Using network motifs to characterize temporal network evolution leading to diffusion inhibition

Demographics and topics impact on the co-spread of COVID-19 misinformation and fact-checks on Twitter

Correcting misconceptions and false beliefs are important for injecting reliable information about COVID-19 into public discourse, but what impact does this have on the continued proliferation of misinforming claims? Fact-checking organisations produce content with the aim of reducing misinformation spread, but our knowledge of its impact on misinformation for particular topics and demographics is limited. In this article, we explore the relation between misinformation and fact-checking spread during the COVID-19 pandemic for different topics, user demographics and attributes. We specifically follow misinformation and fact-checks emerging from December 2019 until the 4th of January 2021 on Twitter. Using a combination of spread variance analysis, impulse response modelling and causal analysis, we highlight the bidirectional, weak causation spread behaviour between misinformation and fact-checks. Although we observe that fact-checks about COVID-19 are appearing fairly quickly after misinformation is circulated, its ability to reduce overall misinformation spread appears to be limited. This is especially visible for misinformation about conspiracy theories and the causes of the virus.

Subgraphs of Interest Social Networks for Diffusion Dynamics Prediction

Finding the building blocks of real-world networks contributes to the understanding of their formation process and related dynamical processes, which is related to prediction and control tasks. We explore different types of social networks, demonstrating high structural variability, and aim to extract and see their minimal building blocks, which are able to reproduce supergraph structural and dynamical properties, so as to be appropriate for diffusion prediction for the whole graph on the base of its small subgraph. For this purpose, we determine topological and functional formal criteria and explore sampling techniques. Using the method that provides the best correspondence to both criteria, we explore the building blocks of interest networks. The best sampling method allows one to extract subgraphs of optimal 30 nodes, which reproduce path lengths, clustering, and degree particularities of an initial graph. The extracted subgraphs are different for the considered interest networks, and provide interesting material for the global dynamics exploration on the mesoscale base.

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the "pattern" by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.