Awesome

Spammers on social network detection

In this project we focus on detecting spammers in a social network. The ultimate purpose of this project to identify (i.e., classify) the spammer users based on their relational and non-relational features.</p> See the report for more details. </p>

Running the code

The dataset represent users from tagged.com social network. The full dataset is provided in : https://linqs-data.soe.ucsc.edu/public/social_spammer/. </p>

Please think to change the name of the csv files to be able to run the code. These are the name of the different datasets we worked on :

• usersdata.csv : this file contain the different users and they content features.
• filtered_relations_1.csv : This file contains the edges of the relation 1 between the nodes.
• filtered_relations_2.csv : This file contains the edges of the relation 2 between the nodes.
• filtered_relations_4.csv : This file contains the edges of the relation 4 between the nodes.
• filtered_relations.csv : This file contains the edges of the relation 5 between the nodes.
• filtered_relations_7.csv : This file contains the edges of the relation 7 between the nodes.

These filtered relations files should be generated using the spammer_subnetwork.ipynb after downloading the full dataset from the link above. </p>

The repository contains four jupyter notebooks :

• spammer_subnetwork.ipynb : (©) Eda Bayram : Please filter the original huge file based on the relation. Create relation 1 2 4 5 and 7: these are the relations we worked on for this project.
• feature_extraction.ipynb : This Notebook allows to compute the graph based features and create a final dataset containing the nodes of the network and the calculated features (content based and graph based).
• feature_exploration.upynb : This notebook try to better understand the computed features in details.
• Classification.ipynb : This notebook allows to tune hyperparameters for different supervised learning algorithm on the dataset provided by the first notebook in order to classify the nodes beetween spammers and non spammers.</p>

More details

See the project report provided also in the repository. The slides presented is also provided.

Authors