Topic Extraction from Microblog Posts Using Conversation Structures

Relevance Modeling for Microblog Summarization

This paper introduces a new type of summarization task, known as microblog summarization, which aims to synthesize content from multiple microblog posts on the same topic into a human-readable prose description of fixed length. Our approach leverages (1) a generative model which induces event structures from text and (2) a user behavior model which captures how users convey relevant content.

Space-Time Aware Behavioral Topic Modeling for Microblog Posts

How can we automatically identify the topics of microblog posts? This question has received substantial attention in the research community and has led to the development of different topic models, which are mathematically well-founded statistical models that enable the discovery of topics in document collections. Such models can be used for topic analyses according to the interests of user gro...

Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning

How fake news goes viral via social media? How does its propagation pattern differ from real stories? In this paper, we attempt to address the problem of identifying rumors, i.e., fake information, out of microblog posts based on their propagation structure. We firstly model microblog posts diffusion with propagation trees, which provide valuable clues on how an original message is transmitted ...

Wikipedia-based Topic Clustering for Microblogs

Microblogging has become a primary channel by which people not only share information, but also search for information. However, microblog search results are most often displayed by simple criteria such as creation time or author. A review of the literature suggests that clustering by topic may be useful, but short posts offer limited scope for clustering using lexical evidence alone. This pape...

Unsupervised Keyword Extraction from Microblog Posts via Hashtags