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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%.

social-media-forensics

Fosensic Tools for Social Media

social_media_authorship_verification

spring-boot

star-me

Star FOSSASIA Repositories on Github and Support the Community

statistical-machine-learning-project-1

Predicting authors of test tweets from among a very large number of authors found in training tweets. The Project also builds generic skills in problem solving, critical analysis, presentation/communication, and team work – all critical for practical SML.

style-change-detection

style-change-detection-using-bert

Solution with the best accuracy for the Style Change Detection task for the competition PAN @ CLEF 2020

stylext

An authorship attribution project with particular emphasis on Twitter analysis

stylometry

Sample project for using stylometry to deanonymize Twitter account author.

susi.ai

SUSI.AI Web Client https://susi.ai

susi_chromebot

SUSI.AI Chrome Extension

susi_server

SUSI.AI server backend - the Artificial Intelligence server for personal assistants https://api.susi.ai

syntactic-recurrent-neural-network-for-authorship-attribution

text-generation

Using deep learning to generate text in the style of an author

tf-text-classification-demo

a demo that uses an LSTM neural network to predict the author of random selections of text pulled from numerous books in Project Gutenberg

trump-speeches

:page_facing_up: 1mb Archive of Donald Trump Speeches

tweet-attribution

My little NLP short text classification project! Author attribution for tweets using TensorFlow. Architecture based on this paper: http://cs.uh.edu/~prasha/papers/cnn-aa-short.pdf **INCOMPLETE/ WORK IN PROGRESS**

tweet-author-prediction

tweet-authorship-attribution

A simplification of the more general problem of authorship attribution, which automatically identifies the authorship of a document

tweet-authorship-attribution-with-doc2vec

Using doc2vec on authorship attribution task

tweet-authorship-attribution-with-subword-embedding

A tweet authorship attribution using subword embeddings + CNN

tweet-prediction

Predicting the user activity on Twitter using tweets and machine learning.

tweet_authorship

Determining authorship of tweets

tweets-authorship-attribution-inclass-kaggle-competition-

tweets_authorship_attribution

tweetsautorshipattributionmodelsevaluation

In this notebook I work on the question whether the author of a tweet (very short text) can be successfully identified. I try to choose the best classification method its parameters set and features

tweetsclassification

The goal of this work is to build predictive models that can automatically infer people’s needs from user- generated content.

twitoff

A fun web application comparing and predicting tweet authorship.