In 2016 a researcher, Vicky Chuqiao Yang started work on computer simulations of US politics. It was part of social science revolution in interrogating data.
Mitchell Wardrop, in a PNAS news feature (8 September 2021), said Yang “was fascinated by the realisation that the ‘left right’ standoff widely described as ‘polarisation’ was not one thing.”
Yang, an applied mathematician at the Sante Fe Institute in New Mexico, argued that: “There are two types of polarisation that the media and the public often get confused.”
One type is issue polarisation: “how much people disagree on policies like what should be the tax rates or what should be the laws to regulate guns.”
Wardrop says that while those divisions have been widening they aren’t “nearly as incendiary as social or ‘affective’ polarisation which is about anger, distrust, resentment, tribal identity and mutual loathing.”
“Researchers are trying to understand why social polarisation is on the rise and – perhaps more important – what can we do about it. Can we find solutions by focussing on racial anxieties, conspiracy theories, and social media echo chambers that endlessly reinforce a single viewpoint. Or do we also need to look for more fundamental forces at work”
Wardrop reports that Dartmouth University in the US is exploiting the fact that researchers now have the computational power to run complex simulations and models as well as access to huge amounts of real-world data on political opinion.
While there is a risk involved in this, as Michael Mas at Karlsurhe Instiute of Technology in Germany says: “we always have to be very careful with drawing conclusions about how to intervene because these models are based on assumptions. So if these assumptions happen to be false, even in small ways, the predictions can change dramatically.”
Wardrop outlines four basic strategies to address the problems. Michael Macy at Cornell University is using all four but stresses the classic method of observation using surveys and historical data to track whether polarisation has increased or decreased over time drawing on surveys which go back to the 1990s
The second is to analyse the data now available from the Internet. “It’s sort of like the survey research except that it’s actual behaviour observed in online communications: but which allows you to study things you can’t get from surveys such as “who listens to whom and how ideas spread through the resulting social network like a contagion,” Macy says.
A third strategy is the experimental approach in which you observe the actual behaviour of communities and social media. The fourth strategy is the use of models in the form of mathematical equations or computer simulations.
Macy suggests that the interplay of two sociological forces have produced some of the most interesting results: the ‘influence’ effect which shows how people who interact a lot end up thinking and acting similarly; and homophily which describes how people are drawn together. In 1977 Robert Axelrod developed a model of culture formation which anticipated much of the current US Republican-Democrat split and the emergence of powerful echo chambers. Axelrod said: “I wasn’t interested in the left-to-right kind of differences, so I treated ‘culture’ simply as a list of arbitrary features that were observable like ‘What kind of hat do you wear’ or ‘What ethnicity are you?’”
His modelling of people as ‘agents’ interacting with each other led some to converge and others to never talk to the others again.
A significant factor in this is the role played by negative emotions which can turn both influence and homophily inside out. Wardrop cites Noah Friedkin from UC Santa Barbra who suggests this stems from the concept of balance theory developed in the 1940s and 1950s in which how people’s opinions and feelings reinforce one another through a feedback loop. If you like someone you start to think and act like them and if you don’t the reverse occurs.
Friedkin says in the balance theory situation there is a complicated evolution of feelings and opinions which reaches a stable equilibrium. There are only two situations possible in this theory – “one big happy clique where everyone is friends with everyone else” or the second where “the group splits into two cliques that are at each other’s throats.”
Much of this might be random choice according to David Garcia of Graz University of Technology in Austria who was part of a team which looked at a balance theory model where participants could have opinions on many issues. Quite quickly the adherents reached the ‘if you’re not with me you’re against me’ position.
Graz mused as to whether much of our left-right division isn’t about issues but it a result of random choice. This musing was given some substance by Macy in 2019 when he was part of team which recruited 4,000 self-identified Republicans and Democrats and asked their opinions on “emerging controversies’. No-one had a pre-existing position because the issues were made up for the experiment.
Whenever one group were aware of what earlier participants had said before making their own choice Republicans would combine on one side and Democrats the other. In subsequent trials when different subjects were asked about the same made-up issue the two parties would often end up with reversed positions.
…and Wardrop says if you are optimistic about reforming social media as a panacea think again as researchers “are still arguing about how much they actually contribute to polarisation. According to some studies, in fact, the algorithms that determine what users see in their feeds are just bit players; most of the online divisions come from people sorting themselves the way they always have, through birds of a feather homophily.”
Moreover, when Chris Bail (see Part 1) and team tested an idea for opening up the echo chambers by getting 1600 Republicans Democrats to follow bots which periodically showed them tweets from the other party it didn’t lead to moderation but people mostly recoiled from discordant information “and Republicans, in fact, became significantly more conservative.”
And bad news for parties spending millions on advertising in in the forthcoming Australian election, Wardrop quotes research by Antonio Sirianni of Dartmouth University that a campaign which pushes too hard or too much political advertising, might actually undermine candidates by radicalising their base and rendering them less likely to bring other people across to their views.