Google Trends

Last updated

Google Trends
Google Trends.png
Type of site
Search analysis
Available inEnglish, Spanish, Portuguese, Chinese, French, and more
Owner Google
Created byGoogle
URL trends.google.com/trends
LaunchedMay 11, 2006;17 years ago (2006-05-11)
Current statusActive

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.

Contents

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. [1]

Background

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. [2] 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. [3] In 2012, Google Insights for Search was merged into Google Trends with a new interface. [1]

Google Trends does not provide absolute values for the number of search queries, but relative search volumes (RSV). The relative search volumes are normalised to the highest value, which is set to 100. [4] The popularity of up to 5 search terms or search topics can be compared directly. Additional comparisons require a comparison term or topic. [5] In contrast to search terms, search topics are "a group of terms that have the same concept in any language". [6]

In 2009, Yossi Matias et al. published research on the predictability of search trends. [7] 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. [8] 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?" [9] 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?" [10] He also examined cultural differences in attitudes around pregnancy. [11]

Google Trends has also been used to forecast economic indicators, [12] [13] [14] and financial markets, [15] and analysis of Google Trends data has detected regional flu outbreaks before conventional monitoring systems. [16] Google Trends is increasingly used in ecological and conservation studies, with the number of research articles growing over 50% per year. [17] Google Trends data has been used to examine trends in public interest and awareness on biodiversity and conservation issues, [18] [19] [20] [21] [22] species bias in conservation project, [23] and identify cultural aspects of environmental issues. [24] 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. [25]

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. [26]

Evidence is provided by Jeremy Ginsberg et al. that Google Trends data can be used to track influenza-like illness in a population. [27] 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. [28]

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, [29] suicide occurrence, [30] asthma, [31] and parasitic diseases. [32] In an analogous concept of using health queries to predict the flu, Google Flu Trends was created. [27] [33] Further research should extend the utility of Google Trends in healthcare.

Google Trends allows the user to compare the relative search volume between two or more terms. Shown: following the 2006 release of Al Gore's film, An Inconvenient Truth, there was an increase in the number of Google searches for the term climate crisis, providing a measure of the film's influence. In 2019, governments made climate emergency declarations in larger numbers, years after the first one in 2016. 20200112 "Climate crisis" vs "Climate emergency" - Google search term usage.png
Google Trends allows the user to compare the relative search volume between two or more terms. Shown: following the 2006 release of Al Gore's film, An Inconvenient Truth , there was an increase in the number of Google searches for the term climate crisis, providing a measure of the film's influence. In 2019, governments made climate emergency declarations in larger numbers, years after the first one in 2016.

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. [36] [37]

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. [38] [39] [40] 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. [41] Their analysis of Google search volume for 98 terms of varying financial relevance, published in Scientific Reports , [42] suggests that increases in search volume for financially relevant search terms tend to precede large losses in financial markets. [43] [44] [45] [46] [47] [48] [49] [50]

The analysis of Tobias Preis was later found to be misleading and the results are most likely to be overfitted. [51] 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. [42]

In 2019, Tom Cochran, from public relations firm 720 Strategies, conducted a study comparing Google Trends to political polling. [52] 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. [53]

Research also shows that Google Trends can be used to forecast stock returns and volatility over a short horizon. [54] 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. [55]

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. [56]  Therefore, repeating analyses at different points in time can increase the reliability of the analysis. [56] [57] 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, [58] and relying on this data for prediction is risky. In 2020, this research made it to major headlines in Germany. [59]

Search quotas

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. [60]

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. [61] 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. [62]

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. [63]

Implications of data

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. [64] 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. [65]

See also

Notes

  1. 1 2 Matias, Yossi (September 27, 2012). "Insights into what the world is searching for -- the new Google Trends". Inside Search. The official Google Search blog.
  2. ↑ "Success! Google Trends Updated". InsideGoogle. July 30, 2007.
  3. ↑ Helft, Miguel (August 6, 2008). "Google's New Tool Is Meant for Marketers". The New York Times . Retrieved August 6, 2008.
  4. ↑ Nuti, Sudhakar V.; Wayda, Brian; Ranasinghe, Isuru; Wang, Sisi; Dreyer, Rachel P.; Chen, Serene I.; Murugiah, Karthik (October 22, 2014). "The Use of Google Trends in Health Care Research: A Systematic Review". PLOS ONE. 9 (10): e109583. Bibcode:2014PLoSO...9j9583N. doi: 10.1371/journal.pone.0109583 . ISSN   1932-6203. PMC   4215636 . PMID   25337815.
  5. 1 2 Springer, Steffen; Strzelecki, Artur; Zieger, Michael (November 1, 2023). "Maximum generable interest: A universal standard for Google Trends search queries". Healthcare Analytics. 3: 100158. doi:10.1016/j.health.2023.100158. ISSN   2772-4425. PMC   9997059 . PMID   36936703.
  6. ↑ "Compare Trends search terms - Trends Help". support.google.com. Retrieved March 1, 2024.
  7. ↑ On the predictability of Search Trends, Yossi Matias, Niv Efron, and Yair Shimshoni, Insights Search, Google Research blog, August 17, 2009.
  8. ↑ Stephens-Davidowitz, Seth (June 9, 2012). "How Racist Are We? Ask Google". The New York Times.
  9. ↑ Stephens-Davidowitz, Seth (December 7, 2013). "How Many American Men Are Gay?". The New York Times.
  10. ↑ Stephens-Davidowitz, Seth (January 18, 2014). "Tell Me, Google. Is My Son a Genius?". The New York Times.
  11. ↑ Stephens-Davidowitz, Seth (May 17, 2014). "What Do Pregnant Women Want". The New York Times.
  12. ↑ Choi, Hyunyoung; Varian, Hal (June 2012). "Predicting the Present with Google Trends". Economic Record. 88: 2–9. doi:10.1111/j.1475-4932.2012.00809.x. ISSN   0013-0249. S2CID   155467748.
  13. ↑ D'Amuri, F.; Marcucci, J. (October–December 2017). "The predictive power of Google searches in forecasting US unemployment". International Journal of Forecasting. 33 (4): 801–816. doi:10.1016/j.ijforecast.2017.03.004. ISSN   0169-2070.
  14. ↑ Monokroussos, George; Zhao, Yongchen (July–September 2020). "Nowcasting in Real Time Using Popularity Priors". International Journal of Forecasting. 36 (3): 1173–1180. doi:10.1016/j.ijforecast.2020.03.004. ISSN   0169-2070.
  15. ↑ Preis, Tobias; Moat, Helen Susannah; Stanley, H. Eugene (April 25, 2013). "Quantifying Trading Behavior in Financial Markets Using Google Trends". Scientific Reports. 3 (1): 1684. Bibcode:2013NatSR...3E1684P. doi:10.1038/srep01684. ISSN   2045-2322. PMC   3635219 . PMID   23619126.
  16. ↑ Carneiro, Herman Anthony; Mylonakis, Eleftherios (November 15, 2009). "Google Trends: A Web-Based Tool for Real-Time Surveillance of Disease Outbreaks". Clinical Infectious Diseases. 49 (10): 1557–1564. doi:10.1086/630200. ISSN   1058-4838. PMID   19845471.
  17. ↑ Troumbis, Andreas Y. Declining Google Trends of public interest in biodiversity: semantics, statistics or traceability of changing priorities?. OCLC   1188566404.
  18. ↑ Burivalova, Zuzana; Butler, Rhett A; Wilcove, David S (October 9, 2018). "Analyzing Google search data to debunk myths about the public's interest in conservation". Frontiers in Ecology and the Environment. 16 (9): 509–514. Bibcode:2018FrEE...16..509B. doi: 10.1002/fee.1962 . ISSN   1540-9295. S2CID   91865977.
  19. ↑ Mccallum, Malcolm L.; Bury, Gwendolyn W. (March 30, 2013). "Google search patterns suggest declining interest in the environment". Biodiversity and Conservation. 22 (6–7): 1355–1367. Bibcode:2013BiCon..22.1355M. doi:10.1007/s10531-013-0476-6. ISSN   0960-3115. S2CID   15593201.
  20. ↑ Nghiem, Le T. P.; Papworth, Sarah K.; Lim, Felix K. S.; Carrasco, Luis R. (March 30, 2016). "Analysis of the Capacity of Google Trends to Measure Interest in Conservation Topics and the Role of Online News". PLOS ONE. 11 (3): e0152802. Bibcode:2016PLoSO..1152802N. doi: 10.1371/journal.pone.0152802 . ISSN   1932-6203. PMC   4814066 . PMID   27028399.
  21. ↑ Troumbis, Andreas Y. (December 2017). "Google Trends and cycles of public interest in biodiversity: the animal spirits effect". Biodiversity and Conservation. 26 (14): 3421–3443. Bibcode:2017BiCon..26.3421T. doi:10.1007/s10531-017-1413-x. ISSN   0960-3115. S2CID   22739960.
  22. ↑ Soriano-Redondo, Andrea; Bearhop, Stuart; Lock, Leigh; Votier, Stephen C.; Hilton, Geoff M. (February 2017). "Internet-based monitoring of public perception of conservation". Biological Conservation. 206: 304–309. Bibcode:2017BCons.206..304S. doi:10.1016/j.biocon.2016.11.031. ISSN   0006-3207.
  23. ↑ Davies, Thomas; Cowley, Andrew; Bennie, Jon; Leyshon, Catherine; Inger, Richard; Carter, Hazel; Robinson, Beth; Duffy, James; Casalegno, Stefano; Lambert, Gwladys; Gaston, Kevin (September 26, 2018). Lambertucci, Sergio A (ed.). "Popular interest in vertebrates does not reflect extinction risk and is associated with bias in conservation investment". PLOS ONE. 13 (9): e0203694. Bibcode:2018PLoSO..1303694D. doi: 10.1371/journal.pone.0203694 . ISSN   1932-6203. PMC   6157853 . PMID   30256838.
  24. ↑ Funk, Stephan M.; Rusowsky, Daniela (August 15, 2014). "The importance of cultural knowledge and scale for analysing internet search data as a proxy for public interest toward the environment". Biodiversity and Conservation. 23 (12): 3101–3112. Bibcode:2014BiCon..23.3101F. doi:10.1007/s10531-014-0767-6. ISSN   0960-3115. S2CID   17644663.
  25. ↑ Proulx, Raphaël; Massicotte, Philippe; Pépino, Marc (August 23, 2013). "Googling Trends in Conservation Biology". Conservation Biology. 28 (1): 44–51. doi:10.1111/cobi.12131. ISSN   0888-8892. PMID   24033767. S2CID   29067445.
  26. ↑ Vosen, Simeon; Schmidt, Torsten (January 13, 2011). "Forecasting private consumption: survey-based indicators vs. Google Trends". Journal of Forecasting. 30 (6): 565–578. doi:10.1002/for.1213. hdl:10419/29900. ISSN   0277-6693.
  27. 1 2 Jeremy Ginsberg; Matthew H. Mohebbi; Rajan S. Patel; Lynnette Brammer; Mark S. Smolinski; Larry Brilliant (2009). "Detecting influenza epidemics using search engine query data". Nature. 457 (7232): 1012–1014. Bibcode:2009Natur.457.1012G. doi: 10.1038/nature07634 . PMID   19020500. S2CID   125775.
  28. ↑ Lampos, Vasileios; Miller, Andrew C.; Crossan, Steve; Stefansen, Christian (August 3, 2015). "Advances in nowcasting influenza-like illness rates using search query logs". Scientific Reports. 5 (12760): 12760. Bibcode:2015NatSR...512760L. doi:10.1038/srep12760. PMC   4522652 . PMID   26234783.
  29. ↑ Cavazos-Rehg, Patricia A., Melissa J. Krauss, Edward L. Spitznagel, Ashley Lowery, Richard A. Grucza, Frank J. Chaloupka, and Laura Jean Bierut. "Monitoring of non-cigarette tobacco use using Google Trends". Tobacco Control 24, no. 3 (2015): 249-255.
  30. ↑ Kristoufek, L., Moat, H.S. and Preis, T., 2016. Estimating suicide occurrence statistics using Google Trends. EPJ data science, 5(1), p.32.
  31. ↑ Bousquet, Jean, Robyn E. O'Hehir, Josep M. Anto, Gennaro D'Amato, Ralph Mösges, Peter W. Hellings, Michiel Van Eerd, and Aziz Sheikh. "Assessment of thunderstorm-induced asthma using Google Trends". The Journal of Allergy and Clinical Immunology . 140, no. 3 (2017): 891-893.
  32. ↑ Walker, M.D., 2018. Can Google be used to study parasitic disease? Internet searching on tick-borne encephalitis in Germany. Journal of vector borne diseases, 55(4), p. 327.
  33. ↑ Lazer, David; Kennedy, Ryan; King, Gary; Vespignani, Alessandro (March 14, 2014). "The Parable of Google Flu: Traps in Big Data Analysis". Science. 343 (6176): 1203–1205. Bibcode:2014Sci...343.1203L. doi:10.1126/science.1248506. ISSN   0036-8075. PMID   24626916.
  34. ↑ Rosenblad, Kajsa (December 18, 2017). "Review: An Inconvenient Sequel". Medium Magazine. Netherlands. Archived from the original on March 29, 2019. climate change, a term that Gore renamed to climate crisis
  35. ↑ "History of Climate Emergency Action by Councils". CACEonline.org. Council Action in the Climate Emergency. Archived from the original on October 30, 2020.
  36. ↑ Tobias Preis; Daniel Reith; H. Eugene Stanley (2010). "Complex dynamics of our economic life on different scales: insights from search engine query data". Philosophical Transactions of the Royal Society A. 368 (1933): 5707–5719. Bibcode:2010RSPTA.368.5707P. doi: 10.1098/rsta.2010.0284 . PMID   21078644.
  37. ↑ Catherine Mayer (November 15, 2010). "Study: Are Google Searches Affecting the Stock Market?". Time . Retrieved January 12, 2011.
  38. ↑ Preis, Tobias; Moat, Helen Susannah; Stanley, H. Eugene; Bishop, Steven R. (2012). "Quantifying the Advantage of Looking Forward". Scientific Reports. 2: 350. Bibcode:2012NatSR...2E.350P. doi:10.1038/srep00350. PMC   3320057 . PMID   22482034.
  39. ↑ Paul Marks (April 5, 2012). "Online searches for future linked to economic success". New Scientist. Retrieved April 9, 2012.
  40. ↑ Casey Johnston (April 6, 2012). "Google Trends reveals clues about the mentality of richer nations". Ars Technica. Retrieved April 9, 2012.
  41. ↑ Philip Ball (April 26, 2013). "Counting Google searches predicts market movements". Nature. doi:10.1038/nature.2013.12879. S2CID   167357427 . Retrieved August 9, 2013.
  42. 1 2 Tobias Preis; Helen Susannah Moat; H. Eugene Stanley (2013). "Quantifying Trading Behavior in Financial Markets Using Google Trends". Scientific Reports . 3: 1684. Bibcode:2013NatSR...3E1684P. doi:10.1038/srep01684. PMC   3635219 . PMID   23619126.
  43. ↑ Nick Bilton (April 26, 2013). "Google Search Terms Can Predict Stock Market, Study Finds". The New York Times. Retrieved August 9, 2013.
  44. ↑ Christopher Matthews (April 26, 2013). "Trouble With Your Investment Portfolio? Google It!". Time. Retrieved August 9, 2013.
  45. ↑ Philip Ball (April 26, 2013). "Counting Google searches predicts market movements". Nature . doi:10.1038/nature.2013.12879. S2CID   167357427 . Retrieved August 9, 2013.
  46. ↑ Bernhard Warner (April 25, 2013). "'Big Data' Researchers Turn to Google to Beat the Markets". Bloomberg Businessweek . Archived from the original on April 26, 2013. Retrieved August 9, 2013.
  47. ↑ Hamish McRae (April 28, 2013). "Hamish McRae: Need a valuable handle on investor sentiment? Google it". The Independent . Retrieved August 9, 2013.
  48. ↑ Richard Waters (April 25, 2013). "Google search proves to be new word in stock market prediction" . Financial Times . Archived from the original on December 11, 2022. Retrieved August 9, 2013.
  49. ↑ David Leinweber (April 26, 2013). "Big Data Gets Bigger: Now Google Trends Can Predict The Market". Forbes . Retrieved August 9, 2013.
  50. ↑ Jason Palmer (April 25, 2013). "Google searches predict market moves". BBC . Retrieved August 9, 2013.
  51. ↑ Challet, Damien; Bel Hadj Ayed, Ahmed (July 17, 2013). "Predicting financial markets with Google Trends and not so random keywords". arXiv: 1307.4643 [q-fin.ST].
  52. ↑ Pfannenstiel, Brianne. "Iowa Poll: Pete Buttigieg rockets to the top of the 2020 field as a clear front-runner". Des Moines Register. Retrieved January 8, 2020.
  53. ↑ Cochran, Tom (November 20, 2019). "Is Google Trends Better Than Polling? | 720 Strategies". www.720strategies.com. Retrieved January 8, 2020.
  54. ↑ Da, Engerlberg, Geo (2015). "The Sum of All Fears: Investor Sentiment and Asset Prices". The Review of Financial Studies . 28: 1–32. doi:10.1093/rfs/hhu072.{{cite journal}}: CS1 maint: multiple names: authors list (link)
  55. ↑ Borup, Schütte (2020). "In search of a job: Forecasting employment growth using Google Trends" (PDF). Journal of Business & Economic Statistics . 40: 1–15. doi:10.1080/07350015.2020.1791133. S2CID   226194319.
  56. 1 2 Dietzel, Marian Alexander (January 1, 2016). "Sentiment-based predictions of housing market turning points with Google trends". International Journal of Housing Markets and Analysis. 9 (1): 108–136. doi:10.1108/IJHMA-12-2014-0058. ISSN   1753-8270.
  57. ↑ Eichenauer, Vera Z.; Indergand, Ronald; Martínez, Isabel Z.; Sax, Christoph (April 2022). "Obtaining consistent time series from Google Trends". Economic Inquiry. 60 (2): 694–705. doi:10.1111/ecin.13049. ISSN   0095-2583. S2CID   244465884.
  58. ↑ Behnen, Philipp; Kessler, Rene; Kruse, Felix; Gómez, Jorge Marx; Schoenmakers, Jan; Zerr, Sergej (2020). "Experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data". In Koprinska, Irena; Kamp, Michael; Appice, Annalisa; Loglisci, Corrado; Antonie, Luiza; Zimmermann, Albrecht; Guidotti, Riccardo; Özgöbek, Özlem; Ribeiro, Rita P. (eds.). ECML PKDD 2020 Workshops. Communications in Computer and Information Science. Vol. 1323. Cham: Springer International Publishing. pp. 374–384. doi:10.1007/978-3-030-65965-3_25. ISBN   978-3-030-65965-3. S2CID   231792143.
  59. ↑ Köhler, Eva; Lerch, Isabel; Milatz, Marvin; Strozyk, Jan (June 5, 2020). "'Google Trends': Daten nicht aussagekräftig". tagesschau.de (in German). Tagesschau, Norddeutscher Rundfunk . Retrieved February 19, 2021.
  60. ↑ "Bug in Google Trends - Too Many Request Error - Google Search Help". support.google.com. Retrieved May 29, 2019.
  61. ↑ How does Hot Searches work?, google.com
  62. ↑ Insights into what the world is searching for -- the new Google Trends, Insights Search, The official Google Search blog, September 28, 2012.
  63. ↑ Elinor Mills (December 4, 2007). "Google Trends API coming soon". Archived from the original on May 10, 2011. Retrieved October 17, 2010.
  64. ↑ "On the Predictability of the U.S. Elections Through Search Volume Activity". The Journalist's Resource. April 5, 2011.
  65. ↑ Lim, Shawn, Stridsberg, Douglas (2014). "Feeling the Market's Pulse with Google Trends". International Federation of Technical Analysts Journal 2015 Edition . SSRN   2502508.{{cite journal}}: CS1 maint: multiple names: authors list (link)


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Public health surveillance is, according to the World Health Organization (WHO), "the continuous, systematic collection, analysis and interpretation of health-related data needed for the planning, implementation, and evaluation of public health practice." Public health surveillance may be used to track emerging health-related issues at an early stage and find active solutions in a timely manner. Surveillance systems are generally called upon to provide information regarding when and where health problems are occurring and who is affected.

<span class="mw-page-title-main">Market sentiment</span> General attitude of investors to market price development

Market sentiment, also known as investor attention, is the general prevailing attitude of investors as to anticipated price development in a market. This attitude is the accumulation of a variety of fundamental and technical factors, including price history, economic reports, seasonal factors, and national and world events. If investors expect upward price movement in the stock market, the sentiment is said to be bullish. On the contrary, if the market sentiment is bearish, most investors expect downward price movement. Market participants who maintain a static sentiment, regardless of market conditions, are described as permabulls and permabears respectively. Market sentiment is usually considered as a contrarian indicator: what most people expect is a good thing to bet against. Market sentiment is used because it is believed to be a good predictor of market moves, especially when it is more extreme. Very bearish sentiment is usually followed by the market going up more than normal, and vice versa. A bull market refers to a sustained period of either realized or expected price rises, whereas a bear market is used to describe when an index or stock has fallen 20% or more from a recent high for a sustained length of time.

Technology forecasting attempts to predict the future characteristics of useful technological machines, procedures or techniques. Researchers create technology forecasts based on past experience and current technological developments. Like other forecasts, technology forecasting can be helpful for both public and private organizations to make smart decisions. By analyzing future opportunities and threats, the forecaster can improve decisions in order to achieve maximum benefits. Today, most countries are experiencing huge social and economic changes, which heavily rely on technology development. By analyzing these changes, government and economic institutions could make plans for future developments. However, not all of historical data can be used for technology forecasting, forecasters also need to adopt advanced technology and quantitative modeling from experts’ researches and conclusions.

Stock market prediction is the act of trying to determine the future value of a company stock or other financial instrument traded on an exchange. The successful prediction of a stock's future price could yield significant profit. The efficient-market hypothesis suggests that stock prices reflect all currently available information and any price changes that are not based on newly revealed information thus are inherently unpredictable. Others disagree and those with this viewpoint possess myriad methods and technologies which purportedly allow them to gain future price information.

<span class="mw-page-title-main">Big data</span> Extremely large or complex datasets

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many entries (rows) offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate. Though used sometimes loosely partly due to a lack of formal definition, the best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only.

<span class="mw-page-title-main">Tobias Preis</span>

Tobias Preis is Professor of Behavioral Science and Finance at Warwick Business School and a fellow of the Alan Turing Institute. He is a computational social scientist focussing on measuring and predicting human behavior with online data. At Warwick Business School he directs the Data Science Lab together with his colleague Suzy Moat. Preis holds visiting positions at Boston University and University College London. In 2011, he worked as a senior research fellow with H. Eugene Stanley at Boston University and with Dirk Helbing at ETH Zurich. In 2009, he was named a member of the Gutenberg Academy. In 2007, he founded Artemis Capital Asset Management GmbH, a proprietary trading firm which is based in Germany. He was awarded a PhD in physics from the Johannes Gutenberg University of Mainz in Germany.

Culturomics is a form of computational lexicology that studies human behavior and cultural trends through the quantitative analysis of digitized texts. Researchers data mine large digital archives to investigate cultural phenomena reflected in language and word usage. The term is an American neologism first described in a 2010 Science article called Quantitative Analysis of Culture Using Millions of Digitized Books, co-authored by Harvard researchers Jean-Baptiste Michel and Erez Lieberman Aiden.

Infoveillance is a type of syndromic surveillance that specifically utilizes information found online. The term, along with the term infodemiology, was coined by Gunther Eysenbach to describe research that uses online information to gather information about human behavior.

<span class="mw-page-title-main">Google Ngram Viewer</span> Online search engine

The Google Ngram Viewer or Google Books Ngram Viewer is an online search engine that charts the frequencies of any set of search strings using a yearly count of n-grams found in printed sources published between 1500 and 2019 in Google's text corpora in English, Chinese (simplified), French, German, Hebrew, Italian, Russian, or Spanish. There are also some specialized English corpora, such as American English, British English, and English Fiction.

<span class="mw-page-title-main">Google Flu Trends</span> Former web service operated by Google

Google Flu Trends (GFT) was a web service operated by Google. It provided estimates of influenza activity for more than 25 countries. By aggregating Google Search queries, it attempted to make accurate predictions about flu activity. This project was first launched in 2008 by Google.org to help predict outbreaks of flu.

The Future Orientation Index was introduced by Tobias Preis, Helen Susannah Moat, H. Eugene Stanley and Steven Bishop using Google Trends to demonstrate that Google users from countries with a higher per capita GDP are more likely to search for information about the future than information about the past. The findings suggest there may be a link between online behaviour and real-world economic indicators. The authors of the study examined Google query logs made by Google users in 45 different 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’).

Infodemiology was defined by Gunther Eysenbach in the early 2000s as information epidemiology. It is an area of science research focused on scanning the internet for user-contributed health-related content, with the ultimate goal of improving public health. It is also defined as the science of mitigating public health problems resulting from an infodemic.

Rare or extreme events are events that occur with low frequency, and often refers to infrequent events that have a widespread effect and which might destabilize systems. Rare events encompass natural phenomena, anthropogenic hazards, as well as phenomena for which natural and anthropogenic factors interact in complex ways.

Seth Isaac Stephens-Davidowitz is an American data scientist, economist, and author. He has worked as a New York Times op-ed contributor, a data scientist at Google, as well as a visiting lecturer at the Wharton School of the University of Pennsylvania. He has published research using Google Trends search data, as well as data from Wikipedia and Facebook, to gain real-time insights into people's thoughts and beliefs that they may be unwilling to admit publicly.

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