Until Sunday, 11 Dec 2016, i`m taking part in the Debug Politics Hackaton , working on “The Outbrake”, a tool that can identify fake viral news, before they become viral.

The best illustration is in this image made using data collected via “The Outbrake” script.

Think of this like this : Having a viral post is something that does not happen all the time. For every successful viral post out there, 100 posts will fail and get weak results, no more then 50–100 likes.

By identifying just the ones that are getting momentum, we can look at the ones that are likely to make the most damage, because they will be read or seen by a higher percent of the general population.

I need help with :

1. Constructing the front end. There we output the viral stories, after we filter them, so that journalists can focus just on 20–40 articles per day that are getting viral from the Facebook pages that usually post fake or misleading articles, instead of going manually to read the over 10.000 FB posts that this pages post each day. Now, i don`t have a method exposing the PostgreSQL data to the user.

There we output the viral stories, after we filter them, so that journalists can focus just on 20–40 articles per day that are getting viral from the Facebook pages that usually post fake or misleading articles, instead of going manually to read the over 10.000 FB posts that this pages post each day. Now, i don`t have a method exposing the PostgreSQL data to the user. 2. Create the viral identification script that will detect the viral articles.

I now check :

- Shares % difference between this hour and 1 hour ago.

- Shares % difference between this hour and 4 hours.

- Some other verifications.

Exclude :

- Remove posts that have less then 300 shares. If they grow from 50 to 500 from hour to hour, is nice but this is not viral.

- Get the avg of shares per post for that page. This allows us to normalize the data between pages that have 5M Likes and the ones that have 50K Likes.

This kind of works and allows me to get a kind of a good result, but i`m sure that i can do it in a better way.

Would love to speak with somebody that have exp in Virology/Data Science/Model Construction/Matematical Models so that i construct a model that does a good classification of the viral posts.

This is how the data looks for every facebook posts. I have the date when i scraped the post, and the number of likes, comments and shares, emotions.

How to join ? Copied this from the FAQ Debug Politics FB Event

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Frequently Asked Questions

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Q: Can I participate remotely?

A: If you’re not in San Francisco 12/9–12/11, you can participate as a remote team member. Debug Politics is beta testing a system that allows people from all over the world to contribute to ideas presented in SF. Fill out this 30 second form if you’re interested: bitly.com/DebugPoliticsRemote

About Me

Raising money so that i can work full time on this project https://www.youcaring.com/newspapersandjournalists-711969

In the last 3 years i collaborate with the Organised Crime and Corruption Reporting Projects (OCCRP), were i do data analysis and pattern recognition to uncover patterns of corruption in unstructured datasets.

In September 2016 i have moved to San Francisco, to start a new life here.

You can find me online on Medium Florin Badita, AngelList, Twitter , Linkedin, Openstreetmap, Github, Quora, Facebook

Sometimes i write on my blog http://florinbadita.com/