research-article

Personalized emerging topic detection based on a term aging model

Authors:

Claudio SchifanellaAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 5, Issue 1

Article No.: 7, Pages 1 - 27

https://doi.org/10.1145/2542182.2542189

Published: 03 January 2014 Publication History

Abstract

Twitter is a popular microblogging service that acts as a ground-level information news flashes portal where people with different background, age, and social condition provide information about what is happening in front of their eyes. This characteristic makes Twitter probably the fastest information service in the world. In this article, we recognize this role of Twitter and propose a novel, user-aware topic detection technique that permits to retrieve, in real time, the most emerging topics of discussion expressed by the community within the interests of specific users. First, we analyze the topology of Twitter looking at how the information spreads over the network, taking into account the authority/influence of each active user. Then, we make use of a novel term aging model to compute the burstiness of each term, and provide a graph-based method to retrieve the minimal set of terms that can represent the corresponding topic. Finally, since any user can have topic preferences inferable from the shared content, we leverage such knowledge to highlight the most emerging topics within her foci of interest. As evaluation we then provide several experiments together with a user study proving the validity and reliability of the proposed approach.

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Index Terms

Personalized emerging topic detection based on a term aging model
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 5, Issue 1

Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining

December 2013

520 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/2542182

Editors:
Dr. Hui Xiong
Rutgers University http://datamining.rutgers.edu/
,
Dr. Shashi Shekhar
University of Minnesota http://www-users.cs.umn.edu/∼shekhar/
,
Dr. Alexander Tuzhilin
New York University http://pages.stern.nyu.edu/∼atuzhili/
,
Francesco Bonchi
Yahoo! Research Barcelona, Spain
,
Wray Buntine
NICTA, Australia and The Australian National University
,
Ricard Gavaldá
Technical University of Catalonia, Spain
,
Shengbo Guo
Xerox Research Centre Europe, France

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Publication History

Published: 03 January 2014

Accepted: 01 October 2012

Revised: 01 July 2012

Received: 01 February 2012

Published in TIST Volume 5, Issue 1

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