Methods for the analysis of spreading phenomena in networks, with a focus on the online spreading of political ideas through visual content

Name: Alexandra Segerberg

Project title: Methods for the analysis of spreading phenomena in networks, with a focus on the online spreading of political ideas through visual content

Department: Department of Government

Area of research: Digital media and politics

project summary

Images are a powerful means of sharing information and have been shown to increase the probability of information spreading and user engagement. In particular, images can be used to spread political messages (for example, for or against climate change mitigating actions), contributing to the definition of the public agenda. Current methods to study political communication through visual content rely on the manual inspection of selected pictures by domain experts, often in print news media. This is an approach that cannot scale to the complex contemporary communicative ecology made of multiple interconnected social and traditional media. This project will address the following research questions: How can we use AI methods rooted in network analysis to extend our ability to map and interpret online visual narratives in depth, at scale and across multiple social networks? What visual narratives around climate change exist and how can they be characterised with respect to their visibility, engagement, polarisation, and temporal evolution?

What do you look forward to the most during your sabbatical?

I’m looking forward to two things. The first is the opportunity to develop this project together with Matteo Magnani (also an AI4Research scholar). Ours is a cross-faculty collaboration, so getting the chance work at the same place for a sustained amount of time will be invaluable for grounding a shared base to build on. The other is the contact with other projects in and around the research hub. An underlying aim of the collaboration is to open up not just possibilities, but also challenges and implications of implementing AI and deep learning methodologies for social science purposes. The chance to have a broad and informal discussion across projects and disciplines will enrich this process.