Most of the tweets that users exchange on Twitter make implicit mentions of named-entities, which in turn can be mapped to corresponding Wikipedia articles using proper Entity Linking (EL) techniques. Some of those become trending entities on Twitter due to a long-lasting or a sudden effect on the volume of tweets where they are mentioned.
We argue that the set of trending entities discovered from Twitter may help predict the volume of requests for relating Wikipedia articles. To validate this claim, we apply an EL technique to extract trending entities from a large dataset of public tweets. Then, we analyze the time series derived from the hourly trending score (i.e., an index of popularity) of each entity as measured by Twitter and Wikipedia, respectively.
Results revealed that Twitter actually leads Wikipedia by a lag of one hour, for more than 40% of the times.
- Ceccarelli, D., Lucchese, C., Orlando, S., and Tolomei, G. Twitter Anticipates Bursts of Requests for Wikipedia Articles. In DUBMOD 2013 (upcoming).