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Statistical Models Applied to the Study of Social Conflict - MECS

Reference of the Group:

GIUV2026-033

 
Description of research activity:

MECS is an interdisciplinary group specializing in the quantitative analysis of data from the psychosocial field. Its objective is to understand the psychosocial dynamics underlying the various manifestations of social conflict, such as prejudice against minorities, hate speech, and the dynamics of disinformation that contribute to the development of social conflict. The group has a particular interest in approaching this study through the application of alternative statistical models (a person-centered approach) as opposed to the traditional statistical model based on a variable-centered approach, as well as through the use of experimental methodology.

 
Web:
 
Scientific-technical goals:
  • Development of statistical models based on obtaining latent classes and profiles for the study of social conflict and disinformation.
  • Measurement of psychosocial constructs and development of measurement instruments related to social conflict: Development and validation of scales that capture new manifestations of prejudice towards minorities and social conflict.
  • Analysis and modeling of psychosocial processes and group dynamics involved in the emergence of social activism, as well as in the processes of cohesion, collective participation and strengthening of the social movements.
  • Development of explanatory models of prejudice. Analysis of the impact of socio-cognitive variables and intergroup conflict on the manifestation of prejudice towards minorities and adversary groups.
  • Study of scientifically unfounded beliefs and other disinformation strategies. Analysis of the impact of socio-cognitive variables and intergroup conflict on adherence to empirically unfounded beliefs and other manifestations of disinformation.
  • Development of interdisciplinary research tools and methods at the confluence of social psychology and computer science for the study of social conflict.
 
Research lines:
  • Latent class analysis and Latent profile analysis.Statistical models based on the identification of latent classes and profiles for the study of social conflict and disinformation.
  • Prejudice and intergoup conflict.Development of explanatory models of prejudice. Analysis of the impact of socio-cognitive variables and intergroup conflict on the manifestation of prejudice towards minorities and groups considered adversaries.
  • Disinformation and unfounded beliefs.Study of scientifically unfounded beliefs and other disinformation strategies. Analysis of the impact of socio-cognitive variables and intergroup conflict on adherence to beliefs without empirical basis and other manifestations of disinformation.
  • Emergence of social activism.Analysis and modeling of the psychosocial processes and group dynamics involved in the emergence of social activism, as well as in the processes of cohesion, collective participation and strengthening of the social movements.
  • Social Psychology and Computational Sciences.Development of interdisciplinary research tools and methods at the confluence of social psychology and computer science for the study of social dynamics of cooperation and conflict.
 
Group members:
Name Nature of participation Entity Description
M FLORENCIA RODRIGO GIMENEZDirectorUniversitat de València
Research team
SANDRA SIMO TEUFELMemberUniversitat de València
MARIA ALBERTA CHULVI FERRIOLSMemberUniversitat de València
M ANGELES MOLPECERES PASTORCollaboratorUniversitat de València
ROSARIO ESPINOSA CALABUIGCollaboratorUniversitat de València
SARA DEGLI-ESPOSTICollaboratorConsejo Superior de Investigaciones CientíficasScientific
DAVID ARROYO GUARDEÑOCollaboratorConsejo Superior de Investigaciones CientíficasTenured scientist
LARA FONTANELLACollaboratorUniversita Degli Studi Gabriele d'Annunzio Di Chieti-PescaraFull university professor
 
CNAE:
  • Higher education
 
Associated structure:
  • Univ. Research Institute of Transit and Road Safety (INTRAS)
 
Keywords:
  • SOCIAL ACTIVISM
  • STATISTICAL MODELING
  • PREJUDICE
  • COMPUTATIONAL LINGUISTICS
  • DISINFORMATION