research-article

A Hierarchical Clustering Algorithm for Characterizing Social Media Users

Authors:

Priyanka Sinha,

Dilys ThomasAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 353 - 362

https://doi.org/10.1145/3366424.3383296

Published: 20 April 2020 Publication History

Abstract

In this paper we propose a method to characterize user behavior from their engagement with enterprise social media. Content analysis often suffers challenges due to noise. Here we study behavior using temporal activity, i.e., the number of posts per month represented as a time series. User posting volume on social media has a long tailed nature. It causes time series clustering algorithms to result in unbalanced clusters with either very few users or almost all users. Thus we propose a hierarchical time series clustering algorithm to group users according to their behavioral homogeneity and provide interpretable characterizations to the resulting clusters. Users in distinct clusters deviate significantly in their topics of interest while being homophilic (near identical or similar minded) within the cluster. Goodness of the clustering is observed over Enterprise Social Media (ESM); Stackexchange; and Linux Kernel Mailing List (LKML) datasets as opposed to existing clustering techniques.

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Cited By

Fernandes EMoro SCortez P(2024)A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methodsInternational Journal of Consumer Studies10.1111/ijcs.1303048:2Online publication date: 29-Feb-2024
https://doi.org/10.1111/ijcs.13030
Montes CSilvosa JAbalorio CNakazato R(2024)Application of BERT Model for Unsupervised Text Classification using Hierarchical Clustering for Automatic Classification of Thesis Manuscript2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC60852.2024.10690039(278-284)Online publication date: 7-Aug-2024
https://doi.org/10.1109/ICESC60852.2024.10690039
Kafeza ERompolas GKyriazidis SMakris C(2023)Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social MediaIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321978110:4(1951-1965)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3219781
Show More Cited By

Index Terms

A Hierarchical Clustering Algorithm for Characterizing Social Media Users
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 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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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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New York, NY, United States

Publication History

Published: 20 April 2020

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WWW '20

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

Taipei, Taiwan

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Fernandes EMoro SCortez P(2024)A data‐driven approach to improve online consumer subscriptions by combining data visualization and machine learning methodsInternational Journal of Consumer Studies10.1111/ijcs.1303048:2Online publication date: 29-Feb-2024
https://doi.org/10.1111/ijcs.13030
Montes CSilvosa JAbalorio CNakazato R(2024)Application of BERT Model for Unsupervised Text Classification using Hierarchical Clustering for Automatic Classification of Thesis Manuscript2024 5th International Conference on Electronics and Sustainable Communication Systems (ICESC)10.1109/ICESC60852.2024.10690039(278-284)Online publication date: 7-Aug-2024
https://doi.org/10.1109/ICESC60852.2024.10690039
Kafeza ERompolas GKyriazidis SMakris C(2023)Time-Series Clustering for Determining Behavioral-Based Brand Loyalty of Users Across Social MediaIEEE Transactions on Computational Social Systems10.1109/TCSS.2022.321978110:4(1951-1965)Online publication date: Aug-2023
https://doi.org/10.1109/TCSS.2022.3219781
Garkalns PNikiforova OZabiniako VKornienko J(2023)Analysis of the Behavior of Company Employees as Users of Information Systems or Tools, Based on Employees Clustering with K-means Algorithm2023 IEEE 64th International Scientific Conference on Information Technology and Management Science of Riga Technical University (ITMS)10.1109/ITMS59786.2023.10317652(1-7)Online publication date: 5-Oct-2023
https://doi.org/10.1109/ITMS59786.2023.10317652
Sinha PPatel RMitra PThomas DDey L(2022)Mining Homophilic Groups of Users using Edge Attributed Node Embedding from Enterprise Social NetworksCompanion Proceedings of the Web Conference 202210.1145/3487553.3524726(1139-1147)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3487553.3524726
Liu SSingh LTian K(2020)Information Exposure From Relational Background Knowledge on Social Media2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA49011.2020.00041(282-291)Online publication date: Oct-2020
https://doi.org/10.1109/DSAA49011.2020.00041

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