Technical workshop


Simone Scardapane - Rome ML & Data Science Meetup

Challenge #2

Make news real again!

Your challenge is to build a model which will make it easier and more efficient to identify what really is fake news and what is not (+ everything in between). You model should be able to determine a level of credibility, content authenticity, and limit the viral spread of fake content, including fake images.

The dataset contains text and metadata from 244 websites [...] Each website was labeled according to the BS Detector [...] There are (ostensibly) no genuine, reliable, or trustworthy news sources represented in this dataset (so far), so don't trust anything you read.

Another example dataset:

Stance Detection involves estimating the relative perspective (or stance) of two pieces of text relative to a topic, claim or issue. [...] we have chosen the task of estimating the stance of a body text from a news article relative to a headline. Specifically, the body text may agree, disagree, discuss or be unrelated to the headline.

Baseline system: bag-of-words + standard classifier



Advanced concepts: vector space models


Advanced concepts: bidirectional recurrent networks


Additional topics

  • Handling metadata information
  • Fake images
  • Additional data sources

Challenge #3

Emotional AI

Your challenge is to build a model (from scratch or on top of an existing model) that is smart enough to recognize various emotions through voice or facial expressions.

Convolutional neural networks

Google Cloud ML - Recognizing emotions

Microsoft cognitive services

Google Video Intelligence API

Good luck to all teams!

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