Recent advances in the availability of cheap computational power and real-world data present researchers with an unprecedented opportunity to construct and test mathematical models of human behaviour. One aspect of human behaviour of particular interest to complex systems modellers is opinion dynamics; the evolution of opinions resulting from the many interactions between individuals in a social network. Understanding these dynamics will in turn be crucial in addressing many societal problems, ranging from financial crises to climate change.
At INET, we draw upon and develop a variety of tools to study opinion dynamics, including but not limited to: developing both agent-based and continuum models of sentiment contagion in social networks; advancing the state of the art in statistical inference for complex simulation models to reproduce real-world data as faithfully as possible using these models; and exploring the use of natural language processing to extract meaningful signals from the large swathes of textual data being generated through our collective digital activities.
Our work in this area includes:
- Using social media data to test psychological theories
- Quantifying social contagion in stock market movements
Using social media data to test psychological theoriesSocial media data presents us with an unparalleled opportunity to study human psychology on a large scale and in arguably more realistic settings than the psychological sciences have historically been afforded. This raises the question of whether analysis of the text generated by social media users can be used to assess the accuracy of psychological theories, which have largely been developed on the basis of experiments performed under artificial or hypothetical circumstances. In this project, we used tweets generated by millions of Twitter users during the COVID-19 pandemic to try to assess the accuracy of the theory of psychophysical numbing, which may be summarised informally with the phrase: “the more who die, the less we care”. Our approach can be used to track the public’s reactions to evolving crises, such as the COVID-19 pandemic, and therefore to guide communication with the public during crisis scenarios by monitoring indicators of the public’s perception of risk in near-to-real-time. Key findings:
Key publications:
Funders include: EPSRC Centre for Doctoral Training in Industrially Focused Mathematical Modelling (EP/L015803/1) in collaboration with Improbable, Conacyt-SENER: Sustentabilidad Energética scoloraship Researchers involved: |
Quantifying social contagion in stock market movementsSocial media has changed the fabric of society. Polarisation, the spread of fake news, and other societal challenges are some of its documented consequences. As many as 4.20 billion people, or 53.6% of the world population, are active social media users, each just a few clicks away from the next popular phenomenon. Now, a growing audience turns to social media for promising stock market gambles. Our research sheds light on the social dynamics driving a new way of investing. In particular, we use text data from the r/WallStreetBets site to follow the investment choices users talk about, and who they talk with. r/WallStreetBets started its ascent to popularity in 2015, incidentally the same time the popular trading app Robinhood became available. Key findings:
We validate the existence of a causal link between WallStreetBets activity and the stock market Key publications:
Impact:
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