For misinformation to have an impact, it needs to go viral. So, it is not surprising that misinformation shares a lot with clickbait and often aims at nothing more than just that. To bait to click.
Several studies have shown that emotionally arousing stories tend to attract audience selection and exposure. There is no doubt that emotionally evocative content is more ‘viral’ than neutral content. The more the anger or anxiety it evokes, the faster and more broadly it spreads. There is also a lot of evidence that emotions impact memories. Emotional memories are vivid and lasting but not necessarily accurate, and under some conditions, emotion even increases people’s susceptibility to false memories.
More directly on misinformation, a study by Brian Weeks of the University of Michigan demonstrated that anger encourages partisan, motivated evaluation of uncorrected misinformation that results in beliefs consistent with the supported political party, while anxiety at times promotes initial beliefs based less on partisanship and more on the information environment. Another study by Northeastern University involving 5,303 posts with 2,614,374 user comments from popular social media platforms, showed more misinformation-awareness signals and extensive emoji and swear word usage with false posts. Misinformation often uses inflammatory and sensational language to alter people’s emotions.
So, what can one do with this knowledge? A good approach is to make sure you take a moment to think when you encounter highly emotive language. An even better way may be to use EUNOMIA’s information cascade functionality, which visualises the sentiment expressed by all the posts that contain the same information. Language that is highly negative is not by itself a sign of mal-intent, but can be one if combined with other indicators.
Weeks, B. E. (2015). Emotions, partisanship, and misperceptions: How anger and anxiety moderate the effect of partisan bias on susceptibility to political misinformation. Journal of communication, 65(4), 699-719.
Jiang, S. and Wilson, C. (2018). Linguistic signals under misinformation and fact-checking: Evidence from user comments on social media. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1-23.