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

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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 publicationProc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19
PublisherSpringer
Publication statusPublished - 28 Oct 2019

Publication series

NameLecture Notes in Artificial Intelligence

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Jabeen, F., Treur, J., & Gerritsen, C. (2019). Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. In Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19 (Lecture Notes in Artificial Intelligence). Springer.
Jabeen, Fakhra ; Treur, Jan ; Gerritsen, Charlotte. / Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer, 2019. (Lecture Notes in Artificial Intelligence).
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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.",
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Jabeen, F, Treur, J & Gerritsen, C 2019, Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. in Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Lecture Notes in Artificial Intelligence, Springer.

Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. / Jabeen, Fakhra; Treur, Jan; Gerritsen, Charlotte.

Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer, 2019. (Lecture Notes in Artificial Intelligence).

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

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T1 - Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression

AU - Jabeen, Fakhra

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Jabeen F, Treur J, Gerritsen C. Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression. In Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19. Springer. 2019. (Lecture Notes in Artificial Intelligence).