Turing patterns of misinformation: A novel early warning system for fake news
Principal Investigator
Prof. Sayan Gupta
Objective
- This study will adopt a multi-scale approach combining mathematical modeling, analytical methods, simulations, and machine learning to study the formation of Turing instability.
Description
- Social media has provides an easy platform for the unscrupulous to spread fake news. Fake news create disruptions in the society, create law and order situations, affect businesses and large corporations with implications to the stock market, tarnish reputations and affect the well being of both celebrities and common people. There is therefore an urgent need to understand the phenomenological issues related to fake news propagation and developing technologies to combat their spread and misuse.
Impact
- A calibrated and validated mathematical model that can simulate the spatiotemporal propagation of a news item and classify its potential to go viral based on early dynamic signatures., A software prototype that ingests real-time social media meta-data and provides a risk score for a piece of information based on its dynamical properties, not its content., Based on the identified pattern, the model will recommend targeted interventions., The possibility of patenting the developed technology and exploring the commercial viability of the developed technology will also be explored.