Online Firestorms and Resentment Propagation on Social Media: Dynamics, Predictability and Mitigation
Social media serves as a place to gather information, interact, and form opinions. More recently, online firestorms, fake news and hate speech have shaken our beliefs and hopes about the positive power of social media to their very foundations. While negative emotions are in the core of human behavior, algorithms on social media, enhanced by Artificial Intelligence (AI) can produce and reinforce new dynamics.
In this project, we will address the mathematical modeling of the formation and dynamics of opinions in large groups of interacting people on social media. Our primary objective is to understand the driving factors of social media group level phenomena that lead to negative dynamics and to offer approaches on how to detect, react to, and possibly mitigate these dynamics early on. The fundamental goal is to reveal the possible relationship between the simple “social forces” acting at individual level, being the “first principles” of social interaction or the game rules, and the potential emergence of a global behavior.
The results of our study will provide insights of ethical relevance by discussing responsibility, delegation and control mechanisms in human-AI interacting systems.
Negative Dynamics on Social Media and their Ethical Challenges for AI
Are your Friends also Haters? Identification of Hater Networks on Social Media (Data Paper)
Advanced Statistical Analysis of large-scale Web-based Data
Against the Others! Detecting Moral Outrage in Social Media Networks
Identifying lexical change in negative word-of-mouth on social media
On the Global Convergence of Particle Swarm Optimization Methods (to appear as a preprint)