weboftruth is a project to use deep representation learning to ascertain the credibility of a statement given past context. It is a proof-of-concept for one approach to algorithmic fake news detection.

This repo contains Python and bash scripts for training Knowledge Graph embeddings using Subject-Verb-Object triples.

Two embedding spaces are created (one for Entities (Subjects/Objects) and one for Relationships/Verbs). Extensive use is made of the package torchkge that implements KGE algorithms like TransE. It is built on PyTorch.

• We trained Entity and Relationship embeddings for a cleaned dataset of SVO triples constructed from Wikipedia sentences.
• We trained three configurations - embeddings of dimension 50, 100 and 200.
• A binary classifier (logistic regression) on concatenated SVO embeddings recorded 79% accuracy at telling true triples apart from false (negatively sampled) ones

This was part of a coursework for CAPP 30255 at the University of Chicago by Aabir Abubaker Kar Adarsh Mathew

• data

  contains data sources used, specifically the SVO dataset of subject-verb-object triples from Wikipedia, and our constructed 'partially true' datasets

• logs

  execution logs for RCC and AWS runs

• notebooks

  Jupyter notebooks used for early prototyping

• shscripts

  shell scripts used for RCC

• weboftruth

  Python code organized into a package. Borrows heavily from torch and torchkge

  • utils.py: miscellaneous useful functions for loading models, data etc.
  • corrupt.py: code for generating the partially true datasets and writing to disk
  • svofunctions.py: tools to translate triples to indices and vice versa
  • wotmodels.py: tools to build and train models from torchkge
  • evaluator.py: functions to evaluate the performance of embeddings as features in truth prediction