Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression

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Abstract

Social media is one of the widely used channels for interpersonal communication, to express feelings and thoughts through certain feedback. Blogs or ecommerce websites share plenty of such information, which serves as a valuable asset, and is also used to make predictions. However, negative feedback can ruin the essence of such platforms, causing frustration among peers. This paper presents a computational network model of a humanoid agent for getting inappropriate feed-backs, who learns to react with a level of competence over aggression due to feedback. Tuning and evaluation of the model is done by performing simulation experiments based on public tweets and mathematical analysis respectively. This model can serve as an input to detect and handle aggression.
Original languageEnglish
Title of host publicationPRIMA 2019: Principles and Practice of Multi-Agent Systems
Subtitle of host publication22nd International Conference, Turin, Italy, October 28–31, 2019, Proceedings
EditorsMatteo Baldoni, Mehdi Dastani, Beishui Liao, Yuko Sakurai, Rym Zalila Wenkstern
PublisherSpringer
Pages559-567
Number of pages9
ISBN (Electronic)9783030337926
ISBN (Print)9783030337919
DOIs
Publication statusPublished - 2019
Event22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019 - Turin, Italy
Duration: 28 Oct 201931 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11873 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA 2019
CountryItaly
CityTurin
Period28/10/1931/10/19

Keywords

  • Agent based modelling
  • Computational model
  • Cyber-aggression

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  • Cite this

    Jabeen, F., Treur, J., & Gerritsen, C. (2019). Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. In M. Baldoni, M. Dastani, B. Liao, Y. Sakurai, & R. Z. Wenkstern (Eds.), PRIMA 2019: Principles and Practice of Multi-Agent Systems: 22nd International Conference, Turin, Italy, October 28–31, 2019, Proceedings (pp. 559-567). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11873 LNAI). Springer. https://doi.org/10.1007/978-3-030-33792-6_41