A deep learning approach to Tweet authorship verification, based on siamese networks. Project currently under development for a deep learning MSc class @ CIn-UFPE.
The Tweet dataset used in this project is openly provided by the University of Victoria, and you may read more about it in the following reference.
Marcelo Luiz Brocardo, Issa Traore. “Continuous Authentication using Micro-Messages”, Twelfth Annual International Conference on Privacy, Security and Trust (PST 2014), Toronto, Canada, July 23-24, 2014.
First, you should have a working Tweet Dev account. Then, substitute the placeholders for your credentials in the tweet_fetcher.py file and execute it.
This file will fetch tweets from the aforementioned dataset and store them in an appropriate directory.
You must also download the GloVe Twitter 27B 50d embeddings:
$ wget http://nlp.stanford.edu/data/glove.twitter.27B.zip && unzip glove.twitter.27B.zip glove.twitter.27B.50d.txt -d embeddings/
Next, execute the file save_tweets_to_csv.py, which will unify the downloaded tweets into a single csv file.
Afterwards, execute the dataset_creator.py file. You may modify its parameters, such as the number of authors and tweets per author, in the main function.
Finally, you may execute the main.py file from the root of this repository, which will load the data, perform every pre-processing step and then train our model together with a comparable one. e.g.
$ python src/main.py tweet_data/ embeddings/glove.twitter.27B.50d.txt data/datasets/same_authors_train.csv data/datasets/same_authors_val.csv data/datasets/same_authors_test.csv --variation=none
For more information on the accepted parameters and possible variations, execute it with the -h parameter.
Please note that while we offer support for our baseline models through the svm.py file, we do not train them in the main pipeline.
All experiments for these models have been conducted using Google Collab.