What social science is telling us about polarisation: Part 1.

There has been an avalanche of social science research on polarisation in recent years – much of it assisted by AI driven analysis of social media; some of it building on long accepted social science findings; and some of it sadly a victim of the replication crisis which emerged in psychology some years ago.

Much of it is, inevitably, based on US experience. However, there are some emerging research centres in Australia such as the one at Queensland University of Technology of which more in a later part of this series.

The concept of cognitive biases underlies many of the polarisation and conspiracy problems such as belief perseverance in the face of overwhelming evidence in things like a vaccine hoax. Adam Rutherford and Hannah Fry sum up the problem – among many others – in their new book The Complete Guide to Everything (Abridged): Adventures in Math and Science) when they implore us all to “be vigilant…because your own brain is  trying to trick you.”

An earlier book, Breaking the Social Media Prism: How to Make our Platforms Less Polarising, by the computational social scientist Chris Bail looks at ideological polarisation, misinformation and the impact others’ content has on our own social media experiences.

He initially looks at social media structures and argues that they are designed to amplify extreme voices and downplay the views of moderates. To test the hypothesis he ran a long-term experiment with a strong Trump supporter and a left-leaning moderate and found that exposure to content from the other side of the ideological spectrum pushed the pair further towards their existing positions. The left-leaning moderate got more left wing and the Trump supporter increased her attacks on liberals.

Another experiment, however, with a simulated social media site design that brought anonymous users together for conversations found that the participants became more moderate.

But of course, people don’t go to much social media for actual conversations and Bail argues that social media platforms twist reality. As a corollary of this he argues that people have a desire to explore and experiment with their self-presentation to refine their self-exploration and identity. Social media platforms satisfy or punish this desire through likes, shares and comments.

As Jennifer Golbeck said in a Science review of the book (9 April 2021): “Bail’s scientific conclusions are refreshing in a space dominated by informed speculation, and the book offers hope that data-driven solutions can bring us back from the brink.”

In December 2021 the PNAS journal published a special feature exploring this and other issues. The feature editors – Simon A. Levin, Helen V. Miller and Charles Perrings – write in an introduction: “The papers explore the impact of information flow networks, the diverse nature of national governance systems, the role of the media and the dynamics of party sorting. They pose a number of key questions. Do the dynamics of such systems follow a natural progression of polarisation and collapse (as Schumpeter argues)? How do migration, globalisation and new technologies such as the Internet affect the trends” and does this all foreshadow “a natural tendency to polarisation in nations with two-party systems?”

A number of papers explore tip over points asking if polarisation can reach a threshold level at which it becomes a runaway process and can this be stopped.

Some of the issues addressed are what the effects of polarisation are on levels of tolerance of other views and economic shock.  How, when individual opinions are heavily shaped by peers, it leads to increasing partisan bias and factions arguing around a reduced number of issues which then lead to more polarisation.

Some of the papers also explore how the process speeds up polarisation and reinforces attitudes among party elite. Thus, the Republicans in the US have already passed a threshold on this and Democrats are following.

Needless to say this is also accentuated by political parties deliberately seeking to focus on a small number of issues, reducible to slogans, which allow them to differentiate themselves from other parties while simultaneously vilifying the others.

Other parts of the feature focus on the social media networks themselves exploring, for instance, how individuals may be influenced by the recommendations of others they are linked to. Sticking to favourite networks can lead to polarisation while exploring more widely can moderate opinion. This leads to echo chambers and the echo chamber effect then leads on to increasing polarisation.

Another paper looks at how individuals in some groups develop strong negative views towards other groups. This is culturally influenced but, interestingly much it is due to financial and social inequality, economic hardship and racial animosities interact with loyalty to political parties and generate more polarisation. Essentially this is an analysis, on the one hand, how people can vote against their own interests – as illustrated by the Trump phenomenon.

In good news for social democrats everywhere the same paper suggests that wealth redistribution and providing public goods can ameliorate the situation and reduce polarisation although this, sadly, doesn’t seem to be working for Joe Biden yet.

Why? is possibly explained in another paper which demonstrates how national commitments to international cooperation evolve as polarisation changes. The more domestic polarisation grows the more cooperation with other nations suffer. This process is helped by political parties demonising the Other and constantly referring to threats from the Other – domestically or internationally. Australia and the US are among the best examples of political discourse in a democracy being largely dependent on the creation of internal and external ‘enemies’.

In mixed news the papers look at some of the distinctive features of the current US political environment and how things could – potentially – become worse or better.

The editors conclude: “Polarisation is a process and that is what complexity theory can best help us understand. Complexity models can trace dynamics through time given some assumptions about actors’ behaviour and show possible system trajectories for polarisation. The evolution of the system as parameters change and feedback occurs is key. Each model in the special feature makes specific assumptions, and then set in motion to see how it evolves, thereby showing what can happen to polarisation under varying circumstances.”

This series has been made possible by John Spitzer’s research  in the publications PNAS and Science over the past year or so.