  Hashtags have become a powerful tool in social platforms such as Twitter to
categorize and search for content, and to spread short messages across members
of the social network. In this paper, we study temporal hashtag usage practices
in Twitter with the aim of designing a cognitive-inspired hashtag
recommendation algorithm we call BLLi,s. Our main idea is to incorporate the
effect of time on (i) individual hashtag reuse (i.e., reusing own hashtags),
and (ii) social hashtag reuse (i.e., reusing hashtags, which has been
previously used by a followee) into a predictive model. For this, we turn to
the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R,
which accounts for the time-dependent decay of item exposure in human memory.
We validate BLLi,s using two crawled Twitter datasets in two evaluation
scenarios: firstly, only temporal usage patterns of past hashtag assignments
are utilized and secondly, these patterns are combined with a content-based
analysis of the current tweet. In both scenarios, we find not only that
temporal effects play an important role for both individual and social hashtag
reuse but also that BLLi,s provides significantly better prediction accuracy
and ranking results than current state-of-the-art hashtag recommendation
methods.
