Data-Driven Misinformation Detection

Create tools, algorithms, or platforms that leverage open government data to identify and flag potential instances of misinformation and disinformation across various online sources. Participants should explore data-driven techniques to analyze patterns, anomalies, and narratives that might indicate the spread of false information.

In today's digital age, misinformation and disinformation can spread rapidly, impacting public perception and decision-making. This challenge focuses on harnessing the power of open government data and the DISARM framework to develop innovative solutions for detecting and countering misinformation in real-time.

Participants in this challenge will leverage open government data to train machine learning models and algorithms designed to detect misleading content. They will delve into techniques like text analysis, sentiment analysis, and network analysis to uncover potential sources of misinformation. Collaborating with domain experts, participants will work on developing criteria and indicators to differentiate between factual information and false narratives. Additionally, they will explore innovative methods to visualize the propagation of misinformation, aiding users in comprehending its dissemination.

The anticipated outcomes of this challenge encompass the creation of a solution capable of scrutinizing text, images, or multimedia content for indicators of misinformation and
isinformation. Moreover, participants will generate interactive visualizations that illuminate how misinformation spreads across online platforms. To enhance the effectiveness of misinformation detection and response, they will demonstrate the integration of their solutions with the DISARM framework's responder TTPs.

https://disarmframework.herokuapp.com/