#FluxFlow: A visual tool to analyze “anomalous information” in social media

Thanks to the Social Media Analytics course I’m taking as part of my Masters in Data Science program, I found a very interesting paper about #FluxFlow that I had to summarize and present.

#FluxFlow is an analytics data visualization tool that helps identifying and understanding how ‘anomalous’ information spreads in social media. In the context of social media, “anomalous information” can be in most cases equated to rumors and ‘fake news’. Having a tool like this available to understand how this type of patterns work can help identifying and taking action over potentially harmful consequences.

The original paper (written by Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins) used for this research is available here for you to read plus a very concise and descriptive video here, and also the real #FluxFlow tool is here for you to see and understand. I created a super simple and brief presentation to summarize the tool and its potential applications to other scenarios.

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