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.