Predicting consumer behavior with Web search

By Sharad Goel, Jake M. Hofman, Sébastien Lahaie, David M. Pennock, Duncan J. Watts. Proceedings of the National Academy of Sciences of the United States of America, 2010

659 citations recorded by the source corpus.

Subfields: Epidemiology, Modeling and Simulation

View Paper: https://www.semanticscholar.org/paper/f10bfb00cd04dbb9f67f9dbef62dbfdabcc291c7

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Citations in the corpus

Dynamical classes of collective attention in twitter (2011, background): “Public attention can suddenly concentrate on a Web page or application [1, 2, 3, 4, 5, 6], a Youtube video [7, 8, 9], a trending topic in Twitter [10, 11, 12], or on a story in the news media [13], sometimes even in absence of an apparent reason.”
The Economics of Privacy (2016, background): “For instance, the ability of Google Flu Trends to correctly estimate influenza activity—which has often been heralded as an example of the power of big data (Goel et al. 2010), and which we cited in the introduction of this review—was later challenged (Butler 2013).”
Run away? Air pollution and emigration interests in China (2017, background): “Goel et al. (2010) and Choi and Varian (2012) find that GT has considerable prediction power on economic outcomes such as macroeconomics indicators, product sales, and consumer behavior.”
OMG, I Have to Tweet that! A Study of Factors that Influence Tweet Rates (2012, methodology): “This use of search query logs is well characterized in Goel et al.'s (2010a) study of the predictive value of search query logs compared to domain-specific data sets, and Goel et al (2010b)'s study on predicting consumer behavior through search queries.”
Consumer Power: Evolution in the Digital Age (2013, background): “Google search trends, which illustrate a form of demand-based power in information search contexts, relate to sales in various settings: retail (Ayoubkhani and Swannell 2012), video game, music, movies (Goel et al. 2010), motor vehicle (Choi and Varian 2012), housing (Wu and Brynjolfsson 2010), and ”
Toward link predictability of complex networks (2015, background): “The recent development of theoretical tools and the expanding availability of massive databases have allowed scientists to predict behaviors and trends, chart emergent events, and locate missing elements of a system (33 – 35).”
The Data Revolution and Economic Analysis (2014, background): “See also Goel et al. (2010) for a further discussion of using search queries to predict consumer behavior.”
Ten years of research change using Google Trends: From the perspective of big data utilizations and applications (2017, background): “(2010) noted that consumers searching online may allow us to predict their collective future behaviour (Goel et al., 2010).”