Join us for talks, lunch, and catching up with networks researchers at IU
Join us for the second lunch colloquium in our spring 2023 series! IUNI will provide lunch for all in-person participants.
Speakers: Fil Menczer (IUNI Advisory Council Member), Matthew DeVerna, Bao Tran Truong, and Rachith Aiyappa.
Register today— in-person spots are limited! If you're unable to attend in person, Zoom attendance is available.
Title: Hacking Online Virality
Abstract: As social media have become major channels for the diffusion of news and information, it is critical to understand how the complex interplay between cognitive, social, and algorithmic biases triggered by our reliance on online social networks makes us vulnerable to manipulation and disinformation. We focus on two key factors that contribute to online virality: the structure of the social network and the engagement mechanisms that manage our limited attention. This talk overviews ongoing network analytics, modeling, and machine learning efforts to study the viral spread of misinformation and to develop tools for countering the online manipulation of opinions.
Bio: Filippo Menczer is the Luddy distinguished professor of informatics and computer science and the director of the Observatory on Social Media at Indiana University. He holds a Laurea in Physics from the Sapienza University of Rome and a Ph.D. in Computer Science and Cognitive Science from the University of California, San Diego. Dr. Menczer is an ACM Fellow and a board member of the IU Network Science Institute. His research interests span Web and data science, computational social science, science of science, and modeling of complex information networks. In the last ten years, his lab has led efforts to study online misinformation spread and to develop tools to detect and counter social media manipulation.
Title: "Belief Networks and Social Contagion"
Abstract: Social contagion is a ubiquitous and fundamental process that drives social changes. Although social contagion arises as a result of cognitive processes and biases, the integration of cognitive mechanisms with the theory of social contagion remains as an open challenge. In particular, studies on social phenomena usually assume contagion dynamics to be either simple or complex, rather than allowing it to emerge from cognitive mechanisms, despite empirical evidence indicating that a social system can exhibit a spectrum of contagion dynamics — from simple to complex — simultaneously. This work proposes a model of interacting beliefs as a unifying framework, from which both simple and complex contagion dynamics can organically arise. Our model also elucidates how a fundamental mechanism of complex contagion — resistance — can come about from cognitive mechanisms. Our model may offer a unifying framework to study both simple and complex contagion dynamics in social systems.
Bao Tran Truong
Title: "Vulnerabilities of the Online Public Square to Manipulation"
Abstract: Social media, the modern public square, is vulnerable to manipulation. By controlling inauthentic accounts impersonating humans, malicious actors can amplify disinformation within target communities. Despite their potential magnitude, the consequences of such operations are difficult to evaluate due to the ethical challenges posed by experiments that would influence online communities. Here we use a social media model that simulates information diffusion to quantify the impacts of adversarial manipulation tactics on the quality of content in an empirical network. We find that social media features such as high information load, limited attention, and the presence of influentials exacerbate the vulnerabilities of online communities. Infiltrating a community is the most harmful tactic that bad actors can exploit to spread low-quality content virally. The harm is further compounded by inauthentic agents flooding the network with deceptively engaging content, but is mitigated when influential or vulnerable individuals are targeted. These insights suggest countermeasures that platforms could employ to increase the resilience of social media users to manipulation.
Title: "Modeling the effect of misinformation on the spread of disease"
Abstract: Understanding how misinformation influences the spread of disease is crucial for public health, especially given recent research indicating that misinformation can discourage vaccine uptake and increase vaccine hesitancy. This work presents an agent-based Susceptible Infected Recovered (SIR) model that accounts for the presence of both “misinformed" and “ordinary" individuals in the population. Coupling large-scale Twitter data about COVID-19 discussions with county-level voting records and cellphone mobility data, we construct a data-driven physical contact network consisting of 20 million nodes, marked as ordinary or misinformed. Utilizing this network and the proposed SIR model, we simulate the influence that misinformed individuals have on the spread of disease and find that this group can have a small but significant negative effect on spreading dynamics. Furthermore, we demonstrate that our model correlates well with actual county-level data.