Mini Course on Information Diffusion in Social Networks

A 3-part minicourse on social networks: learn targeting, influence, and polarization through interdisciplinary theory, data, and models.

Guest Speaker: Francis Bloch

Université Paris 1 Panthéon-Sorbonne / Paris School of Economics

Venue: 12 Grafton Road, Sir Owen G Glenn Building, Room 260-325

Dates and times:

• Lecture #1: 14 April 2026, 10am to 12pm
• Lunch on the Social Bridge on Level 6: 14 April 2026, from 12pm to 2pm
• Lecture #2: 14 April 2026, 2pm to 4pm
• Lecture #3: 15 April 2026, 10am to 12pm

Organisers and Sponsors:

This event is made possible by the generous support of the Visiting Scholar Programme within the Faculty of Business and Economics and is hosted under the auspices of the Centre for Mathematical Social Science (CMSS), The University of Auckland.

Course description: The emergence of digital social media, by drastically reducing communication costs, has greatly transformed the way information is diffused, allowing for targeted advertising, disinformation campaigns, spread of rumors and influence. Understanding how information is diffused in social networks has thus become a matter of great importance, both in positive terms – to analyze and measure the importance of the phenomenon – and normative terms – to help policy makers regulate digital social platforms to prevent harmful effects of information diffusion.

At the cross-roads of several disciplines (economics, mathematics, computer science, marketing, sociology, political economy, law and governance), a new literature has developed in the past twenty years to better understand mechanisms of diffusion in social networks and, with the help of data from digital social media like X or Facebook, quantify the importance of information diffusion. The objective of this minicourse is to review the main findings of this literature and map the current research frontier emphasizing open problems that researchers are working on today.

The minicourse consists of three self-contained lectures dealing with (i) targeting and seeding, (ii) influence and strategic manipulation of information and (iii) opinion dynamics and polarization. Each of the lectures will cover both theoretical models, numerical simulations and empirical work. The lectures will also be an opportunity to learn about tools and models, imported from epidemiology, statistical physics, computer science, graph theory and linear algebra, which are commonly used by researchers working on social networks.

The first lecture, on targeting and seeding, will start with a description of early experiments in sociology on mapping information diffusion. It will continue with a description of landmark papers in computer science discussing the problem of influence maximization – how to identify seeds in a network to maximize the diffusion of information. It will then cover an extensive literature in marketing on the two-step theory of influence which uses numerical simulations to compare different methods of spreading information using different sets of initial influencers. It will close with a recent paper arguing that targeting may not be as important as predicted by earlier models.

The second lecture, on influence and disinformation, will start with a description of evidence on disinformation in political campaigns and posting of reviews. It will then cover theoretical models in economic theory on rational diffusion of rumors in networks, showing how strategic agents can distort messages and exploit social networks to spread false messages. It will also discuss the business models of influencers who must trade-off two sources of revenues: the fees they receive from companies whose products they promote, and the advertising revenues obtained as a function of their number of followers. It will end with a discussion of optimal policies to limit disinformation.

The third lecture, on opinion dynamics and polarization, will first briefly describe models of social learning in networks highlighting the non-Bayesian model of opinion dynamics proposed by De Groot in 1974. It will cover some of the extensive literature in statistical physics and economics which proposes different variants of the model, emphasizing recent advances which show how polarization emerges naturally in societies, or how influencers can manipulate the formation of opinions. It will end with a description of recent empirical studies exploiting data from Facebook to better measure the importance of digital social networks in opinion formation and economic outcomes.

Bio: Professor Francis Bloch graduated from HEC and received a Ph.D. in Economics at the University of Pennsylvania. He taught at Brown University, HEC Paris, Université Catholique de Louvain, Université d'Aix-Marseille and Ecole Polytechnique. He is currently Professor of Economics at Université Paris 1 and the Paris School of Economics.

His research interests are coalition and network theory, and he has also worked on applications in Industrial Organization, Public and Development Economics. His research has been published in top economics journals, such as, the American Economic Review, Rand Journal of Economics, Journal of Economic Theory, Theoretical Economics, American Economic Journal: Microeconomics, Games and Economic Behavior.

He has also done consulting work on internet taxation for the European Union, the French Prime Minister's Office and the French Ministry of Foreign Affairs, and on infrastructure tariffs for the Société du Grand Paris.

He has been an Associate Editor of Econometrica, Games and Economic Behavior and the Journal of Public Economic Theory and is currently a member of the editorial board of Economics Letters and Theoretical Economics.

He is a Fellow of the Econometric Society (since 2025), of the Society for Advancement of Economic Theory (since 2019) and of the Game Theory Society (since 2018).

He is currently a scientific advisor to the French Minister of Higher Education and Research.

Selective bibliography:

Akbarpour, M., Malladi, S., & Saberi, A. (2025). Just a few seeds more: The value of network data for diffusion. American Economic Review, 115(11), 3713-3748.

Bloch, F., Demange, G., & Kranton, R. (2018). Rumors and social networks. International Economic Review, 59(2), 421-448.

Chatterjee, K., & Dutta, B. (2016). Credibility and strategic learning in networks. International Economic Review, 57(3), 759-786.

Chetty, R., Jackson, M. O., Kuchler, T., Stroebel, J., Hendren, N., Fluegge, R. B., ... & Wernerfelt, N. (2022). Social capital I: measurement and associations with economic mobility. Nature, 608(7921), 108-121.

Chetty, R., Jackson, M. O., Kuchler, T., Stroebel, J., Hendren, N., Fluegge, R. B., ... & Wernerfelt, N. (2022). Social capital II: determinants of economic connectedness. Nature, 608(7921), 122-134.

Coleman, J., Katz, E., & Menzel, H. (1957). The diffusion of an innovation among physicians. Sociometry, 20(4), 253-270.

DeGroot, M. H. (1974). Reaching a consensus. Journal of the American Statistical Association, 69(345), 118-121.

Fainmesser, I. P., & Galeotti, A. (2021). The market for online influence. American Economic Journal: Microeconomics, 13(4), 332-372.

Grabisch, M., Mandel, A., Rusinowska, A., & Tanimura, E. (2018). Strategic influence in social networks. Mathematics of Operations Research, 43(1), 29-50.

Kempe, D., Kleinberg, J., & Tardos, É. (2003, August). Maximizing the spread of influence through a social network. In Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 137-146).

Watts, D. J., & Dodds, P. S. (2007). Influentials, networks, and public opinion formation. Journal of Consumer Research, 34(4), 441-458.