Dynamics of information propagation through social media networks and measures for mitigating spread of fake news

Disinformation and propaganda have emerged as critical challenges in the digital era,;particularly within social media ecosystems. The rapid dissemination of false or misleading;content can have profound consequences, including the erosion of public trust, the distortion;of political discourse, create law and order situations, affect business and large corporations;affecting the stock market, tarnish reputations and affect the well-being of individuals [1,2].

;Unlike misinformation—often a result of errors or misunderstandings—disinformation is;strategically designed to deceive, manipulate, and polarize audiences, making it a more potent;threat. The United Nations have identified fake news as one of the challenges faced by the;global society and have emphasized the role of governments in countering false narratives;[3].

There is therefore an urgent need to understand the phenomenological issues in fake;news propagation and developing technologies to combat their spread and misuse.;Existing technologies for fake news identification are built on ML/AI algorithms that are based;on content comparisons with existing databases and can be classified as knowledge-based,;language-based, typically machine learning and/or hybrid approaches and could be topic;agnostic [4,5].

However, the lack of a theoretical and empirical framework for understanding;how disinformation spreads, evolves, and gains traction within social networks presents a;significant gap in the literature.

;Since fake news propagation is essentially a dynamical phenomenon,the proposed study aims;to carry out a theoretical/numerical investigation of the dynamical characteristics of news;propagation by adopting agent based mathematical models [6], using data gleaned from social;networks. The key research questions to be addressed (but not limited to) are;

1. What are the necessary or sufficient conditions for news virality?;Identifying the structural and contextual factors that enhance the likelihood of;disinformation becoming widespread.;

2. Are there measurable precursors that can serve as early indicators of;disinformation?;Developing predictive models to identify misinformation before it reaches critical mass.;

3. What effective mitigation strategies can be designed to counteract disinformation;propagation?

Exploring intervention mechanisms, including algorithmic filtering, network-based;disruption strategies, and targeted counter-messaging.;The findings from this study will have significant impact for various stakeholders, including;policymakers, technology companies, and fact-checking organizations on policy development,;fake news detection, public awareness and strategic interventions.