Time Aware Knowledge Extraction for Microblog Summarization on Twitter

TL;DR

This paper introduces a Time Aware Knowledge Extraction methodology that enhances microblog summarization by capturing the evolution of information over time, enabling more relevant and customizable summaries of Twitter content.

Contribution

It proposes a novel temporal extension of Fuzzy Formal Concept Analysis for dynamic knowledge extraction and a summarization algorithm that adapts to user needs with high quality results.

Findings

Effective filtering of relevant concepts over time

Ability to generate summaries with adjustable detail levels

Experimental results show high quality and completeness

Abstract

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.