What You Say and How You Say it: Joint Modeling of Topics and Discourse in Microblog Conversations
Table 4: Evaluation of tweet classification results in accuracy (Acc) and average F1 (Avg F1). Representations learned by various models serve as the classification features. For our model, both the topic and discourse representations are fed into the classifier.
['[BOLD] Models', 'TREC Acc', 'TREC Avg F1', 'TWT16 Acc', 'TWT16 Avg F1']
We observe that our model outperforms other models with a large margin. The possible reasons are two folds. First, our model derives topics from conversation threads and thus potentially yields better message representations. Second, the discourse representations (only produced by our model) are indicative features for hashtags, because users will exhibit various discourse behaviors in discussing diverse topics (hashtags). For instance, we observe prominent “argument” discourse from tweets with “#Trump” and “#Hillary”, attributed to the controversial opinions to the two candidates in the 2016 U.S. presidential election.