## Triangles, Networks, and New Connections: Donald Trump and Bill O’reilly

Have you ever wanted to send a message to someone with whom you do not have direct contact? What if you knew a friend who had direct contact with this someone? You could send the message through your friend.

This principle can be modeled geometrically. Let’s say you are A and the person with whom you want to speak is C. There is no direct link between A and C.
However, your friend, B, knows this someone, C. So there are links between you, A, and your friend, B, as well as your friend, B, and this someone, C.

Thus, your friend, B, can serve as a bridge, linking you and this someone, and a triangle is formed.

Although when presented geometrically this process may appear abstract, we are familiar with the practice in everyday settings. For instance, we link two contacts via an introduction email, or we introduce two friends at a cocktail party. Triangles are a divine geometry, as they serve to create new connections.

With the US Political season on the horizon, let’s use Twitter and apply this geometry to Republican Presidential Candidate Donald Trump, @realdonaldtrump. Trump follows 44 twitter profiles, one of which is that of Bill O’reilly, @oreillyfactor. O’reilly follows 37 twitter profiles, one of which is that of Donald Trump. So there is a two way connection between Donald Trump and Bill O’reilly.

Well Let’s image this two way connection did not exist and that Donald Trump wanted to connect with Bill O’reilly. How might the two connect? Yes, we must find the common links between Trump and O’reilly, that is the twitter profiles that they both follow. When conducting the analysis, via Python, we find that there are 4 twitter profiles that both Trump and O’reilly follow. The four profiles are @foxandfriends, @BretBaier, @greta, and @ericbollinger . Again, here’s the visualization from above.

To connect with O’reilly, Trump could form a triangle from any of the four. The more links, the greater the likelihood a connection will be made. Triangles create opportunities for our message to reach peripheral networks, without us having to directly transmit the information to the end recipient.

Therein lies the true power of social networks.

## Targeting an Audience, Mapping a Tour: Luther Dickinson

In this post, we will map Luther Dickinson’s US twitter followers, by count and influence, and examine how these distributions match his band’s upcoming tour routing, with the intent to demonstrate the value of twitter data for targeting audiences and planning performances.

Luther Dickinson is the lead guitarist and vocalist for the North Mississippi Allstars. As of August 11, 2015, Luther has 1010 twitter followers. Of these 1010 followers, 483 identify their location as based in the US (not all followers identify location). The map below shows the concentrations of US followers, with the greatest numbers in darkest blue.

Followers

Top 10 states by follower count (darkest blue):

 State Followers Tennessee 78 Mississippi 60 California 45 New York 35 Pennsylvania 25 Georgia 22 Colorado 20 Louisiana 18 Washington 17 Illinois 15

Now we will map Luther’s followers by influence, i.e. the followers of Luther’s followers. In other words, if each of Luther’s followers retweeted, how many individuals would see the retweet?

Influence

Top 10 states by influence (darkest blue):

 State Influence California 1137586 Tennessee 479783 New York 70690 Georgia 64776 Louisiana 63685 Mississippi 59011 Illinois 35373 Colorado 29206 Texas 26612 Rhode Island 23377

We see differences between followers and influence, with Mississippi, Pennsylvania, and Washington hosting greater concentrations of followers, who have less influence. Conversely, we see Rhode Island and Texas hosting lower concentrations of followers, who have more influence. California and Tennessee are strong points for both followers and influence.

Let’s see if this aligns with Luther’s plans for Fall 2015.

According to www.nmallstars.com, the band will tour the following cities in October 2015:

 10.1 – San Francisco, CA 10.2 – San Francisco, CA 10.3 – Los Angeles, CA 10.4 – Anaheim, CA 10.5 – Solana Beach, CA 10.6 – Las Vegas, NV 10.9 – Boulder, CO 10.10 – Denver, CO 10.12 – Chicago, IL 10.13 – Pittsburgh, PA 10.14 – Washington D.C. 10.15. – Glenside, PA 10.16 – New York, NY 10.17 – Boston, MA 10.24 – Placerville, CA 10.25 – Placerville, CA

While we do not see a Tennessee performance during the stretch, all dates besides for two, in Las Vegas and Boston, match the list for top 10 states by followers. Furthermore, we see almost half of the performances, 44%, in California, a strong point for both followers and influence. We view this as strong support for the value of twitter data in targeting audiences and planning performances.

Luther’s map serves as a guide for up and coming artists within the genre. Using the raw data, one could target influencers within each state who would welcome the genre.

To this point, I envision developing a platform that leverages twitter data to help artists better identify audiences and geographic strong points within the genre. If you are an artist, manager, data scientist, or entrepreneur, and are interested in this work, contact me at andrewshamlet@gmail.com

## Gauging US Politics with Reddit

Reddit is an entertainment, social networking, and news site where registered users can vote submissions up or down in a bulletin board-like fashion . Content entries are organized by areas of interest called “subreddits.” This post uses subreddits /r/Republican and /r/Democrats to analyze US Politics as of July 22, 2015.

Thanks to Dr. Randal Olson and his reddit-analysis script, we crawled /r/Republican and /r/Democrats. Making word clouds, we visualize word frequency, largest to smallest by count.

/r/Republican

/r/Democrats

The word clouds provide a high level view of the subreddits. Now let’s dive in to gain insight!

/r/Republican has 16,942 readers, and /r/Democrats has 15,152.

During the timespan 6/22/15 – 7/22/15, 86,609 words appeared in /r/Republican and 73,156 words appeared in /r/Democrats. We will compare word frequency as % of total. In the event of significant difference, the greater of the two will be bolded.

 /r/Republican % of total /r/Democrats % of total “Good” 0.11 0.20 “Bad” 0.06 0.10
 /r/Republican % of total /r/Democrats % of total “Love” 0.05 0.05 “Hate” 0.05 0.05
 /r/Republican % of total /r/Democrats % of total “GOP” 0.07 0.27 “Fox” 0.02 0.08
 /r/Republican % of total /r/Democrats % of total “Trump” 0.30 0.15 “Hillary” 0.07 0.28
 /r/Republican % of total /r/Democrats % of total “Obama” 0.12 0.18 “Bush” 0.07 0.13
 /r/Republican % of total /r/Democrats % of total “Country” 0.11 0.14 “States” 0.14 0.07
 /r/Republican % of total /r/Democrats % of total “Students” 0.04 0.00 “School” 0.04 0.02
 /r/Republican % of total /r/Democrats % of total “Gay” 0.06 0.09 “Marriage” 0.10 0.13
 /r/Republican % of total /r/Democrats % of total “Inequality” 0.01 0.02 “Equality” 0.00 0.03
 /r/Republican % of total /r/Democrats % of total “White” 0.06 0.10 “Black” 0.04 0.04
 /r/Republican % of total /r/Democrats % of total “Health” 0.02 0.10 “Insurance” 0.03 0.05
 /r/Republican % of total /r/Democrats % of total “Workers” 0.02 0.04 “Unions” 0.05 0.01
 /r/Republican % of total /r/Democrats % of total “Gun” 0.05 0.04 “Control” 0.03 0.10
 /r/Republican % of total /r/Democrats % of total “Minimum” 0.02 0.06 “Wage” 0.02 0.08
 /r/Republican % of total /r/Democrats % of total “Church” 0.06 0.01 “Religion” 0.01 0.01

While we’ll let you come to your own conclusions, here are the insights we found surprising:

• Greater Frequency of “GOP” and “Fox” in /r/Democrats
• Greater Frequency of “Students” in /r/Republicans
• Greater Frequency of “White” in /r/Democrats
• Greater Frequency of “Union” in /r/Republicans

That’s it for now. Please comment with additional insights or reach out directly at:

andrewshamlet@gmail.com