Using Agent-Based Modelling to Address Malicious Behavior on Social Media

In this study we create a platform for evaluating social media policies through simulation. We argue that social media policies need to be tested and refined before they can be successfully applied. We propose agent-based modelling (ABM) as a method for representing both malicious and legitimate social media agents, along with their key behaviors. Our two main research questions are as follows. 1. How do we build an agent-based model of a social media platform to address social media regulation? 2. How can an agent-based simulation approach be used to assess the effectiveness of social media policies? A preliminary agent-based model has been implemented (in Python), using the five human user types (‘amplifier’, ‘broadcaster’, ‘commentator’, ‘influential user’ and ‘viewer’) and two bot types (‘simple’ and ‘sophisticated’). During the simulation, a social media network of 100 agents is created and the agents\u27 behaviors are captured in this paper

Development and Evaluation of Multi-Agent Models Predicting Twitter Trends in Multiple Domains

Abstract—This paper concerns multi-agent models predicting Twitter trends. We use a step-wise approach to develop a novel agent-based model with the following properties: (1) it uses individual behavior parameters for a set of Twitter users and (2) it uses a retweet graph to model the underlying social network structure of these Twitter users to predict trends. The model parameters can be optimized using empirical data. To investigate to what extend this agent-based model can predict Twitter trends, we validate the model performance on two case studies using real Twitter data: tweets on banks and tweets on universities. We furthermore compare a version of the model that only uses the retweet graph (PM1) with the model that also simulates individual behavior (PM2) for small to larger prediction time intervals. For both case studies the results show that PM1 performs better for small prediction time intervals (up to one day in the future), while PM2 performs better for larger time intervals (from a day to a week). We think this opens up the possibility to use similar models for helping organizations to extend their monitoring capabilities of social media with predictive modeling and to become more pro-active and less reactive