FaktNews

• 

  B for the BBC

• 

  Average veracity scores by category on BBC

Inspiration

Some member's were motivated by Black Pepper's challenge to identify Fake News, others wanted to try to use majestic's API, another simply wanted to give natural language processing a go.

What it does

FaktNews user with convenient dashboard of metrics that scrutinise a website, for it's association with other sites using the Majestic API, for it's content using NLP and sentiment analysis and for it's standing with the community (as represented by a thumbs-up/ thumbs-down feature).

How we built it

We wrote Python scripts that run from a Flask application, sending information to a chrome extension.

Challenges we ran into

It's hard to produce meaningful analysis on content, assigning truth values to articles is problematic and even more so for sites. Getting lightweight and appropriate JQuery plugins to display all the information we were generating.

Accomplishments that we're proud of

Collaborating well enough to have a working prototype. Familiarising ourselves with more libraries and better ways to do things.

What we learned

This is a massively complicated problem to tackle, interestingly there is very often very little substantive difference between a reliable and unreliable article. Ideally a program could independently fact-check, corroborating claims made by sites with substantive authorities. The Issue there would be recognising those substantive, ideally infallible sources.

What's next for FaktNews

Probably sell it for $1,000,000,000

Built With

• python
• flask
• beautiful-soup
• natural-language-processing
• javascript
• jquery
• chrome