H6513 Information Organisation
Google Trends
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Google trends
Public service based on Google Search that shows how often a particular search-term is entered relative to the total search-volume — across various regions of the world, and in various languages
The horizontal axis represents time (starting from 2004)
The vertical represents how often a term is searched for–relative to the total number of searches, globally
Below the main graph, popularity is broken down by countries, regions, cities and language
Can compare the relative search volume of searches between two or more terms
Source: Google
How Trends data is adjusted
Trends data don’t present absolute frequencies — data is adjusted to make comparisons between terms easier — proportional to the time and location of a query
Each data point is divided by the total searches of the geography and time range, to compare relative popularity. Otherwise places with the most search volume would always be ranked highest
The resulting numbers are scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics
Different regions that show the same number of searches for a term will not always have the same total search volumes
Source: Google
Where Trends data comes from
Google Trends data is a random sample of Google search data. Only a percentage of searches are used to compile Trends data.
Real time data is a random sample of searches from the last seven days.
Non-real time data is a random sample of Google search data that can be pulled from as far back as 2004 and up to 36 hours prior to your search.
Once the search data is collected, they are connected to topics and personal information removed
Data that is excluded
Searches made by very few people: Trends only shows data for popular terms, so search terms with low volume appear as 0.
Duplicate searches: Trends eliminates repeated searches from the same person over a short period of time.
Special characters: Trends filters out queries with apostrophes and other special characters.
Trending Searches
https://trends.google.com/trends/trendingsearches/daily?geo=SG
Trending Searches highlights searches that jumped significantly in traffic among all searches over the past 24 hours and updates hourly
Can use Trending Searches to see what people are most interested in at any given time and how the searches rank compared to one another
Trends Top Charts
https://trends.google.com/trends/yis/2018/SG/
Lists of the top trending or most searched actors, sports teams, books, etc.
Also an annual version of Top Charts, Year in Search.
Top Charts relies on the Google Knowledge Graph (semantic web/ontology) to identify searches about specific people, places, and things
Trending Searches highlights queries that jumped significantly in traffic among all searches over the past 24 hours, whereas Top Charts highlights topics with high overall search volume for that month
What can Google Trends be used for?
Stephens-Davidowitz
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
To estimate the size of the gay population
The most popular search beginning “is my husband” is “is my husband gay?“
To find that American parents were more likely to search “is my son gifted?” than “is my daughter gifted?”
But were more likely to search “is my daughter overweight?” than “is my son overweight?”
Stephens-Davidowitz, Seth. “How Racist Are We? Ask Google”. The Times.
Stephens-Davidowitz, Seth. “How Many American Men Are Gay?”. The Times.
Stephens-Davidowitz, Seth. “Tell Me, Google. Is My Son a Genius?”. The Times.
Ginsberg et al. (2009)
Google Trends data can be used to track influenza-like illness in a population
Relative frequency of certain queries is highly correlated with the percentage of physician visits in which a patient presents with influenza-like symptoms
;. Mohebbi;. Patel;;. Smolinski; (2009). “Detecting influenza epidemics using search engine query data”. Nature. 457 (7232): 1012–1014
Lampos, Vasileios; Miller,.; Crossan, Steve; Stefansen, Christian (3 Aug 2015). “Advances in nowcasting influenza-like illness rates using search query logs”. Scientific Reports. 5 (12760). Bibcode:2015NatSR…512760L. doi:10.1038/srep12760. PMC 4522652 Freely accessible. PMID 26234783.
Potential uses
Can help you identify products that are in great demand and, therefore, recommended for stocking.
Can highlight products with seasonal demand so that you can stock them
Using Google Trends to gain competitive advantage in the market
Google Trends as a resource for informing plastic surgery marketing decisions
Celebrities can influence the medical decisions of the general population.
Measured the impact of highly publicized plastic surgery-related events — on the frequency of specific Google search terms.
Method: Google Trends was used to analyze: and lip fillers, Joan Rivers and plastic surgery, and rhinoplasty, which were then analyzed using t-tests.
The average interest level in lip fillers increased by 30.31 points after announced that she received Juvéderm lip injections.
Source: Ward, B., Ward, M., & Paskhover, B. (2018). Google Trends as a Resource for Informing Plastic Surgery Marketing Decisions. Aesthetic plastic surgery, 42(2):598-602. doi: 10.1007/s00266-017-1019-4.
Google Trends as a resource for informing plastic surgery marketing decisions (2)
The interest level in plastic surgery decreased by 21.3% the month after Joan Rivers’ death.
Between January 2004 and May 2017, the average interest level for rhinoplasty was significantly different in January/December (67.91 ± 20.68) and June/July (70.12 ± 18.89) from the remaining calendar months (63.58 ± 19.67).
Los Angeles, City, and Miami showed consistently high interest levels throughout the time period, while Tulsa, OK, showed a major interest increase between 2015 to 2016 of 65 points.
Adverse drug reaction early warning
using user search data
Test collection: 5 diabetes drugs, including one already withdrawn due to adverse events (Avandia)
Google Trends series of Avandia side effects search reveal a significant early warning signal for the side effect emergence of Avandia.
The approach of using user search data to detect ADRs was found to have a longer lead time than traditional drug reaction discovery methods.
Correlation between the user search series of Avandia’s side effect and the ADR reports officially collected by FDA
The 5 drugs are used to construct a cumulated index for ADR signaling
, Hsinchun Chen,, (2017) “Adverse drug reaction early warning using user search data”, Online Information Review, Vol. 41 Issue: 4, pp.524-536, https://doi.org/10.1108/
OIR-10-2015-0341
Class exercise:
Compare bakery chains in Singapore
Bakery chains in Singapore:
Swee Heng bakery
Polar Puffs
Crystal Jade My Bread / Crystal Jade bakery
Cake History
Bakery Cuisine
Paris Baguette
Google Trends: https://trends.google.com/trends/explore
Compare their trends
Time period: Past 12 months
Location: Singapore
Other questions
What words are each bakery associated with?
Other questions?
Google Correlate
https://www.google.com/trends/correlate
Enter a search term to find other search terms that are correlated
Find search terms that correlate with your own time series
Compare Singapore brands with competitors in other countries
Paris Baguette
Location:Taiwan, China, Korea, Japan
Political issues
Singapore?
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