This project asks why disinformation on social media is so successful in the realm of political speech. By analyzing the twitter accounts of fringe actors claiming to report political “news”, we explore the underlying cultural narratives that make this type of information meaningful and appealing. Do accounts that purposefully spread false information (disinformation) use the language of openness, democracy and truth, even as they muddy the political discourse on the internet? What other cultural queues do these accounts use to signal followers sympathetic to their political views? And finally, what topics generate the most engagement and popularity?*
We construct a novel labelled dataset of tweets pertaining to politically polarizing Canadian news events, mined from Twitter using keywords identified through topic models. We then present an analysis of the dataset, exploring the linguistic differences between polarizing and innocuous discourse, and use deep learning to automatically identify disinformative tweets.
*Description provided by Prof. Komeili.