Google Search Data Research Articles

Fear and Loathing in the Housing Market: Evidence from Search Query Data

We use Google search data to develop a broad-based and real-time index of housing distress. Unlike established indicators, this new Housing Distress Index (HDI) directly reects sentiment revealed through search queries. Research ndings indicate that the HDI provides important new insights in the determination of subprime mortgage credit-default swaps, the VIX, foreclosures, and national or city-specic housing returns. Further, the eects of housing distress on returns are asymmetric and stronger during times of crisis. Overall, results suggest that real estate and related markets are highly sensitive to vulnerabilities in household nances as captured by our measure of housing distress. G02, R30, G12

The Cost of Racial Animus on a Black Presidential Candidate: Using Google Search Data to Find What Surveys Miss

How can we know how much racial animus costs a black candidate if few will admit such socially unacceptable attitudes to surveys? I suggest a new proxy for an area’s racial animus from a non-survey source: the percent of Google search queries that include racially charged language. I compare the proxy to Barack Obama’s 2008 and 2012 vote shares, controlling for the vote share of the 2004 Democratic presidential candidate, John Kerry. Previous research using a similar specification but survey proxies for racial attitudes yielded little evidence that racial attitudes affected Obama. An area’s racially charged search rate, in contrast, is a robust negative predictor of Obama’s vote share. Continuing racial animus in the United States appears to have cost Obama roughly four percentage points of the national popular vote in both 2008 and 2012, giving his opponent the equivalent of a home-state advantage nationally.

Asset Prices and Google's Search Data

This paper investigates the novel relationship of asset price determination via Google data and trading volume. To capture this relation, we construct a model and secondly estimate panel and time series regressions. We use weekly data from 2004 to 2010 for 30 international banks. Our study is the first which differentiate between Google’s search volume and Google’s search clicks. We find that asset prices are positively related to the growth rate of Google’s search, trading volume and the level of Google search clicks. Moreover, this finding is in line with our theoretical model. Secondly, we find that the absolute level of Google’s search volume and Google’s search clicks behave differently regarding asset price dynamics. Google’s search volume, which measures long-run searches, is negatively related to asset prices and Google’s search click is positively related. Thus, Google’s search data offer a new insight into two different aspects: On the one hand it is a natural way of measuring attention to assets, and on the other hand it provides a timely and unbiased measurement of asset price dynamics especially during turbulent times. We conclude that Google’s data contain important information for the identification of asset bubbles.

Dollar illiquidity and central bank swap arrangements during the global financial crisis

While the global financial crisis was centered in the United States, it led to a surprising appreciation in the dollar, suggesting global dollar illiquidity. In response, the Federal Reserve partnered with other central banks to inject dollars into the international financial system. Empirical studies of the success of these efforts have yielded mixed results, in part because their timing is likely to be endogenous. In this paper, we examine the cross-sectional impact of these interventions. Theory consistent with dollar appreciation in the crisis suggests that their impact should be greater for countries that have greater exposure to the United States through trade and financial channels, less transparent holdings of dollar assets, and greater illiquidity difficulties. We examine these predictions for observed cross-sectional changes in CDS spreads, using a new proxy for innovations in perceived changes in sovereign risk based upon Google-search data. We find robust evidence that auctions of dollar assets by foreign central banks disproportionately benefited countries that were more exposed to the United States through either trade linkages or asset exposure. We obtain weaker results for differences in asset transparency or illiquidity. However, several of the important announcements concerning the international swap programs disproportionately benefited countries exhibiting greater asset opaqueness.

The Effects of Racial Animus on a Black Presidential Candidate: Using Google Search Data to Find What Surveys Miss

The Effects of Racial Animus on a Black Presidential Candidate: Using Google Search Data to Find What Surveys Miss

Correlation of "Google Flu Trends" with Sentinel Surveillance Data for Influenza in 2009 in Japan

Google Flu Trends (http://www.google.org/flutrends/) (GFT) aggregates Google search data to estimate flu activity in 28 countries. This study explored the correlation of GFT with Japanese national sentinel surveillance data in 2009. We obtained GFT and national sentinel surveillance data for influenza in all 47 Japanese prefectures from 29 June- 31 Dec 2009. Pearson correlation coefficients were calculated between GFT and sentinel surveillance data in each prefecture. Multiple regression analysis was also used to determine associations between them. Correlation coefficients were greater than 0.9 between GFT and the sentinel surveillance data in Tokyo, Kanagawa, Aichi, Osaka, and Hyogo, which have relatively large populations. Peaks for GFT were 2 weeks earlier than sentinel surveillance in these prefectures. Multiple regressions showed that only Tokyo (r = 0.97; P < 0.01) was significantly associated with GFT. People's interest in influenza affects GFT data, which could reflect influenza spread in mega-cities such as Tokyo. Further development of technology to identify prefecture-wide epidemic patterns could offer more accurate information on influenza spread in Japan.

Behavioral Consequences of Conflict-Oriented Health News Coverage: The 2009 Mammography Guideline Controversy and Online Information Seeking

Building on channel complementarity theory and media-system dependency theory, this study explores the impact of conflict-oriented news coverage of health issues on information seeking online. Using Google search data as a measure of behavior, we demonstrate that controversial news coverage of the U.S. Preventive Services Task Force's November 2009 recommendations for changes in breast cancer screening guidelines strongly predicted the volume of same-day online searches for information about mammograms. We also found that this relationship did not exist 1 year prior to the coverage, during which mammography news coverage did not focus on the guideline controversy, suggesting that the controversy frame may have driven search behavior. We discuss the implications of these results for health communication scholars and practitioners.

Dollar Illiquidity and Central Bank Swap Arrangements during the Global Financial Crisis

While the global financial crisis was centered in the United States, it led to a surprising appreciation in the dollar, suggesting global dollar illiquidity. In response, the Federal Reserve partnered with other central banks to inject dollars into the international financial system. Empirical studies of the success of these efforts have yielded mixed results, in part because their timing is likely to be endogenous. In this paper, we examine the cross-sectional impact of these interventions. Theory consistent with dollar appreciation in the crisis suggests that their impact should be greater for countries that have greater exposure to the United States through trade and financial channels, less transparent holdings of dollar assets, and greater illiquidity difficulties. We examine these predictions for observed cross-sectional changes in CDS spreads, using a new proxy for innovations in perceived changes in sovereign risk based upon Google-search data. We find robust evidence that auctions of dollar assets by foreign central banks disproportionately benefited countries that were more exposed to the United States through either trade linkages or asset exposure. We obtain weaker results for differences in asset transparency or illiquidity. However, several of the important announcements concerning the international swap programs disproportionately benefited countries exhibiting greater asset opaqueness.

GET WELL: an automated surveillance system for gaining new epidemiological knowledge

BackgroundThe assumption behind the presented work is that the information people search for on the internet reflects the disease status in society. By having access to this source of information, epidemiologists can get a valuable complement to the traditional surveillance and potentially get new and timely epidemiological insights. For this purpose, the Swedish Institute for Infectious Disease Control collaborates with a medical web site in Sweden.MethodsWe built an application consisting of two conceptual parts. One part allows for trends, based on user specified requests, to be extracted from anonymous web query data from a Swedish medical web site. The second conceptual part permits tailored analyses of particular diseases, where more complex statistical methods are applied to the data. To evaluate the epidemiological relevance of the output, we compared Google search data and search data from the medical web site.ResultsIn the paper, we give concrete examples of the output from the web query-based system. We also present results from the comparison between data from the search engine Google and search data from the national medical web site.ConclusionsThe application is in regular use at the Swedish Institute for Infectious Disease Control. A system based on web queries is flexible in that it can be adapted to any disease; we get information on other individuals than those who seek medical care; and the data do not suffer from reporting delays. Although Google data are based on a substantially larger search volume, search patterns obtained from the medical web site may still convey more information from an epidemiological perspective. Furthermore we can see advantages with having full access to the raw data.

Prediction and surveillance of influenza epidemics

To describe the use of surveillance and forecasting models to predict and track epidemics (and, potentially, pandemics) of influenza. We collected 5 years of historical data (2005-2009) on emergency department presentations and hospital admissions for influenza-like illnesses (International Classification of Diseases [ICD-10-AM] coding) from the Emergency Department Information System (EDIS) database of 27 Queensland public hospitals. The historical data were used to generate prediction and surveillance models, which were assessed across the 2009 southern hemisphere influenza season (June-September) for their potential usefulness in informing response policy. Three models are described: (i) surveillance monitoring of influenza presentations using adaptive cumulative sum (CUSUM) plan analysis to signal unusual activity; (ii) generating forecasts of expected numbers of presentations for influenza, based on historical data; and (iii) using Google search data as outbreak notification among a population. All hospitals, apart from one, had more than the expected number of presentations for influenza starting in late 2008 and continuing into 2009. (i) The CUSUM plan signalled an unusual outbreak in December 2008, which continued in early 2009 before the winter influenza season commenced. (ii) Predictions based on historical data alone underestimated the actual influenza presentations, with 2009 differing significantly from previous years, but represent a baseline for normal ED influenza presentations. (iii) The correlation coefficients between internet search data for Queensland and statewide ED influenza presentations indicated an increase in correlation since 2006 when weekly influenza search data became available. This analysis highlights the value of health departments performing surveillance monitoring to forewarn of disease outbreaks. The best system among the three assessed was a combination of routine forecasting methods coupled with an adaptive CUSUM method.