Final Project Slides: https://docs.google.com/presentation/d/1gN0y5BoAiCQjCHKCdz-uIiKDFmoRM2ytTTVuuPeeqlQ/edit?usp=sharing
Final Project Code: https://github.com/animekraxe/fake-news-analyzer
Given the contemporary ubiquity for charged terms such as ‘fake news’ and ‘post-truth politics,’ this project seeks to work toward a more usefully quantitative metric for measuring the veracity of allegedly factual news stories spread through social media platforms. We acknowledge the inherent politicization of inaccuracies posing as legitimate reporting in a landscape wherein value judgments too often inform factual exposure rather than vice versa, but instead of finding an excuse to dismiss ideological components from our metric here we instead see an opportunity to fold them into our process of identification and observation. Further, we speculate that in making ‘fake news’ more tangible there must exist additional commonalities, both across those social media users that proliferate these stories and within the content of the stories themselves, that could reinforce the accuracy of an identification metric.
Therefore, this project seeks to question which factors of purportedly factual news stories and the users that share them might in fact serve as reliably high indicators of ‘fake news.’ Through our consideration of available data from social media sites like Twitter and consideration of previous research and scholarship of online credibility detection we hope to propose both the most and least effective indicators of inaccurate news stories. Additionally, observations regarding the spread of the stories through user activity will inform a metric for measuring the likelihood of any given user’s propensity for fake news proliferation. From this research we hope to produce an algorithm that might begin to evaluate news stories and social media users for our stated concerns in real time.