Google Trends

Popularity analysis of search queries

Google Trends is a website by Google that analyzes the popularity of top search queries in Google Search across various regions and languages. The website uses graphs to compare the search volume of different queries over time. On August 5, 2008, Google launched Google Insights for Search, a more sophisticated and advanced service displaying search trends data. On September 27, 2012, Google merged Google Insights for Search into Google Trends.

Originally, Google neglected updating Google Trends on a regular basis. In March 2007, internet bloggers noticed that Google had not added new data since November 2006, and Trends was updated within a week. Google did not update Trends from March until July 30, and only after it was blogged about, again. Google now claims to be "updating the information provided by Google Trends daily; Hot Trends is updated hourly." On August 6, 2008, Google launched a free service called Insights for Search. Insights for Search is an extension of Google Trends and although the tool is meant for marketers, it can be utilized by any user. The tool allows for the tracking of various words and phrases that are typed into Google's search box. The tracking device provided a more indepth analysis of results. It also has the ability to categorize and organize the data, with special attention given to the breakdown of information by geographical areas. In 2012, Google Insights for Search was merged into Google Trends with a new interface. The popularity of up to 5 search terms or search topics can be compared directly. Additional comparisons require a comparison term or topic. In 2009, Yossi Matias et al. published research on the predictability of search trends. In a series of articles in The New York Times, Seth Stephens Davidowitz used Google Trends to measure a variety of behaviors. For example, in June 2012, he argued that search volume for the word "nigger(s)" could be used to measure racism in different parts of the United States. Correlating this measure with Obama's vote share, he calculated that Obama lost about 4 percentage points due to racial animus in the 2008 presidential election. He also used Google data, along with other sources, to estimate the size of the gay population. This article noted that the most popular search beginning "is my husband" is "is my husband gay?" In addition, he found that American parents were more likely to search "is my son gifted?" than "is my daughter gifted?" But they were more likely to search "is my daughter overweight?" than "is my son overweight?" He also examined cultural differences in attitudes around pregnancy. Google Trends has also been used to forecast economic indicators, and financial markets, and analysis of Google Trends data has detected regional flu outbreaks before conventional monitoring systems. Google Trends is increasingly used in ecological and conservation studies, with the number of research articles growing over 50% per year. Google Trends data has been used to examine trends in public interest and awareness on biodiversity and conservation issues, species bias in conservation project, and identify cultural aspects of environmental issues. The data obtained from Google Trends has also been used to track changes in the timing biological processes as well as the geographic patterns of biological invasion. A 2011 study found that an indicator for private consumption based on search query time series provided by Google Trends found that in almost all conducted forecasting experiments, the Google indicator outperformed survey based indicators. Evidence is provided by Jeremy Ginsberg et al. that Google Trends data can be used to track influenza like illness in a population. Because the relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza like symptoms, an estimate of weekly influenza activity can be reported. A more sophisticated model for inferring influenza rates from Google Trends, capable of overcoming the mistakes of its predecessors has been proposed by Lampos et al. The use of Google Trends to study a wide range of medical topics is becoming more widespread. Studies have been performed examining such diverse topics as use of tobacco substitutes, suicide occurrence, asthma, and parasitic diseases. In an analogous concept of using health queries to predict the flu, Google Flu Trends was created. Further research should extend the utility of Google Trends in healthcare. Furthermore, it was shown by Tobias Preis et al. that there is a correlation between Google Trends data of company names and transaction volumes of the corresponding stocks on a weekly time scale. In April 2012, Tobias Preis, Helen Susannah Moat, H. Eugene Stanley and Steven R. Bishop used Google Trends data to demonstrate that Internet users from countries with a higher per capita gross domestic product (GDP) are more likely to search for information about the future than information about the past. The findings, published in the journal Scientific Reports, suggest there may be a link between online behaviour and real world economic indicators. The authors of the study examined Google search queries made by Internet users in 45 countries in 2010 and calculated the ratio of the volume of searches for the coming year (‘2011’) to the volume of searches for the previous year (‘2009’), which they call the ‘future orientation index’. They compared the future orientation index to the per capita GDP of each country and found a strong tendency for countries in which Google users enquire more about the future to exhibit a higher GDP. The results hint that there may potentially be a relationship between the economic success of a country and the information seeking behaviour of its citizens online. In April 2013, Tobias Preis and his colleagues Helen Susannah Moat and H. Eugene Stanley introduced a method to identify online precursors for stock market moves, using trading strategies based on search volume data provided by Google Trends. Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports, suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. The analysis of Tobias Preis was later found to be misleading and the results are most likely to be overfitted. The group of Damien Challet tested the same methodology with unrelated to financial markets search words, such as terms for diseases, car brands or computer games. They have found that all these classes provide equally good "predictability" of the financial markets as the original set. For example, the search terms like "bone cancer", "Shelby GT 500" (car brand), "Moon Patrol" (computer game) provide even better performance as those selected in original work. The study was in response to Pete Buttigieg's surge in a poll of Iowa's likely Democratic caucusgoers conducted between November 8 to 13 by the Des Moines Register. Using Google Trends, he looked into the relationship between polling numbers and Google searches. His findings concluded that, while polling consists of far smaller sample sizes, the primary difference with Google Trends is that it only demonstrates intent to seek information. Google search volume was higher for candidates having higher polling numbers, but the correlation did not mean increased candidate favorability. Research also shows that Google Trends can be used to forecast stock returns and volatility over a short horizon. Other research has shown that Google Trends has strong predictive power for macroeconomic series. For example, a paper published in 2020 shows that a large panel of Google Trends predictors can forecast employment growth in the United States at both the national and state level with a relatively high degree of accuracy even a year in advance. Google Trends uses representative sub samples for analysis, which means that the data can vary depending on the time of the survey and is associated with background noise. Therefore, repeating analyses at different points in time can increase the reliability of the analysis. It was shown that Google Trends data can exhibit a high variability when queried at different points in time, indicating that it may not be reliable except for very frequent search terms due to sampling, and relying on this data for prediction is risky. In 2020, this research made it to major headlines in Germany.

Google has incorporated quota limits for Trends searches. This limits the number of search attempts available per user/IP/device. Details of quota limits have not yet been provided, but it may depend on geographical location or browser privacy settings. It has been reported in some cases that this quota is reached very quickly if one is not logged into a Google account before trying to access the Trends service.

Google Hot Trends is an addition to Google Trends which displays the top 20 hot, i. e., fastest rising, searches (search terms) of the past hour in various countries. This is for searches that have recently experienced a sudden surge in popularity. For each of the search terms, it provides a 24 hour search volume graph as well as blog, news and web search results. Hot Trends has a history feature for those wishing to browse past hot searches. Hot Trends can be installed as an iGoogle Gadget. Hot Trends is also available as an hourly Atom web feed.

Since 2008 there has been a sub section of Google Trends which analyses traffic for websites, rather than traffic for search terms. This is a similar service to that provided by Alexa Internet. The Google Trends for Websites became unavailable after the September 27, 2012, release of the new Google Trends product.

An API to accompany the Google Trends service was announced by Marissa Mayer, then vice president of search products and user experience at Google. This was announced in 2007, and so far has not been released.

A group of researchers at Wellesley College examined data from Google Trends and analyzed how effective a tool it could be in predicting U. S. Congress elections in 2008 and 2010. In highly contested races where data for both candidates were available, the data successfully predicted the outcome in 33.3% of cases in 2008 and 39% in 2010. The authors conclude that, compared to the traditional methods of election forecasting, incumbency and New York Times polls, and even in comparison with random chance, Google Trends did not prove to be a good predictor of either the 2008 or 2010 elections. Another group has also explored possible implications for financial markets and suggested possible ways to combine insights from Google Trends with other concepts in technical analysis.