Our platform analyzes Twitter's historical and live data to provide a visually intuitive representation to non-technical professionals.


Our platform will analyze Twitter's historical and live data and design visual representation that shows how specific trends relevant to ethical and societal issues rise and fall. Following a multilayer analysis (e.g.Tweepy, TextBlob, Sklearn within Python Scikit-Learn, and TFIDIF) of the data, we apply visualization techniques, using a combination of open source ( Many Eyes, or Google Data Studio, or Open Refine) tools to make the data understandable and accessible to nontechnical professionals. We will design easy-to-understand visualizations to map the life cycle of hate speech on Twitter, and using a proprietary predictive analysis AI tool to identify potential interventions to stop the flow of virtual vitriol.

Key Benefits

By creating an image that is visually accessible that shows the life cycle of viral tweets, we can identify points of intervention, making social media more 'social' rather than a hatefest.


The virulent nature of hate speech on Twitter will be visualized in a way, which will help non-technical professionals understand its context and the implications of the replies and retweets.
We hope this will provide an opportunity for non-technical professionals to quickly identify the most prominent problems whose mitigation should be prioritized,

We had a more extensive application with more information submitted under the Munich Social Media Viral Twitter challenge, and the deadline was listed at 5pm April 22, 2022, but no time zone was given, so we thought we had until 5pm local time. I tried to submit this more extensive application around noon PST, but received a message that the application was no longer active. If we could have a chance to submit our application in full (which we had prepared) we'd be very grateful to have an opportunity to make a real impact on this social dilemma.

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