474.

    Article  Google Scholar 

  • Liu, Y. 2006. Word of mouth for movies: Its dynamics and impact on box office revenue. Journal of Marketing 70 (3): 74–89.

    Article  Google Scholar 

  • Liu, B. 2007. Web data mining: Exploring hyperlinks, contents, and usage data. Springer Science & Business Media.

    Google Scholar 

  • Liu, B., M. Hu, and J.Cheng. 2005. Opinion observer: Analyzing and comparing opinions on the Web. In Proceedings of the 14th international conference World Wide Web, 342–351. Chiba, Japan: Association for Computer Machinery.

    Google Scholar 

  • Lovett, M.J., R. Peres, and R. Shachar. 2013. On brands and word of mouth. Journal of Marketing Research 50 (4): 427–444.

    Article  Google Scholar 

  • Ludwig, S., K. De Ruyter, M. Friedman, E.C. Brüggen, M. Wetzels, and G. Pfann. 2013. More than words: The influence of affective content and linguistic style matches in online reviews on conversion rates. Journal of Marketing 77 (1): 87–103.

    Article  Google Scholar 

  • Manning, C.D., and H. Schütze. 1999. Foundations of statistical natural language processing, vol. 999. Cambridge: MIT press.

    Google Scholar 

  • Mayzlin, D., Y. Dover, and J. Chevalier. 2014. Promotional reviews: An empirical investigation of online review manipulation. American Economic Review 104 (8): 2421–2455.

    Article  Google Scholar 

  • Moe, W.W., and D.A. Schweidel. 2012. Online product opinions: Incidence, evaluation, and evolution. Marketing Science 31 (3): 372–386.

    Article  Google Scholar 

  • Moe, W.W., and M. Trusov. 2011. The value of social dynamics in online product ratings forums. Journal of Marketing Research 48 (3): 444–456.

    Article  Google Scholar 

  • Nam, H., and P.K. Kannan. 2014. The informational value of social tagging networks. Journal of Marketing 78 (4): 21–40.

    Article  Google Scholar 

  • Netzer, O., R. Feldman, J. Goldenberg, and M. Fresko. 2012. Mine your own business: Market-structure surveillance through text mining. Marketing Science 31 (3): 521–543.

    Article  Google Scholar 

  • O’Connor, B., R. Balasubramanyan, B.R. Routledge, and N.A. Smith. 2010. From tweets to polls: Linking text sentiment to public opinion time series. ICWSM 11 (122–129): 1–2.

    Google Scholar 

  • Pang, B., and L. Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2 (1–2): 1–135.

    Article  Google Scholar 

  • Pennebaker, J.W., M.E. Francis, and R.J. Booth. 2001. Linguistic inquiry and word count: LIWC 2001, 71. Mahway: Lawrence Erlbaum Associates.

    Google Scholar 

  • Resnik, P., and R. Zeckhauser. 2002. Trust among strangers in internet transactions: Empirical analysis of eBay’s reputation system. In The economics of the internet and e-commerce, vol. 11, 127 Advances in applied microeconomics.

    Google Scholar 

  • Rosen-Zvi, M., T. Griffiths, M. Steyvers, and P. Smyth. 2004. The author-topic model for authors and documents. In Proceedings of the 20th conference on uncertainty in artificial intelligence, July 7, 487–494. AUAI Press.

    Google Scholar 

  • Rzhetsky, A., I. Iossifov, T. Koike, M. Krauthammer, P. Kra, M. Morris, H. Yu, P.A. Duboué, W. Weng, W.J. Wilbur, and V. Hatzivassiloglou. 2004. GeneWays: A system for extracting, analyzing, visualizing, and integrating molecular pathway data. Journal of Biomedical Informatics 37 (1): 43–53.

    Article  Google Scholar 

  • Schlosser, A.E. 2005. Posting versus lurking: Communicating in a multiple audience context. Journal of Consumer Research 32 (2): 260–265.

    Article  Google Scholar 

  • Schweidel, D.A., and W.W. Moe. 2014. Listening in on social media: A joint model of sentiment and venue format choice. Journal of Marketing Research 51 (4): 387–402.

    Article  Google Scholar 

  • Schweidel, D.A., Y.-H. Park, and Z. Jamal. 2014. A multi-activity latent attrition model for customer base analysis. Marketing Science 33 (2): 273–286.

    Article  Google Scholar 

  • Stephen, A.T., and J. Galak. 2012. The effects of traditional and social earned media on sales: A study of a microlending marketplace. Journal of Marketing Research 49 (5): 624–639.

    Article  Google Scholar 

  • Srinivasan, S., O.J. Rutz, and K. Pauwels. 2015. Paths to and off purchase: Quantifying the impact of traditional marketing and online consumer activity. Journal of the Academy of Marketing Science 1–14.

    Google Scholar 

  • Sun, M. 2012. How does the variance of product ratings matter? Management Science 58 (4): 696–707.

    Article  Google Scholar 

  • Swanson, D.R. 1988. Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine 31 (4): 526–557.

    Article  Google Scholar 

  • Swanson, D.R., and N.R. Smalheiser. 2001. Information discovery from complementary literatures: Categorizing viruses as potential weapons. Journal of American Society for Information Science and Technology 52 (10): 797–812.

    Article  Google Scholar 

  • Tirunillai, S., and G.J. Tellis. 2012. Does chatter really matter? Dynamics of user-generated content and stock performance. Marketing Science 31 (2): 198–215.

    Article  Google Scholar 

  • Tirunillai, S., and G.J. Tellis. 2014. Mining marketing meaning from online chatter: Strategic brand analysis of big data using latent Dirichlet allocation. Journal of Marketing Research 51 (4): 463–479.

    Article  Google Scholar 

  • Toubia, O., and O. Netzer. 2017. Idea generation, creativity and prototypicality. Marketing science 36 (1):1–20.

    Google Scholar 

  • Toubia, O., and A.T. Stephen. 2013. Intrinsic vs. image-related utility in social media: Why do people contribute content to twitter? Marketing Science 32 (3): 368–392.

    Article  Google Scholar 

  • Westbrook, R. A. 1987. Product/consumption-based affective responses and postpurchase processes. Journal of marketing research 258–270.

    Google Scholar 

  • Yu, Y., W. Duan, and Q. Cao. 2013. The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems 55 (4): 919–926.

    Article  Google Scholar 

  • You, Y., G.V. Gautham, and A.M. Joshi. 2015. A meta-analysis of electronic word-of-mouth elasticity. Journal of Marketing 79 (2): 19–39.

    Article  Google Scholar 

  • Zhang, H., G. Kim, and E.P. Xing. 2015. Dynamic topic modeling for monitoring market competition from online text and image data. In Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, 1425–1434. ACM.

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of Maryland, College Park, MD, USA

    Wendy W. Moe

  2. Columbia University, New York, NY, USA

    Oded Netzer

  3. Georgetown University, Washington, DC, USA

    David A. Schweidel

Authors
  1. Wendy W. Moe
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  2. Oded Netzer
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  3. David A. Schweidel
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Correspondence to Wendy W. Moe .

Editor information

Editors and Affiliations

  1. Rotterdam School of Management, Erasmus University, Rotterdam, South Holland, The Netherlands

    Berend Wierenga

  2. Department of Marketing, Hong Kong University of Science and Technology, Kowloon, Hong Kong

    Ralf van der Lans

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Moe, W.W., Netzer, O., Schweidel, D.A. (2017). Social Media Analytics. In: Wierenga, B., van der Lans, R. (eds) Handbook of Marketing Decision Models. International Series in Operations Research & Management Science, vol 254. Springer, Cham. https://doi.org/10.1007/978-3-319-56941-3_16

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