Classify Twitter users based on different parameters

A twitter user is classified into Anomalous, Non Anomalous and Intermediate using 5 parameters and each of these parameter will be given a rank:

• Time Difference (denoted by a)
• Similarity of Tweets (b)
• URL Ranking (c)
• Malware URL (d)
• Adult Content (e)

each of these parameter will be assigned a value from 1-10 for each user and these parameters have a weight which together will decide whether a user is anomalous or not

Weights of each parameter are :

• Time Difference: 0.15
• Similarity of Tweets: 0.25
• URL Ranking: 0.30
• Malware URL: 0.30
• Adult Content: 1

• This algorithm is applied on a dataset of twitter users from which a dataset of a,b,c,d,e and FAL values are obtained.
• Onto this dataset different classification methods are applied.

• K-nearest neighbors (KNN)
• Support Vector Machine (SVM)
• Naive Bayes classifiers
• Random Forest
• Decision Tree

• Files related to algorithm used is present in Twitter folder
• Main.py is the root file to be run from which other functions are called
• dataset_generator.py generates dummy data of values a,b,c,d,e,FAL,type into dataset_gen.csv
• Classifier.py takes in the data present in the dataset_gen.csv and classify the users based on different Classification Algorithm
• wot.py is used to calculate Web Of Trust Rank
• similarity.py is used to calculate similarity of tweets
• url.py is used to calculate Alexa rank of url's present in the tweets
• checkTime.py is used to calculate time difference of tweets
• checkContent.py is used to check for adult contents in tweets

To check whether a particular user is anomalous :-

• clone this repo
• run the following commands in the terminal from the cloned folder
• virtualenv venv
• source venv/bin/activate
• pip install -r requirements.txt
• python manage.py migrate
• python manage.py runserver
• open localhost:8000/main in your browser

To do the classification follow these steps :-

• open Twitter folder in terminal
• store the dataset of usernames which needs to be classified in dataset_gen.csv
• run python main.py
• output based on 5 classification algorithm will be displayed as the output