Time Aware Knowledge Extraction for Microblog Summarization on Twitter
This addresses the challenge for users and analysts in efficiently finding relevant information in dynamic social media streams, though it is incremental as it builds on existing methods with temporal extensions.
The paper tackles the problem of extracting and summarizing information from noisy, redundant microblog posts like Twitter by introducing a Time Aware Knowledge Extraction (TAKE) methodology that uses temporal Fuzzy Formal Concept Analysis to organize concepts in a time-dependent hierarchy, resulting in a summarization algorithm that produces summaries with adjustable detail and achieves good quality and completeness in experiments.
Microblogging services like Twitter and Facebook collect millions of user generated content every moment about trending news, occurring events, and so on. Nevertheless, it is really a nightmare to find information of interest through the huge amount of available posts that are often noise and redundant. In general, social media analytics services have caught increasing attention from both side research and industry. Specifically, the dynamic context of microblogging requires to manage not only meaning of information but also the evolution of knowledge over the timeline. This work defines Time Aware Knowledge Extraction (briefly TAKE) methodology that relies on temporal extension of Fuzzy Formal Concept Analysis. In particular, a microblog summarization algorithm has been defined filtering the concepts organized by TAKE in a time-dependent hierarchy. The algorithm addresses topic-based summarization on Twitter. Besides considering the timing of the concepts, another distinguish feature of the proposed microblog summarization framework is the possibility to have more or less detailed summary, according to the user's needs, with good levels of quality and completeness as highlighted in the experimental results.