Abstract
| Original language | English |
|---|---|
| Title of host publication | Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19 |
| Publisher | Springer |
| Publication status | Published - 28 Oct 2019 |
Publication series
| Name | Lecture Notes in Artificial Intelligence |
|---|
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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 proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Learning to Explain Anger: An Adaptive Humanoid-Agent for Cyber-Aggression
AU - Jabeen, Fakhra
AU - Treur, Jan
AU - Gerritsen, Charlotte
PY - 2019/10/28
Y1 - 2019/10/28
N2 - 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.
AB - 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.
M3 - Conference contribution
T3 - Lecture Notes in Artificial Intelligence
BT - Proc. of the 22nd International Conference on Principles and Practice of Multi-Agent Systems, PRIMA’19
PB - Springer
ER -