The Common Ground Algorithm - A Possible Remedy for Filter Bubbles

People have a tendency towards Confirmation Bias, whereby they seek out things that confirm their existing opinions and avoid things that challenge them. On social networks and recommendation systems, this can lead to the development of a filter bubble, whereby their sources of information come to be structured around what they already believe. This, of course, acts as an obstacle to healthy discussion between people of differing opinions, and causes their positions to become ever more deeply entrenched and polarised. Instead of seeing those with whom they differ as being decent people who have something of value to offer them, and who may be persuadable on some of their differences, people start seeing their opponents as the enemy. To prevent this, people need something that will put them in touch with people with whom they have generally opposing viewpoints. Of course, we can't just confront people with contrary opinions - this will risk provoking hostile reactions. What we need is to show people what they have in common with those whose opinions are different, so that they can build trust and begin to interact in a healthy way. As an attempt to do this, I present The Common Ground Algorithm. This uses a combination of topic modelling and sentiment analysis to characterise a user's opinions. It then finds people whose opinions are generally opposed to theirs, and identifies the topics on which they share common ground, recommending posts where they agree on something with people they disagree with in general. I've coded up a reference implementation in Python, and am releasing it under the MIT Licence to encourage its use and further development.