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

Use of a simulated directional social network to compare measures of user influence

This paper proposes a new method for measuring user influence in directional social networks, derived from the works of Reilly et al. and Cha et al. The method being proposed in this paper considers an element from each of the two works. The first is the ratio of ‘messages forwarded’ over ‘messages posted’. The second element is the size of the audience. The second part of this study entails modeling and simulating an online social network. Using a data sample from the Twitter network to implement the simulation, it is going to allow us to compare the methods that are used to measure influence. The behaviors modeled include the act of gaining a follower, the act of creating a message, and the act of forwarding a message. These are the three behaviors we are using to compute influence

Social Fingerprinting: Identifying Users of Social Networks by their Data Footprint

This research defines, models, and quantifies a new metric for social networks: the social fingerprint. Just as one\u27s fingers leave behind a unique trace in a print, this dissertation introduces and demonstrates that the manner in which people interact with other accounts on social networks creates a unique data trail. Accurate identification of a user\u27s social fingerprint can address the growing demand for improved techniques in unique user account analysis, computational forensics and social network analysis. In this dissertation, we theorize, construct and test novel software and methodologies which quantify features of social network data. All approaches and methodologies are framed to test the accuracy of social fingerprint identification. Further, we demonstrate and verify that features of anonymous data trails observed on social networks are unique identifiers of social network users. Lastly, this research delivers scalable technology for future research in social network analysis, business analytics and social fingerprinting