salhasalman

author-classification

Tweet author classification tool

author-identification

author identification using deep learning

Author-Identification-2

Author Identification Project using NLP methods and Machine Learning

Author-Prediction-Using-Deep-Learning-Models

Six different machine learning models to compare performance accuracies of the training, validation and test sets.

AuthorAttributionForYelp

Authors_Dataset_LSTM

Deep Learning Based Classification Engine

authorship

authorship identification, UH course 'Machine Learning'

authorship-attribution-2

Text classification among various authors in the gutenberg dataset

Authorship-Attribution-of-Short-Texts

Authorship Attribution of Tweets using Convolutional Neural Networks Over character n-grams

authorship-identification-2

salhasalman/Author-Identification-2

Author Identification Project using NLP methods and Machine Learning

salhasalman/Author-Prediction-Using-Deep-Learning-Models

Six different machine learning models to compare performance accuracies of the training, validation and test sets.

salhasalman/authorship-identification-2

salhasalman/AuthId-RNN

Code for "Authorship Identification using Recurrent Neural Networks"

salhasalman/Author-Identification-using-Text-Snippets

Author identification given multiple short text snippets via using stylometric and lexicographical features.

salhasalman/author-predictor

Author prediction from a paragraph using Deep Neural Nets

salhasalman/AuthorIdentification

salhasalman/AuthorIdentification-SentimentAnalysis-TopicModeling

Project done for the NLP course at Sorbonne University

salhasalman/authorship-identification-web-fictions

salhasalman/AuthorshipAttribution-1

Models for text classification and authorship attribution based on the Higher Criticism

salhasalman/DatabaseTools

The class MongoCollection allow an easy config of a MongoDB collection by providing an interface which handle authentication, indexes management, data conversion and pretty print of collections. It can work like a Python dict if you give at least one index.

salhasalman/deeplearning_twitterbotornot

Revisiting Twitter Bot or Not with Deep Learning

salhasalman/DeepStyle

DeepStyle provides pretrained models aiming to project text in a stylometric space. The base project consists in a new method of representation learning and a definition of writing style based on distributional properties. This repository contains datasets, pretrained models and other ressources that were used to train and test models.

salhasalman/h2database

H2 is an embeddable RDBMS written in Java.

salhasalman/HatefulUsersTwitter

Code for the paper "Characterizing and Detecting Hateful Users on Twitter"

salhasalman/LeetCodeDaily

salhasalman/NLP-AuthorPrediction-WebApp

Machine Learning application (to predict author of tweets) with its front-end developed using React which interacts with a Flask service as the back-end.

salhasalman/NLP-Authorship-Attribution-with-Markov-Chain

salhasalman/NLP_author_identification

salhasalman/Obfuscation-Detection

Obfuscation detection tool. Given a document, it tells if it has been written by human or altered by an automated authorship obfuscation tool.

salhasalman/pan2020_authorship_verification

salhasalman/QuizApp

salhasalman/RusLit

📚 A small collection of Russian literature 📚

salhasalman/simple-todo

salhasalman/Social-Media-Disinformation-Network-BERT

# Social_Media_Disinformation_Network Twitter is a social networking platform where many political thoughts and views are exchanged between users. Some of the users are, in fact, nation state actors – individuals having close links to the military, intelligence or state control apparatus of their country – who share fake news to engage in espionage, propaganda or disinformation campaigns. Twitter has already identified many of these accounts and banned them from Twitter for violating Twitter policies. Our main goal is to build a classification Natural Language Processing (NLP) model by learning disinformation and fake news patterns from tweets and to classify them either as “Disinformation” or “Others.” This study makes use of state-linked information operations (“IO”) data published by Twitter in June 2020 covering operations attributed to Russia and Turkey. We narrowed our focus to the Turkish and Russian tweets which were involved in a range of manipulative and coordinated activities spreading geopolitical narratives favorable to their respective political parties in Turkey. For our classification model we also incorporated Twitter live stream data from the Twitter archives for the same time period. Using SQL queries, we isolated the 8,392 banned Turkish & Russian accounts from the archived live stream data to create our “Others” category data. Using a Bidirectional Encoder Representation from Transformers (BERT) model, with the “Turkey” & “Russia” information operations and “Others” live stream archive category data for training, we tested this model against archived Twitter tweets for the month time period following the time period of the training data. Our model predicted 43,568 tweets as “Turkey” disinformation out of 411,095 tweets with an accuracy of 89.4%. For the same time period Twitter banned only 26,259 disinformation tweets. Based on our prediction model it appears that Twitter may still be missing 17,309 information operations tweets for that time period, Similarly our model predicted 20,826 tweets as “Russia” disinformation out of 114,416 tweets with an accuracy of 81.79%.

salhasalman/TwitOff

A fun web application comparing and predicting tweet authorship.

salhasalman/twitoff-2

Web application for guessing a Tweet's author

salhasalman/Twitter-Author-Prediction

salhasalman/Twitter-Authority

This is a competition involving the construction of a model through the practical and fundamental machine learning of natural language processing using data obtained from Twitter to detect hoax tweets.

salhasalman/Who-Tweeted-That

COMP90051 Project 1: Authorship Attribution with Limited Text on Twitter