Springer’s Social Network Analysis and Mining (SNAM), a prestigious journal, publishes groundbreaking research at the intersection of computational and information sciences discipline and the social science discipline. Recently, SNAM published one of our studies conducted by Prof. Agarwal and his colleagues at Creighton University, Prof. Samer Al-khateeb and Jack Burright. In the article titled, “Evaluating collective action theory-based model to simulate mobs”, the authors focused on the combination of social network dynamics with behavioral analysis. The researchers utilize agent-based modeling (ABM) to model mob networks, specifically for those that are categorized as Deviant Cyber Flash Mobs (mobs that quickly converge in the real-world after digital collaboration). The authors used ABM and as well as Monte Carlo methods to simulate mob formation based on collective action theory. The model examined how factors like participant interest, control, and decision-making time affect a mob’s success rate. To validate the model, authors collected data from 459 real Meetup.com events. The simulation assigned agents random interest and control values, then processed their decisions through various scenarios. The research aims to predict mob success rates, determine the number of powerful actors needed for success, and understand how decision-making time impacts participation.
Their key findings were the following:
- Mob success rate correlated negatively with threshold values for participation.
- Average participation rates were 35.5% or 48% depending on the calculation method.
- More powerful actors (organizers) were needed for larger crowds.
- Decision-making time affected participation rates—when people had more time to decide, participation increased.
- The model could predict cyber mob outcomes more accurately than physical mob outcomes.
Prof. Agarwal said, “Such research studies provide insights into factors affecting mob behavior, success rates, and organizer requirements. Journal articles like these demonstrate how social media and digital collaboration can have lasting effects in the physical realm.” Click here to read the full article.
The journal SNAM focuses on the theoretical and practical aspects of social network analysis and data mining. It covers interdisciplinary research in fields such as computer science, sociology, physics, economics, and more. Some key themes and ideas covered in SNAM are
- Social network structures and dynamics,
- Data mining and machine learning for networks,
- Computational and algorithmic approaches,
- Influence, diffusion, and community detection, and
- Applications in social media, marketing, and behavioral analysis.