A study on microblog and search engine user behaviors: how twitter trending topics help predict google hot queries

Once every five minutes, Twitter publishes a list of trending topics by monitoring and analyzing tweets from its users. Similarly, Google makes available hourly a list of hot queries that have been issued to the search engine. We claim that social trends fired by Twitter may help explain and predict web trends derived from Google. Indeed, we argue that information flooding nearly real-time across the Twitter social network could anticipate the set of topics that users will later search on the Web. In this work, we analyze the time series derived from the daily volume index of each trend, either by Twitter or Google. Our study on a real-world dataset reveals that about 26% of the trending topics raising from Twitter “asis” are also found as hot queries issued to Google. Also, we find that about 72% of the similar trends appear first on Twitter. Thus, we assess the relation between comparable Twitter and Google trends by testing three classes of time series regression models. First, we find that Google by its own is not able to effectively predict the time behavior of its trends. Indeed, we show that autoregressive models, which try to fit time series of Google trends, perform poorly. On the other hand, we validate the forecasting power of Twitter by showing that models, which use Google as the dependent variable and Twitter as the explanatory variable, retain as significant the past values of Twitter 60% of times. Moreover, we discover that a Twitter trend causes a similar Google trend to later occur about 43% of times. In the end, we show that the very best-performing models are those using past values of both Twitter and Google.