My First Week at Full Fact

Full Fact, the charity I’m currently working at, is an independent factchecking charity that “…provide free tools, information and advice so that anyone can check the claims we hear from politicians and the media.” They do factchecks in a variety of areas from the NHS to student debt, and factcheck claims made during the Prime Minister’s Questions (PMQs), among others.

While Full Fact factchecks claims in many different areas, they have yet to touch claims/questions made regarding the metaphysical realm, such as “What is life?” or “To be, or not to be?” or “I think, therefore I am.” Such questions are best left to the reader to consult, consult a Philosophy professor, or ponder about over lunch.

Full Fact currently has two factchecking tools: Live – which monitors TV subtitles and other sources and then factchecks (near instantaneously) claims for which reliable data do exist – and Trends – which seeks to determine the sources for inaccurate claims that have been repeated.

I given the task of improving on the automated factchecking process that Full Fact employs. Automated factchecking can be broken down into 3 essential stages: understanding the claim, obtaining the relevant data and finally, presenting the required data. The first stage involves Natural Language Processing (NLP), which incorporates, among other things, linguistics. The second stage entails getting facts (and not opinions) from official and impartial sources of information such as the Office for National Statistics (ONS). These facts would then be presented in the last stage, in a simple and unambiguous manner.

I spent the first week reading up a bit on NLP, and delving into the second and third stages; my focus was on GDP data from the ONS’s website and the different claims that could be made regarding such data. I wrote several Python functions for the different ways in which we could interpret these claims, and ran the GDP data through these functions/scripts for evaluation. Given a sequence of real data, it was interesting to see how one could present different intepretations of it.

My biggest takeaway so far isn’t the heavy lunch that I had at the nearby Pret a few days back, but rather, the fact that implementing an idea might not always be as easy as it seems. And that Google – and not Dogs – are man’s best friend.

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