Creating Stories from Socially Curated Microblog Messages

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

doi:10.1587/transinf.E97.D.1557

Creating Stories from Socially Curated Microblog Messages

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

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E97-D No.6 pp.1557-1566

Publication Date: 2014/06/01

Publicized

Online ISSN: 1745-1361

DOI: 10.1587/transinf.E97.D.1557

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Akisato KIMURA
  NTT Corporation
Kevin DUH
  Nara Institute of Science and Technology
Tsutomu HIRAO
  NTT Corporation
Katsuhiko ISHIGURO
  NTT Corporation
Tomoharu IWATA
  NTT Corporation
Albert AU YEUNG
  Axon Labs Limited

Keyword

social curation, microblogging, learning to rank

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Akisato KIMURA, Kevin DUH, Tsutomu HIRAO, Katsuhiko ISHIGURO, Tomoharu IWATA, Albert AU YEUNG, "Creating Stories from Socially Curated Microblog Messages" in IEICE TRANSACTIONS on Information, vol. E97-D, no. 6, pp. 1557-1566, June 2014, doi: 10.1587/transinf.E97.D.1557.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1557/_p

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@ARTICLE{e97-d_6_1557,
author={Akisato KIMURA, Kevin DUH, Tsutomu HIRAO, Katsuhiko ISHIGURO, Tomoharu IWATA, Albert AU YEUNG, },
journal={IEICE TRANSACTIONS on Information},
title={Creating Stories from Socially Curated Microblog Messages},
year={2014},
volume={E97-D},
number={6},
pages={1557-1566},
abstract={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.},
keywords={},
doi={10.1587/transinf.E97.D.1557},
ISSN={1745-1361},
month={June},}

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TY - JOUR
TI - Creating Stories from Socially Curated Microblog Messages
T2 - IEICE TRANSACTIONS on Information
SP - 1557
EP - 1566
AU - Akisato KIMURA
AU - Kevin DUH
AU - Tsutomu HIRAO
AU - Katsuhiko ISHIGURO
AU - Tomoharu IWATA
AU - Albert AU YEUNG
PY - 2014
DO - 10.1587/transinf.E97.D.1557
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2014
AB - 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.
ER -