The Political Scientist

Sentiment expressed on social media is far from a scientific poll of the overall population's feelings toward President Trump, but it can offer a valuable measure of expressed attitudes within the online public square. Below are two figures that offer telling insights into current discussion of "#POTUS" among Twitter users as of February 25, 2017 (the day of this writing). Using the NRC Emotion Lexicon in R, I analyzed a dataset of 2,896 Tweets tweeted by 2,455 unique Twitter users that I scraped just this afternoon. [ Note : I'm still collecting more Tweets as I write this post, and it'll take some time before I have a sizable dataset.] The positive vs. negative sentiment score counts per Tweet clearly lean positive. Much of this positive sentiment, as the below figure shows, likely results from a substantial amount of trust that appears in several Tweets that contain #POTUS. While fear , anger , sadness , and disgust all make showings, trust , joy ,

My research for my master's thesis centered on the intersection of foreign aid and immigration -- specifically, on the question of whether foreign aid has an impact on international migrant flows. Various political scientists and economists have at times considered this question, but most never have given it much serious consideration. Unlike these scholars, I apparently was curious enough about this topic to dedicate months of research, data collection, and analysis to answering it (either that or I didn't have much of a life...). Findings My analysis ultimately showed that when wealthy countries increase official development aid (ODA) sent to a given developing country in a given year, the result is an increase in immigration from said developing country the following year. This finding held true, even when controlling for several factors that consistently have been shown to have an impact on migrant inflows. [ Note : The analysis included data from 1994 to 2011.] Belo