cibmangotree.org

What It Is

CIB Mango Tree is an open-source project initiated by Civic Tech DC. Its goal is to make it easier for researchers, journalists, technologists, and ordinary citizens to identify coordinated inauthentic behavior (CIB) on social media. It is not a crawler and does not collect platform data itself; instead, it runs detection and analysis on social media activity datasets provided by the user.

Core Capabilities and Technical Form

The tool follows a “low-hanging fruit” approach: it first uses easy-to-run tests to identify more obvious, lower-complexity signs of manipulation, then gradually expands to more advanced methods. The detection methods explicitly listed in the main text include the Copy Pasta Test and Hashtag Test, with more methods still planned. It runs on a local computer through an interactive command-line interface and requires Python 3.12 or later. In terms of supported languages/frameworks, publicly available information is mainly limited to Python; it does not specify which social platform data formats, NLP frameworks, or network analysis libraries are supported.

Open Source, Deployment, and Ecosystem

The project is clearly positioned as an open-source tool and provides instructions for cloning the GitHub repository, setting up a virtual environment, running a bootstrap script, and starting it with python -m cibmangotree. Its local-first design is helpful for analyzing sensitive data and reproducing methods. In terms of ecosystem, it belongs to Civic Tech DC and is driven by a volunteer community, with Engagement Scrum, Product Scrum, and project nights, making it suitable for participatory development and public-interest research. However, the main text does not disclose a license, API/SDK, formal plugin mechanism, or third-party platform integrations.

Pricing and Documentation

The main text does not mention commercial pricing or a paid version. Given its open-source nature and nonprofit community background, it currently appears to be more of a free public-interest tool. As for documentation quality, the Quick Start covers installation and launch, which is enough for developers to run an initial test. However, the scraped text provides insufficient detail on input data structure, output interpretation, detection metrics, false-positive control, and advanced usage.

Pros, Cons, and Who It’s For

Its strengths are open-source transparency, local execution, clear onboarding steps, and a focus on analyzing misinformation, bots, and manipulation around public issues. Its drawbacks are that the functionality is still early-stage and the publicly disclosed methods are limited; because it does not collect data, users need to obtain and clean datasets themselves; the CLI still poses a barrier for non-technical users, and support mainly depends on the community.

Access from China

The main text does not state whether the website or GitHub can be accessed from mainland China, so this remains unknown. If network access or GitHub availability is unstable, alternatives or complementary tools such as Botometer, Hoaxy, the OSoMe toolkit, Gephi, and NetworkX may be worth considering.