Constructing a multi-theoretical framework for mob modeling

Abstract As social media has made us more connected, it has also increased our ability to mobilize groups of people and coordinate events, such as mobs. Understanding the motivation of individuals to join such events and the ability to predict the outcome of these events (success or failure) is complex and requires a robust understanding of social science theories. In this research, we aim to analyze the applicability of five social science theories to explain and model mobs, namely: collective action theory, diffusion of innovation theory, lifestyle-routine activity (L-RAT) theory, low self-control theory, and environmental design theory (as developed through Crime Prevention Through Environmental Design (CPTED). By mapping the concepts/factors from these theories and providing potential operationalization methods accordingly, we hope to gain a deeper understanding of the mob phenomenon. The five social science theories comprise shared and unique concepts/factors related to the individuals (i.e., potential mobbers) and the event (i.e., the mob). We identify areas of theoretical overlap, creating a network of connected social science theories to model this phenomenon. Additionally, we discuss methods in which these concepts/factors might be estimated using social media sites such as event-based social media (EBSM) platforms. Finally, we derive a set of 24 sentences (expressed in English and using first-order logic) concerning the factors and rules that contribute to the mobber’s decision and 15 first-order logic sentences that govern the mob outcome.

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Publication Details

Journal
Social Network Analysis and Mining
Published
2025-04-04
DOI
https://doi.org/10.1007/s13278-025-01449-4
Primary Topic
Crime Patterns and Interventions
Type
article
Field-Weighted Citation Impact
0.00

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article

Constructing a multi-theoretical framework for mob modeling

Samer Al-khateeb, Nitin Agarwal, Regan Murray, Bridget Orr
Social Network Analysis and Mining
Crime Patterns and Interventions
article

Constructing a multi-theoretical framework for mob modeling

Samer Al-khateeb, Nitin Agarwal, Regan Murray, Bridget Orr
article en

Abstract

Abstract As social media has made us more connected, it has also increased our ability to mobilize groups of people and coordinate events, such as mobs. Understanding the motivation of individuals to join such events and the ability to predict the outcome of these events (success or failure) is complex and requires a robust understanding of social science theories. In this research, we aim to analyze the applicability of five social science theories to explain and model mobs, namely: collective action theory, diffusion of innovation theory, lifestyle-routine activity (L-RAT) theory, low self-control theory, and environmental design theory (as developed through Crime Prevention Through Environmental Design (CPTED). By mapping the concepts/factors from these theories and providing potential operationalization methods accordingly, we hope to gain a deeper understanding of the mob phenomenon. The five social science theories comprise shared and unique concepts/factors related to the individuals (i.e., potential mobbers) and the event (i.e., the mob). We identify areas of theoretical overlap, creating a network of connected social science theories to model this phenomenon. Additionally, we discuss methods in which these concepts/factors might be estimated using social media sites such as event-based social media (EBSM) platforms. Finally, we derive a set of 24 sentences (expressed in English and using first-order logic) concerning the factors and rules that contribute to the mobber’s decision and 15 first-order logic sentences that govern the mob outcome.

Social Network Analysis and MiningVol. 15(1)
University of Arkansas at Little Rock (US), Creighton University (US), International Computer Science Institute (US), Arkansas Department of Agriculture (US)
National Science Foundation, U.S. Department of Defense, Arkansas Research Alliance, Defense Advanced Research Projects Agency, U.S. Air Force, Advanced Research Projects Agency, Office of the Under Secretary of Defense, Office of Naval Research, Army Research Office, Air Force Research Laboratory
Openalex Percentile: Top 91%
Crime Patterns and Interventions
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