Creating Stories from Socially Curated Microblog Messages

Akisato KIMURA  Kevin DUH  Tsutomu HIRAO  Katsuhiko ISHIGURO  Tomoharu IWATA  Albert AU YEUNG  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E97-D   No.6   pp.1557-1566
Publication Date: 2014/06/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.E97.D.1557
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
social curation,  microblogging,  learning to rank,  

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Summary: 
Social media such as microblogs have become so pervasive such that it is now possible to use them as sensors for real-world events and memes. While much recent research has focused on developing automatic methods for filtering and summarizing these data streams, we explore a different trend called social curation. In contrast to automatic methods, social curation is characterized as a human-in-the-loop and sometimes crowd-sourced mechanism for exploiting social media as sensors. Although social curation web services like Togetter, Naver Matome and Storify are gaining popularity, little academic research has studied the phenomenon. In this paper, our goal is to investigate the phenomenon and potential of this new field of social curation. First, we perform an in-depth analysis of a large corpus of curated microblog data. We seek to understand why and how people participate in this laborious curation process. We then explore new ways in which information retrieval and machine learning technologies can be used to assist curators. In particular, we propose a novel method based on a learning-to-rank framework that increases the curator's productivity and breadth of perspective by suggesting which novel microblogs should be added to the curated content.