A Joint Model of Conversational Discourse and Latent Topics on Microblogs

Abstract

Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts results in poor message-level word co-occurrence patterns. To address this issue, we organize microblog messages as conversation trees based on their reposting and replying relations, and propose an unsupervised model that jointly learns word distributions to represent: 1) different roles of conversational discourse, 2) various latent topics in reflecting content information. By explicitly distinguishing the probabilities of messages with varying discourse roles in containing topical words, our model is able to discover clusters of discourse words that are indicative of topical content. In an automatic evaluation on large-scale microblog corpora, our joint model yields topics with better coherence scores than competitive topic models from previous studies. Qualitative analysis on model outputs indicates that our model induces meaningful representations for both discourse and topics. We further present an empirical study on microblog summarization based on the outputs of our joint model. The results show that the jointly modeled discourse and topic representations can effectively indicate summary-worthy content in microblog conversations.

Conversational Structure Aware and Context Sensitive Topic Model for Online Discussions

“…Extracting and combing paths is time consuming and labeling is labor intensive, so LeadLDA may not be suitable for large online discussion datasets. Li et al [14] exploits discourse in conversations and joins conversational discourse and latent topics together for topic modeling. This model also organizes microblog posts as a conversation tree structure, but does not consider topic hierarchies and model robustness issue like our proposed model.…”

Understanding Social Media Cross-Modality Discourse in Linguistic Space

“…The popular tasks in the styles of either RST (Rhetorical Structure Theory) (Mann and Thompson, 1988;Liu et al, 2019) or PDTB (Penn Discourse Tree Bank) (Prasad et al, 2008;Xu et al, 2018) explore the rhetorical relations of discourse units (e.g., phrases or sentences) that cohesively connect them form a sense of coherence. These studies have demonstrated their helpfulness in diverse stream of NLP applications (Choubey et al, 2020), such as sentiment analysis (Bhatia et al, 2015), text categorization (Ji and Smith, 2017), and microblog sum-marization (Li et al, 2018). Nevertheless, limited work examines a social media image as a discourse unit of the pragmatic structure in multimedia contexts, which is a gap to be filled in this work.…”

What Changed Your Mind: The Roles of Dynamic Topics and Discourse in Argumentation Process

“…Most of them extend Hidden Markov Model (HMM) to produce distributional clusters of words to reflect latent discourse [8,33]. In discourse learning, features are exploited via modeling of conversation tree structure [25], relative position of sentences [20], topic content [32,52], and so forth.…”