Target Audience Analysis (TAA), as described by many field manuals such as FM 33-1-1, is the transitional phase where PSYOP planning moves toward execution, where all communications must be tailored to the local dynamics of the targeted audience in order to be successhfull. In conjuction with modern infromation warfare, which is about corrupting information processing systems and the way they operate, any social media such as Twitter, can be used to distribute and disseminate information in order to manipulate public opinion more efficient, more accurate and in real-time get the feedback and adapt to the conditions. A way to interrupt this proccess is by continuosly monitor these elements (distributors and messages), or at least establish a transparent mechanism to do so.
Reverse Target Audience Analysis, is CVCIO's real-time intelligence dashboard, where we try to reverse engineer this process, by tracking inauthentic account activity and the narratives they are trying to push forward.
RTAA—72 was created during Bellingcat's Hackathon on September 2022.
- Real-time Twitter streaming.
- Network Visualization with sigmajs, and graphology.
- Gephi support via Websockets.
- (Optional) Account Classification via rtaa-classifier.
- (Optional) Tweet Classification via rtaa-classifier.
While this is just a prototype, we plan to support, data indexing with Elasticsearch, Bookmarks, Exports to various formats, more text-classification pipeline, Meme extraction and classification, a collaboration mechanism, and any other suggestion of the community. If you have any, please let us know at info@cvcio.org.
git clone git@github.com:cvcio/rtaa-72.git
cd rtaa-72Before using the services you need to obtain an Api Key / Secret from Twitter in order to connect to the streaming service. Navigate to https://developer.twitter.com/en/portal/dashboard, create a new application, and set the TWITTER_CONSUMER_KEY with the Api Key generated by Twitter and TWITTER_CONSUMER_SECRET with the secret into the environment variables.
To run RTAA—72 it is highly suggested to use docker and docker-compose. Please read the official instrucions on how to install. Afterwards you can edit the environment variables in docker-compose.yaml and start the services. Keep a note that by default it will also start the classification service, which is optional, and depends on multiple models served via Huggingface.
make services-runIf you're new to contributing to Open Source on Github, this guide can help you get started. Please check out the contribution guide for more details on how issues and pull requests work. Before contributing be sure to review the code of conduct.
In general, we are making this software publicly available for broad, noncommercial public use, including academics, journalists, policymakers, researchers and the public in general.
If you use this service, please let us know at info@cvcio.org.
See our LICENSE for the full terms of use for this software.