I conducted an analysis of public Facebook pages and groups that support US Presidential Candidates Donald Trump and Joe Biden to determine what pages are be most influential, may be coordinating to amplify content, and what topics they are discussing.

An analysis of junk news articles shared in pro-Trump and pro-Biden Facebook pages and groups

Since October 2018, Twitter has been releasing archived datasets of tweets from state-sponsored information operations (IO) that it has identified on its platform. On 20 September, Twitter announced that the PP allegedly used fake accounts to fraudulently amplify its messages and support levels in the run up to the April 2019 elections. Twitter published a full archive of the Spain IO accounts and activity it identified on its platform. I dove into the Twitter Spain IO data to see what behavioral patterns can be mined to better understand how the campaign operated.

Presentation for the https://mbaldassaro.shinyapps.io/predictionary/ app.

The goal of this report is to explain the basic exploratory analysis performed on the datasets and plans for the production app and algorithm. This document explains only the major features of the data that have been identified and briefly summarizes plans for creating the prediction algorithm and Shiny app.

The data concerning the adverse impact on the health and economic well-being of the US population between 1950 and 2011 illustrates a complex picture. After examining the data, it is clear that, cumulatively, tornadoes have been responsible for more deaths and injuries by a wide margin - more than the next five weather events combined. However, on a per event Heat is responsible for deaths and injuries than any other event. From an economic perspective, cumulatively, flooding has caused higher property damage costs than other weather events by a wide margin. Also, cumulatively, drought has caused higher crop damage costs than other events by a significant margin. On a per event basis, excluding exceptionally rare events (i.e. occurring less than 10 times), the cost per event tells a different story. On a per event basis, typhoons have cost the most in terms of property and crop damage on a per event basis.