Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities (ACM TIST 2020)
This is the code and the dataset for the paper titled
Analyzing and Detecting Collusive Users Involved in Blackmarket Retweeting Activities. Udit Arora*, Hridoy Sankar Dutta*, Brihi Joshi, Aditya Chetan, Tanmoy Chakraborty
published at ACM Transactions on Intelligent Systems and Technology.
- Python 3.6+ To install the dependencies used in the code, you can use the requirements.txt file as follows -
pip install -r requirements.txt
multi.csv contain the 50-dimensional user embeddings that give the best results for binary and multi-class classification of collusive/genuine users. Each line of the files represents a user, where the first 50 values represent the 50-dimension embedding, and the last value is the label.
user_views.tsv.gz contains the user views corresponding to each user. Each row of the file contains tab separated values corresponding to the following for each user -
- [label 1 1 1 1 1 1]: 'label' represents the annotated label for the user, and the six '1's represent the presence of the corresponding view for the user
- [AV1]: Tweet2Vec view
- [AV2]: SCoRe view
- [NV1]: Retweet network view
- [NV2]: Quote network view
- [NV3]: Follower network view
- [NV4]: Followee network view
- label = 0: Genuine User
- label = 1: Normal Customer
- label = 2: Promotional Customer
- label = 3: Bot
Running the code
Use https://github.com/abenton/wgcca with
user_views.tsv as input to generate the multi-view user embeddings using WGCCA.
Pick the type of dataset to be analyzed, and use the
run_classifier method in
classifiers.py to train a classifier and evaluate the performance.
Copyright (c) 2020 Udit Arora, Hridoy Sankar Dutta, Brihi Joshi, Aditya Chetan, Tanmoy Chakraborty