Predicting the Present with Google Trends

By Hyunyoung Choi, Hal R. Varian. 2009

2,596 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/33b0925f7681f3199a5d075324e7f3c5e33f2c76

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In Epidemiology

Citations in the corpus

Forecasting hotel room demand using search engine data. (2012, methodology): “We obtained search volume data from Google Trends (Carneiro, H & Mylonakis, E 2009; Choi & Varian 2009).”
Big data, smart cities and city planning (2013, methodology): “To provide some sense of how recent this move to big data has been, we can use big data to explore ‘the rise of big data’ through Google Trends, which enables us to mine the Google search engine to generate the popularity of different trends (Choi and Varian, 2009).”
New games, new rules: big data and the changing context of strategy (2015, background): “Varian (2010) offered examples of economic transactions in fields such as transportation, advertising or contracting that benefit from the information provided by big data and highlighted its importance in making short-term economic predictions (Choi and Varian, 2012).”
Investor Information Demand: Evidence from Google Searches Around Earnings Announcements (2012, neutral): “Choi and Varian [2009] and Da et al.”
Forecasting Abnormal Stock Returns and Trading Volume Using Investor Sentiment: Evidence from Online Search ? (2011, background): “Choi and Varian (2009), for example, employ measures of search behavior to predict automobile sales and tourism.”
Text as Data (2017, background): “In Choi and Varian (2012) and Scott and Varian (2014, 2015), search term counts are aggregated by week and by geographic location, then converted to location-specific frequency indices.”
Big Data for Development: A Review of Promises and Challenges (2016, neutral): “The applications of Big Data Analytics in this are range from swine flu pandemic (Ritterman et al., 2009), to the sales of motor vehicle parts and travel patterns (Choi and Varian, 2012).”
Kernel Archetypal Analysis for Clustering Web Search Frequency Time Series (2014, background): “Aggregated search behaviors of millions of users yield reliable predictions for sales or general economic indicators [16], [17].”
Internet search behavior as an economic forecasting tool: The case of inflation expectations (2011, positive): “These three survey measures have been instrumental in developing models of inflation expectations formation, and in testing the rational expectations hypothesis.(6)”