Hello! My name is Andrew Hamlet, and I am a MBA student at NYU. I am developing proficiency with Python, for data mining applications. I plan to publish research on this blog. Since my background is in social media and web analytics, that’s where I will start. As is the nature with scientific inquiry, collaboration is welcome! Please comment with suggestions or further lines of inquiry. Now let’s begin. Using Python, I gathered tweets, including the retweet and favorite counts, occurring in June 2015 for the Top Twitter Personalities (no companies), as listed by twittercounter.com. Here’s a table displaying the data, in ascending order by Followers. The Tweets, Retweets, and Favorites columns display totals for each Twitter Profile during June 2015. Since there is variance among total number of Tweets, that is Katy Perry tweeted 35 times in June 2015 while Justin Bieber tweeted 167 times in June 2015, I normalized Retweets and Favorites, dividing them by Tweets to give Monthly Average Retweet per Tweet and Monthly Average Favorite per Tweet. Here’s a table displaying the results. There appears to be a relationship Between Monthly Average Retweet per Tweet and Monthly Average Favorite per Tweet, that is as MARpT increases MAFpT increases. Let’s plot to verify. (For visual display, Justin Timberlake, Britney Spears, Ellen Degeneres, and Justin Bieber are removed from the chart.) Yes! There’s a 0.83 correlation between MARpT and MAFpT, so Favorites increase as Retweets increase. Even more, we see a Favorite bias among this sample, that is the twitter profiles receive more MAFpT than MARpT. However, we see a Retweet Bias with Barack Obama. Perhaps politicians receive more retweets, while entertainers receive more favorites? In the next post, using this sample we will investigate whether there is a relationship between total number of Tweets and MARpT or MAFpT, that is does tweeting more during a month boost the number of interactions per tweet? Check back to find out!