Temporal networks

Spread of mood in social networks

In the last decade, modelling and analysis of dynamical process on networks has become relevant in several application areas: well-being, marketing, information retrieval, epidemiology, defence, etc.

Observing and measuring behaviour together with communications through a social network is the first step toward disentangling influence (or contagion) and selection (who is communicating with whom) processes on the network.

Several challenges arise in temporal (aka dynamic or evolving) networks with dynamical processes on them: community detection, centrality ranking of vertices and edges, computational complexity of algorithms that measure some of the networks or processes properties.

My research in this area includes the following research topics:

Large online studies

In this one year project funded by Centre for Defence Enterprise, the main aims were to investigate how moods spreads through large networks and to build a model of collective sentiment dynamics.

We analysed a large sample of UK Twitter users over several months and focused only on their tweets that contained mentions. We found [1] that users with the largest potential communication reach use positive sentiment more often and more strongly than the average user (for similar observations from small face to face studies see below).

Following Twitter communities that form around topics or friendships over a period of months, we found that their sentiment levels are relatively stable. Sudden changes in sentiment from one day to the next can be traced to external events affecting the community. We then created a simple agent-based model capable of reproducing measures of emotive response comparable to those observed.

Small human studies

In this one year research project funded by Unilever, the aim was to incorporate social psychological theories in the building of mathematical models of the spread of mood through networks. We also wanted to explore different strategies for behaviour change interventions using small scale experimental studies.

Together with Dr Robert Hurling, behaviour change expert from Unilever R&D in Colworth, we investigated temporal networks coupled with attitudes/behaviours. Our goal was to identify what is dynamics of positive and negative affect (independent constructs that are often used to describe ephemeral mood) in an evolving social network. We also looked at the optimal choice of nodes/individuals who should be triggered in order for behaviours to evolve in wanted directions.

In these projects we have conducted

- Analysis of centrality in dynamic networks

- Research on how to disentangle selection and influence processes on networks

- Analysis of different strategies when choosing "the agents of change" for a behaviour change intervention.

Our results [2,7] have shown that positive and negative affect are independent and not complementary and that they follow different dynamics in small social networks. while positive affect goes toward a mean, negative affect tends to go toward observed extremes in a social network. We were able to find evidence of both positive and negative affect contagion in small human networks. Using gratitude exercises to improve positive and reduce negative affects, we have shown that the blanket intervention works well, but a selective intervention can have qualitatively different results based on the selection process. Finally we observed that the individuals with the least negative affects levels at the beginning of the study were the best "broadcasters" - having the largest potential reach.