Simone Scardapane - Rome ML & Data Science Meetup
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.
Sample dataset: https://www.kaggle.com/mrisdal/fake-news
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: http://www.fakenewschallenge.org/
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
Text Classification using Neural Networks [machinelearning.co]
Advanced concepts: vector space models
Vector representations of words [TensorFlow]
Advanced concepts: bidirectional recurrent networks
Neural Networks, Types, and Functional Programming [colah.github.io]
- Handling metadata information
- Fake images
- Additional data sources
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
Understanding convolutional neural networks for NLP [WildML.com]
Google Cloud ML - Recognizing emotions
Microsoft cognitive services
Google Video Intelligence API
Example dataset 2
Good luck to all teams!
Global AI Hackathon - Technical workshop
By Simone Scardapane