The first major study of social media behaviour during wartime has found that posts celebrating national and cultural unity in a country under attack receive significantly more online engagement than derogatory posts about the aggressors.
University of Cambridge psychologists analysed a total of 1.6 million posts on Facebook and Twitter (now X) from Ukrainian news outlets in the seven months prior to February 2022, when Russian forces invaded, and the six months that followed.
Once the attempted invasion had begun, posts classified as expressing Ukrainian "ingroup solidarity" were associated with 92% more engagement on Facebook, and 68% more on Twitter, than similar posts had achieved prior to Russia's full-scale attack.
While posts expressing "outgroup hostility" towards Russia only received an extra 1% engagement on Facebook after the invasion, with no significant difference on Twitter.
"Pro-Ukrainian sentiment, phrases such as Glory to Ukraine and posts about Ukrainian military heroism, gained huge amounts of likes and shares, yet hostile posts aimed at Russia barely registered," said Yara Kyrychenko, from Cambridge's Social Decision-Making Lab (SDML) in its Department of Psychology.
"The vast majority of research on social media uses US data, where divisive posts often go viral, prompting some scholars to suggest that these platforms drive polarisation. In Ukraine, a country under siege, we find the reverse," said Kyrychenko, lead author of the study published today in Nature Communications.
"Emotions that appeal to ingroup identity can empower people and boost morale. These emotions may be more contagious, and prompt greater engagement, during a time of active threat – when the motivation to behave beneficially for one's ingroup is heightened."
Previous research from the same Cambridge lab found that going viral on US social media is driven by hostility: posts that mock and criticise the opposing sides of ideological divides are far more likely to get engagement and reach larger audiences.
The new study initially used the same techniques, finding that – prior to the invasion –social media posts from pro-Ukrainian as well as pro-Russian news sources that contained keywords of the 'outgroup' – opposing politicians, placenames, and so on – it did indeed generate more traction than posts containing 'ingroup' keywords.*
However, researchers then trained a large language model (LLM) – a form of language-processing AI, similar to ChatGPT – to better categorise sentiment and the motivation behind the post, rather than simply relying on keywords, and used this to analyse Facebook and Twitter posts of Ukrainian news outlets before and after the invasion.**
This deeper dive revealed a consistently strong engagement rate for solidarity posting – higher than for 'outgroup hostility' – in the lead up to Russia's attack, which leaps even further after the invasion, while interactions with derisive posts about Russia flatline.
Lastly, a separate dataset of 149,000 post-invasion Tweets that had been geo-located to Ukraine was fed into a similar LLM, to test this effect on social media posts from the Ukrainian population, rather than only news sources.***
