Content Strategy: The Cats Meeeow

It has been written 15% of all Internet traffic is cat-related. Whether you believe this statistic, there is no doubt cats inhabit the digital space. To cite more popular examples, we have encountered LOL Cats, Grumpy Cat, and Lil’ Bub…what cuties! To date, there are 72 million media tagged as “cat” on instagram.

With so many kitties purring around the interwebz, how might a content creator know where to start? Well I have created a visualization showing the most popular cat breeds by hashtag on instagram. Enjoy, and MEEEEEEEOW!!!
catbreedsvisualization_1

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

oreillyfactorrealdonaldtrumpnetwork

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.

oreillyfactorrealdonaldtrumpnetwork

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.

Screen Shot 2015-08-11 at 10.37.43 PM

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

lutherdickinson follower map

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

luterhdickinson_follower_influenceTop 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

redditrepublicanwordcloud

/r/Democrats

redditdemocratswordcloudThe 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

Part 3: Most common words used in tweets by Taylor Swift, Katy Perry, and Britney Spears

Hi there! For part 3, we will use visualization to analyze the most common words used in tweets among Taylor Swift, Katy Perry, and Britney Spears. In Part 1, we noted a favorite bias among entertainers. That is, entertainers receive more favorites than retweets. A clue to the favorite bias may lie in word choice. Let’s see.

Taylor Swift:

taylorswift_copyright

Katy Perry:

katyperrycopyright

Britney Spears:

britneyspearscopyright

Each visualization displays the most common words, by usage, for the respective twitter profiles during June 2015. The more often a word is used, the larger the word is displayed.

“Love” and “Day”/”Today” appear in large typeface on all three. Furthermore, the sense of temporality is evident among all three. We see words such as new, now, hour, tonight, and night.

Based on our findings, followers of Taylor Swift, Katy Perry, and Britney Spears receive strong messages of Love Now/Today/Tonight. There’s positive sentiment in these message, followed by an implicit call to action.

Once again, the charts from part 1.

Twitter10TenJune2015RetweetFavorite_v4

monthlyaverageretweettable_V2

Part 2: Does Tweeting More Often Increase Favorites per Tweet and Retweets per Tweet?

In the last post we examined the relationship between MARpT and MAFpT, and found a .83 correlation. That is, as Retweets increase, Favorites increase. We also found a Favorite bias among entertainers and a Retweet bias with Barack Obama. In this post, we will examine whether tweeting more often increases favorites per tweet and/or retweets per tweet. Using the sample of Top Twitter Profiles, as listed by twittercounter.com, we will plot number of tweets against MAFpT and MARpT. Remember MAFpT and MARpT gives us the monthly average number of Favorites and Retweets per tweet. By this logic, if tweeting more often increases retweets or favorites per tweet, we should see higher MARpT and MAFpT with higher Monthly Tweets. Let’s look and see!

tweetstomafpt_v1

This plot displays MAFpT as a function of Monthly Tweets. A previous plot included Justin Bieber, who single handedly increased the correlation 0.4. Considering Justin Bieber an outlier and removing him from the sample, we find a weak correlation, r = 0.25. That is, there’s no strong link between monthly tweets and MAFpT. As for Justin Bieber, as his monthly tweets increase, his favorites per tweet increase–keep on tweeting, Biebs!

retweetsmonthlytweets_v1

This plot displays MARpT as a function of Monthly Tweets. We find an even weaker correlation here, r = 0.16. That is there’s no strong link between monthly tweets and MARpT. The weaker correlation supports the favorite bias we found among entertainers in part 1. That is, entertainers receive more favorites than retweets.

In conclusion, for this sample, we find little evidence that tweeting more often increases either favorites per tweet or retweets per tweet. While this may, or may not, be transferrable to your own twitter account, the findings lead us to ask what factors increase engagement with a tweet, or a message in general?

Until next time!

Part 1: Top Twitter Profiles, June 2015

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. celebrityanalysistable_v2 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. monthlyaverageretweettable_V2 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.) Twitter10TenJune2015RetweetFavorite_v5 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!