Moreover, the occurrence of non-topic words such as emotional, sentimental, functional and even meaningless words are very common in microblog posts, which may distract the models from recognizing topic-related key words and thus fail to produce coherent and meaningful topics.
the various C variables refer to counts excluding the message m on conversation tree t. For each message m on a tree t, we sample the leader switcher y t,m and topic assignment z t,m according to the following conditional probability distribution: where g(s, k, t, m) takes different forms depending on the value of s: For each word n in m on t, the sampling formula of its background switcher is given as the following: where To evaluate our LeadLDA model, we conducted experiments on real-world microblog dataset collected from Sina Weibo that has the same 140character limitation and shares the similar market penetration as Twitter (Rapoza, 2011).