Santiago González Silot Portfoltio - NLP Data Scientist

🎓👨🏽‍💻 My Thesis - fIlfA: Fake News Detection using BERT, Transformers and NLP 📰⁉️

This poster briefly summarizes the work done during my thesis:

• An analysis of the main datasets available for the detection of fake news has been carried out.
• Comparison between 7 BERT and RoBERTa models (4 for English and 3 for Spanish) with 4 different optimization and regularization techniques specialized for word embeddings. Giving a total of 28 different models tested.
• Results very close to the winners of the Iberlef and Constraint AAAI competitions were obtained using a considerably simpler model.
• The project resulted in the publication of a paper in the International Seminar on Artificial Intelligence and Disinformation. https://sites.google.com/go.ugr.es/aidisinfo2022/registration?authuser=0&pli=1
• Implementation of a basic web interface for the use and access to the models. Currently it can be accessed through HuggingFace: https://huggingface.co/spaces/sgonzalezsilot/Fake-News-Twitter-Detection_from-my-Thesis

Model	F1-Score	Place in the competition	Difference with the winner
English	98.41	8	0.2
Spanish	73.77	5	2.89

Model architecture

Fake News in English

Fake News in Spanish

Project 1: Analysis of +4000 TED Talks topics using document clustering:

• Comparison of clustering formed using tf-idf and word embeddings using the most commons clustering algorithms like KMeans, Gaussian Mixture Models and Agglomerative Clustering.
• Tuning of the hyperparameters of all models.
• Comparaison of the results using multiple clustering metrics (DBI, Silhoutte and Calinski).
• Bonus experiment using only the most relevant tf-idf words and partly solving the curse of dimensionality.
• Bonus experiment using word embeddings from Microsoft MiniLM-L12-H384.
• Final analysis using wordclouds and n-grams to identify the topics.
• Found insights about which algorithms and metrics work best for document clustering and why.
• I used cuML, Spark (PySpark) and sentence-transformers.

Project 2: Pneumonia Detection with Chest X-Ray Images:

• Build a CNN (Convolutional Neural Network) to detect pneumonia with chest x-ray images.
• Fine-Tuned Resnet50 (trained with Imagenet) to obtain 94.54% accuracy.
• We apply ImageDataGenerator to balance the classes.
• We used TensorFlow and Scikit-Learn.

Still in progress…