Fake reviews detection using transfer learning

We study the effectiveness of transfer learning for the detection of fake reviews. We apply the Universal Language Model Fine-tuning (ULMFiT) by Howard and Ruder, 2018 to fake reviews detection and demonstrate that it can successully detect fake reviews.

Notebooks

Amazon_ulmfit.ipynb notebook contains the following:

• Fake reviews classifier using transfer learning (ULMFiT model)
• Performance metrics and interpretation analysis using Sequential Jacobian and LIME
• Sensitivity and robustness checks

Amazon_mlp.ipynb notebook contains the architecture, training and testing of a MultiLayer Perceptron (MLP). We use MLP as one of the baseline to compare the performance of our fake review classifier in Amazon_ulmfit.ipynb

Utils_unzip_data.ipynb contains a short code snippet to unzip data.

Running the code

Data

• create a separate data folder in your repo (and train, test, unsup subfolders if uploading extracted data manually);
• extract zip file or locate relevant files in the data folder (and relevant subfolders if uploading extracted data manually); and
• if necessary, amend the path variable in the Settings codeblock of each jupyter notebook.

Note: The train and test data can be downloaded from: https://myleott.com/op-spam.html We use additional unsup data to train the language model from: http://odds.cs.stonybrook.edu/yelpchi-dataset/

Installation requirements

The code has been developed and run on Salamander server (www.salamander.ai) which supports Pytorch v1 and Fastai v1 library. If you want to run the jupyter notebooks on another server provider (e.g. Kaggle) or personal server, please refer to the Fastai installation guidelines: https://docs.fast.ai/install.html

Note: the code has been developed using Fastai v1.0.58

Other dependencies

Please note that the interpretation analysis in Amazon_v2.ipynb requires the installation of the lime library. To do so, run !pip install lime in the notebook.