research-article

Topic taxonomy adaptation for group profiling

Authors:

Jianping Zhang,

John J. SalernoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1, Issue 4

Article No.: 1, Pages 1 - 28

https://doi.org/10.1145/1324172.1324173

Published: 01 February 2008 Publication History

Abstract

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a group's interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the dynamic change of a group's interest. This article addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflects the change of a group's interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approach's efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.

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Cited By

Schmank JBuchkremer R(2024)Navigating the Digital Public Sphere: An AI-Driven Analysis of Interaction Dynamics across Societal DomainsSocieties10.3390/soc1410019514:10(195)Online publication date: 26-Sep-2024
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Aalijah KIrfan R(2020)Scalable Taxonomy Generation and Evolution on Apache Spark2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00110(634-639)Online publication date: Aug-2020
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Mittal R(2020)Identification of Salient Attributes in Social Network: A Data Mining ApproachData Science and Analytics10.1007/978-981-15-5830-6_16(173-185)Online publication date: 28-May-2020
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Index Terms

Topic taxonomy adaptation for group profiling
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Language resources
  2. Machine learning
    1. Learning settings
2. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 1, Issue 4

January 2008

143 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/1324172

Issue’s Table of Contents

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 01 February 2008

Accepted: 01 August 2007

Revised: 01 May 2007

Received: 01 February 2007

Published in TKDD Volume 1, Issue 4

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Cited By

Schmank JBuchkremer R(2024)Navigating the Digital Public Sphere: An AI-Driven Analysis of Interaction Dynamics across Societal DomainsSocieties10.3390/soc1410019514:10(195)Online publication date: 26-Sep-2024
https://doi.org/10.3390/soc14100195
Aalijah KIrfan R(2020)Scalable Taxonomy Generation and Evolution on Apache Spark2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00110(634-639)Online publication date: Aug-2020
https://doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00110
Mittal R(2020)Identification of Salient Attributes in Social Network: A Data Mining ApproachData Science and Analytics10.1007/978-981-15-5830-6_16(173-185)Online publication date: 28-May-2020
https://doi.org/10.1007/978-981-15-5830-6_16
Lucas TGomes JVimieiro RPrudencio RLudermir T(2019)A Multivariate Method for Group Profiling Using Subgroup Discovery2019 8th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2019.00072(371-376)Online publication date: Oct-2019
https://doi.org/10.1109/BRACIS.2019.00072
Li Gde Carvalho R(2019)Process Mining in Social Media: Applying Object-Centric Behavioral Constraint ModelsIEEE Access10.1109/ACCESS.2019.29251057(84360-84373)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2925105
Ho TDo P(2018)Social Network Analysis Based on Topic Model with Temporal FactorInternational Journal of Knowledge and Systems Science10.4018/IJKSS.20180101059:1(82-97)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJKSS.2018010105
Taha K(2018)CRSStudent Engagement and Participation10.4018/978-1-5225-2584-4.ch028(553-570)Online publication date: 2018
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Gomes JPrudêncio RNascimento A(2018)Centrality-Based Group Profiling: A Comparative Study in Co-authorship NetworksNew Generation Computing10.1007/s00354-017-0028-936:1(59-89)Online publication date: 1-Jan-2018
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Gomes JPrudencio RNascimento A(2016)A Comparative Study of Group Profiling Techniques in Co-authorship Networks2016 5th Brazilian Conference on Intelligent Systems (BRACIS)10.1109/BRACIS.2016.074(373-378)Online publication date: Oct-2016
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Puget RBaskiotis N(2015)Hierarchical label partitioning for large scale classification2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA.2015.7344792(1-10)Online publication date: Oct-2015
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