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Spotting Collective Behaviour of Online Frauds in Customer Reviews

This is the code for the paper titled

Spotting Collective Behaviour of Online Frauds in Customer Reviews. Sarthika Dhawan*, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty

accepted at 28th International Joint Conference on Artificial Intelligence.

Quick Start

Requirements

• Python To install the dependencies used in the code, you can use the requirements.txt file as follows -

pip install -r requirements.txt

Running the code

Run the detection.py followed by refine_groups.py as follows -

python detection.py

The agruments it takes are (All are mandatory):

• --metadata: Path to metadata for the particular dataset.
• --rc: Path to review content for the particular dataset.
• --dg: Path to save the groups detected (json format).

python refine_groups.py

The agruments it takes are (All are mandatory):

• --metadata: Path to metadata for the particular dataset.
• --rc: Path to review content for the particular dataset.
• --groups: Path to groups generated by detection.py.
• --outputgroups: Path to save the output groups (json format).

This will generate fraud reviewer groups for the particular dataset.

Run the ranking.py as follows -

python ranking.py

The agruments it takes are (All are mandatory):

• --groups: Path to groups generated by refine_groups.py.
• --ef: Path to reviewer embeddings.
• --rankedgroups: Ranked group IDs (txt format, line separated IDs).

This will rank fraud reviewer groups for the particular dataset.<br> Provide appropriate paths for data files and parameters.

Contact

If you face any problem in running this code, you can contact us at sarthika15170[at]iiitd[dot]ac[dot]in.

License

For copyright (c) Sarthika Dhawan, Siva Charan Reddy Gangireddy, Shiv Kumar, Tanmoy Chakraborty

For license information, see LICENSE or http://mit-license.org