There are 7 repositories under disinformation topic.
A tool to quickly identify relevant, publicly-available open source intelligence ("OSINT") tools and resources, saving valuable time during investigations, research, and analysis.
AMITT (Adversarial Misinformation and Influence Tactics and Techniques) framework for describing disinformation incidents. Includes TTPs and countermeasures.
A minimum-dependency ECMAScript client library and CLI tool for Parler – a "free speech" social network that accepts real money to buy "influence" points to boost organic non-advertising content
Collection of scripts for The TWINT project
UNOFFICIAL Python API to interface with Parler.com
Amber Heard Social Network Analysis of Disinformation/Influence Operations, Bots, & Crime Across-Platforms. - Twitter, Reddit, YouTube, Instagram, Change.org, Facebook, Tumblr, TikTok. To create Foundations to Help victims of bots, cyberabuse, domestic abuse, coercive control, crime, & disinformation operations. We want to Save Lives & help partners create systems to help online - including specialized and accurate rescue, quality custom, data analysis, social network analysis, forensics, research, and public safety technologies - with focus on the victim primarily & her environment.
This repository contains list of available fake news datasets for data mining.
🗳️+👀 A platform to protect elections in a disinformation world.
Python implementation of C2PA: Coalition for Content Provenance and Authenticity.
Resources (conference/journal publications, references to dataset) for harmful memes detection.
This windows CLI app lets you collect data from twitter via REST API and convert it into a CSV data set that can be used with Gephi. Other social networks (Reddit, Youtube, WWW) are also supported.
Utilities & scripts to collect and find insight from social network data and users.
A verification “Swiss army knife” helping journalists, fact-checkers, and human rights defenders to save time and be more efficient in their fact-checking and debunking tasks on social networks especially when verifying videos and images
Official implementation of the ACL 2023 paper: "Faking Fake News for Real Fake News Detection: Propaganda-Loaded Training Data Generation"
iVerify Apps: Apps that support the AI-powered iVerify platform to combat misinformation and hate speech
This collaborative resource aims at empowering all actors countering information manipulation to grow and improve.
Digitalsherlocks
COVID-19 Infodemic Twitter dataset
Promise Tracker is a tool designed to help journalists and civil society watchdogs track campaign/promises/pledges by government officials. Accessible at https://promisetracker.dev.codeforafrica.org/
Allow exploration and conservation of political ads archives
A Flask application for analyzing activity on an online discussion forum, using scraping, indexing, analytics, relational graph and NLP.
Dataset: Fighting the COVID-19 Infodemic: Modeling the Perspective of Journalists, Fact-Checkers, Social Media Platforms, Policy Makers, and the Society
Troglodyte Networked Conspiracy Database
Machine learning on a fake news data set
Assess the the legality of political advertisement on digital platforms
Monitoring Italian conversations around vaccines on multiple social media (Twitter, Facebook)
[HACKATHON] Detect "dormant" bots on Twitter
An AI-powered tool to support lateral reading.
Influence Effects & Hollywood Fixers - Society, Organizations, Experts, Witnesses with Open-Source Documents - Amici Organizations Filings, Official Records, Information - Impacts to Legal Cases, Victims/Witnesses - Updated Data of 5 Platforms to Investigations - Methodology of Fixers
Role of Context in Detecting Previously Fact-Checked Claims
Deep Classiflie is a framework for developing ML models that bolster fact-checking efficiency. As a POC, the initial alpha release of Deep Classiflie generates/analyzes a model that continuously classifies a single individual's statements (Donald Trump) using a single ground truth labeling source (The Washington Post). For statements the model deems most likely to be labeled falsehoods, the @DeepClassiflie twitter bot tweets out a statement analysis and model interpretation "report"