Fake Bananas is a fake news detector web app based on stance detection, natural language processing and machine learning. At HackMIT 2017, Fake Bananas finished in the top 10 teams out of over 400 teams and 1250 hackers. Fake Bananas also won Best AI/Hack for Social Good from Baidu and the prize for the Most Interesting Use of Data from Hudson River Trading.

Our fake news detection is based on the concept of stance detection. Fake news is tough to identify. Many 'facts' are highly complex and difficult to check, or exist on a 'continuum of truth' or are compound sentences with act and fiction overlapping. The best way to attack this problem is not through fact checking, but by comparing how reputable sources feel about a claim.

How FakeBananas works:

  1. 1. Users input a claim like "The Afghanistan war was bad for the world"
  2. 2. Our program will search the thousands of global and local news sources for their 'stance' on that topic.
  3. 3. We run sources through our Reputability Algorithm. If lots of reputable sources all agree with your claim, then it's probably true.
  4. 4. Then we cite our sources so our users can click through and read more about that topic!
I was primarily in charge of web scraping for articles. Given a user URL or claim, I used Microsoft's Azure Cognitive and IBM's Natural Language Processing to parse the article or claim and perform keyword extraction. I then used combinations of the keywords to collect up to a few thousand articles from Event Registry's database to pass on to the machine learning model. Here I aired on the side of collecting more rather than fewer articles because the machine learning will accurately determine relevancy further in the pipeline.

Moving forward, we hope to launch a public facing web application and potentially even a browser plug-in that can detect news articles and display what our pipeline returns.

-- Link to Github --

About Me

I am a computer science and cognitive science double major at Swarthmore College graduating in the Spring of 2020.
In addition, I am also fascinated by psychology, neuroscience, and nutrition. In my free time, I like to play baksetball and workout, cook, make/listen to music, and go chasing sunsets. I play many instruments, but mainly the piano and GuZheng. My favorite classical pieces include Grades etudes de Paganini by Franz Liszt, all three of Rachmaninioff's Piano Concertos, and Chopin Scherzo No.3 in C Sharp Minor, Op.39.

In computer science, I am passionate about parallel and distributed systems and machine learning applications. In cognitive science, I am fascinated by the microbiome of the gut, the psychology of language, as well as physiological psychology.

On a journey to quench my thirst for knowledge