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Public/private tweet classification method to prevent posting to wrong accounts

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iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services

Pages 151 - 155

https://doi.org/10.1145/3151759.3151827

Published: 04 December 2017 Publication History

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Abstract

Corporations, municipalities, and governments have public Twitter accounts from which they send advertisements, publicity, and announcements. If such public account holders also have private accounts, posts might be sent from the wrong account. In this paper, we propose a classification method for public/private tweets using machine learning. As one classification feature, we use the tweet format and end-of-sentence expressions because these features are not confined to tweet content. We experimentally compared the classifiers built for multiple account holders and one general classifier.

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Cited By

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Tago KMachida AOnose SNakagawa Y(2022)Analysis of Emotional and Topical Tendencies Focusing on a Twitter User’s Multiple AccountsProceedings of the 2022 6th International Conference on Compute and Data Analysis10.1145/3523089.3523092(12-17)Online publication date: 25-Feb-2022
https://dl.acm.org/doi/10.1145/3523089.3523092
Yoshida KFushimi T(2022)Comparative Analysis of Information Spreading Focused on Topics and Emotions via Temporal Point Process2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00077(480-487)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00077

Index Terms

Public/private tweet classification method to prevent posting to wrong accounts
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
2. Security and privacy
  1. Software and application security
    1. Social network security and privacy

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Published In

iiWAS '17: Proceedings of the 19th International Conference on Information Integration and Web-based Applications & Services

December 2017

609 pages

ISBN:9781450352994

DOI:10.1145/3151759

Editors:
Maria Indrawan-Santiago
Monash University, Australia
,
Matthias Steinbauer
Johannes Kepler University Linz, Austria
,
Ivan Luiz Salvadori
Federal University of Santa Catarina, Brazil
,
Ismail Khalil
Johannes Kepler University Linz, Austria
,
Gabriele Anderst-Kotsis
Johannes Kepler University Linz, Austria

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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 December 2017

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JSPS KAKENHI

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iiWAS2017

iiWAS2017: The 19th International Conference on Information Integration and Web-based Applications & Services

December 4 - 6, 2017

Salzburg, Austria

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Cited By

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Tago KMachida AOnose SNakagawa Y(2022)Analysis of Emotional and Topical Tendencies Focusing on a Twitter User’s Multiple AccountsProceedings of the 2022 6th International Conference on Compute and Data Analysis10.1145/3523089.3523092(12-17)Online publication date: 25-Feb-2022
https://dl.acm.org/doi/10.1145/3523089.3523092
Yoshida KFushimi T(2022)Comparative Analysis of Information Spreading Focused on Topics and Emotions via Temporal Point Process2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00077(480-487)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00077

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