How the latest technology and some healthy activism can curb fake news

The main take away from our research is that when it comes to preventing the spread of fake news, privacy is key.

The term “fake news” has become ubiquitous over the past two years. The Cambridge English dictionary defines it as “false stories that appear to be news, spread on the internet or using other media, usually created to influence political views or as a joke”.

As part of a global push to curb the spread of deliberate misinformation, researchers are trying to understand what drives people to share fake news and how its endorsement can propagate through a social network.

But humans are complex social animals, and technology misses the richness of human learning and interactions.

That’s why we decided to take a different approach in our research. We used the latest techniques from artificial intelligence to study how support for – or opposition to – a piece of fake news can spread within a social network. We believe our model is more realistic than previous approaches because individuals in our model learn endogenously from their interactions with the environment and not just follow prescribed rules. Our novel approach allowed us to learn a number of new things about how fake news is spread.

The main take away from our research is that when it comes to preventing the spread of fake news, privacy is key. It is important to keep your personal data to yourself and be cautious when providing information to large social media websites or search engines.

The most recent wave of technological innovations has brought us the data-centric web 2.0 and with it a number of fundamental challenges to user privacy and the integrity of news shared in social networks. But as our research shows, there’s reason to be optimistic that technology, paired with a healthy dose of individual activism, might also provide solutions to the scourge of fake news.

Modelling human behaviour

Existing literature models the spread of fake news in a social network in one of two ways.

In the first instance, you could model what happens when people observe what their neighbours do and then use this information in a complicated calculation to optimally update their beliefs about the world.

The second approach assumes that people follow a simple majority rule: everyone does what most of their neighbours do....Read More

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