Abstract: 

Fake news has a negative impact on individuals and society, hence the detection of fake news is becoming a bigger field of interest for data scientists. Attempts to leverage artificial intelligence technologies particularly machine/deep learning techniques and natural language processing to automatically detect fake news and prevent its viral spread have recently been actively discussed. However, assessing news veracity, particularly in social media, is a challenging problem. 
 
Through use cases and examples, we will discuss the different fake news detection approaches from feature extraction to model construction.
We will focus on how to leverage NLP to characterize and extract discriminative features of fake news by analyzing its text content.
We will take a gentle and detailed tour through text processing techniques used to extract both lexical and syntactic features including word embedding (word2vec), bag-of-words, n-gram models, etc.
We will guide you through the process of building ML models and demonstrate the best practices to tune its parameters.
Finally, we will extend our analysis to explore new key features of fake news that are arisen by social media like news proliferation patterns, and user/publisher profile.

Bio: 

Sihem Romdhani received her MASc degree in Machine Learning from the department of Electrical and Computer Engineering at the University of Waterloo, where her research was focused on Deep Learning for Speech Recognition. She is currently working with Veeva Systems as a Machine Learning Engineer and Data Scientist, where she is building ML models for Natural Language Processing. She has led multiple projects on text parsing, sequence tagging, and information extraction from unstructured text data. Sihem is very interested in Deep Learning and how to apply it to solve new and challenging problems. Throughout her education, academic research, and work in industry, she gathered experiences and knowledge that she enjoys sharing by actively doing public presentations.