MIT: Measuring Media Bias in Major News Outlets With Machine Learning

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A study from MIT has used machine learning techniques to identify biased phrasing across around 100 of the largest and most influential news outlets in the US and beyond, including 83 of the most influential print news publications. It's a research effort that shows the way towards automated systems that could potentially auto-classify the political character of a publication, and give readers a deeper insight into the ethical stance of an outlet on topics that they may feel passionately about. The work centers on the way topics are addressed with particular phrasing, such as undocumented immigrant illegal Immigrant, fetus unborn baby, demonstrators anarchists. The project used Natural Language Processing (NLP) techniques to extract and classify such instances of'charged' language (on the assumption that apparently more'neutral' terms also represent a political stance) into a broad mapping that reveals left and right-leaning bias across over three million articles from around 100 news outlets, resulting in a navigable bias landscape of the publications in question. The paper comes from Samantha D'Alonzo and Max Tegmark at MIT's Department of Physics, and observes that a number of recent initiatives around'fact checking', in the wake of numerous'fake news' scandals, can be interpreted as disingenuous and serving the causes of particular interests.