A growing number of studies have examined how search engine and social media activity can be used to document current social trends and predict future patterns. Indeed, research literature has found that activity registered in Google Trends and Twitter chat can help predict various kinds of consumer and social data, including consumer goods marketability, the success of movies pending release and disease outbreaks.
In a 2011 study “On the Predictability of the U.S. Elections Through Search Volume Activity,” researchers from Wellesley College examined if Google Trends volume — which reflects the number of search queries around keywords — was predictive of 2008 and 2010 U.S. Congressional elections. The study’s authors separated electoral races into two categories: those with trend data recorded for one candidate; and those with trend data recorded for both candidates.
The study’s findings include:
- For political races where there were Google Trends data for only one candidate, the data predicted the outcome 70% of the time in 2008 and 52% in 2010.
- For highly contested races in which data were available for only one candidate, the success rate for predictions was 81% in 2008 and 39% in 2010.
- In races where there were data for both candidates, the data successfully predicted the outcome in 43% of cases in 2008 and 40% in 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. The study suggests that “the sentiment of a user’s query” may be an omitted variable that could affect Google Trends’ accuracy as a tool for predicting elections.