  Twitter, a microblogging service, has evolved into a powerful communication
platform with millions of active users who generate immense volume of
microposts on a daily basis. To facilitate effective categorization and easy
search, users adopt hashtags, keywords or phrases preceded by hash (#)
character. Successful prediction of the spread and propagation of information
in the form of trending topics or hashtags in Twitter, could help real time
identification of new trends and thus improve marketing efforts. Social
theories such as consistency theory suggest that people prefer harmony or
consistency in their thoughts. In Twitter, for example, users are more likely
to adopt the same trending hashtag multiple times before it eventually dies. In
this paper, we propose a low-rank weighted matrix factorization approach to
model trending hashtag adoption in Twitter based on consistency theory. In
particular, we first cast the problem of modeling trending hashtag adoption
into an optimization problem, then integrate consistency theory into it as a
regularization term and finally leverage widely used matrix factorization to
solve the optimization. Empirical experiments demonstrate that our method
outperforms other baselines in predicting whether a specific trending hashtag
will be used by users in future.
