The expression of social, cultural and political opinions in social media often features a strong affective component, especially when it occurs in highly-polarized contexts (e.g., in discussions on political elections, migrants, civil rights, and so on). In particular, hate speech is recognized as an extreme, yet typical, expression of opinion, and it is increasingly intertwined with the spread of defamatory, false stories. Current approaches for monitoring and circumscribing the spread of these phenomena mostly rely on simple affective models that do not account for emotions as complex cognitive, social and cultural constructs behind linguistic behavior. In particular, moral emotions possess a potential for advancing sentiment analysis in social media, especially since they provide insights on the motivations behind hate speech. Understanding these affective dynamics is important also for modelling human behavior in social settings that involve other people and artificial agents, as well as for designing socially-aware artificial systems. How can we include finer grained accounts of emotions in computational models of interpersonal and social interactions, with the goal of monitoring and dealing with conflicts in social media and agent interactions? How can we leverage the recent advances in machine learning and reasoning techniques to design more effective computational models of interpersonal and social conflict?

Introduction to the Special Section on Computational Modeling and Understanding of Emotions in Conflictual Social Interactions

Damiano, Rossana;Patti, Viviana;
2020-01-01

Abstract

The expression of social, cultural and political opinions in social media often features a strong affective component, especially when it occurs in highly-polarized contexts (e.g., in discussions on political elections, migrants, civil rights, and so on). In particular, hate speech is recognized as an extreme, yet typical, expression of opinion, and it is increasingly intertwined with the spread of defamatory, false stories. Current approaches for monitoring and circumscribing the spread of these phenomena mostly rely on simple affective models that do not account for emotions as complex cognitive, social and cultural constructs behind linguistic behavior. In particular, moral emotions possess a potential for advancing sentiment analysis in social media, especially since they provide insights on the motivations behind hate speech. Understanding these affective dynamics is important also for modelling human behavior in social settings that involve other people and artificial agents, as well as for designing socially-aware artificial systems. How can we include finer grained accounts of emotions in computational models of interpersonal and social interactions, with the goal of monitoring and dealing with conflicts in social media and agent interactions? How can we leverage the recent advances in machine learning and reasoning techniques to design more effective computational models of interpersonal and social conflict?
20
2
1
5
https://dl.acm.org/doi/abs/10.1145/3392334
Affective computing, socio-affective behavior, social interactions, hate speech detection
Damiano, Rossana; Patti, Viviana; Clavel, Chloé; Rosso, Paolo
File in questo prodotto:
File Dimensione Formato  
3392334-editorialECSI-TOIT2020.pdf

Accesso riservato

Tipo di file: PDF EDITORIALE
Dimensione 82.62 kB
Formato Adobe PDF
82.62 kB Adobe PDF   Visualizza/Apri   Richiedi una copia

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1739984
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact