Blogging, Two Years On

Tuesday marked the second anniversary of YSPR. I wrote the first post while at a political science conference, so it seems fitting that I spent the last couple of weeks travelling to ISA and MPSA. From those two conferences it is clear that blogging and social media are playing an increasingly prominent role in the field. At ISA there was a strong turnout for the blogging reception. While in Chicago for MPSA I had the pleasure of joining a dinner for conflict scholars hosted by Will Moore and Christian Davenport. One notable aspect of that dinner was that in the invitation email everyone had a personal website or blog.

I sincerely appreciate everyone who has visited this blog over the past two years and expressed their support either online or in person. For others who may be starting a blog or thinking about doing so, here are a few lessons I have learned over the past year:

1. Schedule your writing. Making time to write is an important habit to get into. Whether it’s daily or weekly, set aside some time that you can avoid distractions and just write. I usually like first thing in the morning, but you may prefer late evening or another time of day.

2. Schedule your posts. I used to hit “publish” as soon as I wrote something, but that changed this year. Instead, I like to line up about a week’s worth of posts at a time. This allows me to arrange some continuity between posts. It also gives time for my ideas to gel and to ruminate over new post ideas without feeling rushed (and sometimes catch typos).

3. Get involved in a community of writers. Blogging can feel like a solitary task, but it doesn’t have to be. A year ago I got in touch with Duke professor Marc Bellemare since I enjoy his blog. We now get lunch or coffee occasionally and chat about all manner of interesting topics. There are also a few scholar-bloggers I know primarily through blogs and Twitter (Jay Ulfelder, Trey Causey).  Creating friendships with people who will respond to your writing and offer critique when you need it is invaluable.

Thanks for being part of the conversation!

Blogging, One Year On

It has been exactly one year since the initial post on YSPR. In that year, the two biggest changes for the blog have probably been that the main author started graduate school and the move to a new domain name. The first change has meant that my own writing has made up a smaller proportion of the posts, relying  more heavily on readings or links. The second change has meant that I’ve tried to put up content at least three times a week.

Combined, these two changes have meant more content but less of my own voice. They have also meant a substantial growth in readership since the beginning of 2012, when I started keeping a more regular schedule (generally Monday, Wednesday, Friday). If you are new, what attracted you to the blog? If you have been reading for a while, which changes have you liked and which have you disliked?

I thoroughly enjoy this process of putting ideas out there, even if it is only a memorandum to my future self to remember something (as with many of the news reports of DTO leaders being captured or killed). The biggest lessons I have learned from a year of blogging are:

  1. Create content often. A blogger can probably get away without having a regular schedule, but it definitely helps me. If I let the blog slip for a while, by the time I come back to it I have forgotten the ideas I had while I was away. Frequent blogging helps me to come up with more ideas than I otherwise would have had.
  2. Be yourself. There is no point blogging if you have to pretend. Unoriginality will get you nowhere; even if your “take” on things is represented only in the uniqueness of the way that you combine ideas, that can still be a valuable contribution. Write about what interests you.
  3. Respect your readers. I really appreciate getting comments from readers, whether they are personal friends or individuals I have never met before. I make an effort to respond to as many comments as I can, whether through a comment of my own, a response post, or an email.
  4. Include others. This may seem to contradict #2, but really it is a combination of the first three lessons. Jim’s guest post last year is an example of content I never would have been able to share if not for the blog. My writing absolutely benefits from the comments of others, whether on the blog or when the post is still in draft form.
  5. Respect other authors. This is a fairly recent lesson that I learned when I saw some content I had created shown on another blog. I did not mind that the other blogger had reposted it, but there were a number of constructive criticisms of my work on that blog that I had missed out on for months because I was unaware of the cross-posting.

For more reflections on blogging and some links to other good advice, I recommend Marc Bellemare’s post here and the Tyler Cowen video below. Tomorrow I’ll post my top ten favorite posts from the past year.

Meta-Blogging Pt. 3: Social Media and Page Views

In this post I continue the series begun here by asking, How do comments/tweets/likes correlate with page views? To answer this question I continue to use Anton Strezhnev’s scrape of The Monkey Cage (TMC), which was described in the introductory post. I also use the Google Analytics (GA) data for TMC’s top 2000 posts, which John Sides was kind enough to send me. (I’ll discuss the data I collected soon.)

To combine the information from the two files, I wrote this script in Python that puts the dates and titles in the GA file into the form used in Strezhnev’s data. Note that the titles in the GA file are truncated. I then matched the posts based on date and title in R using the substr() command to match on the first 10 characters. It is possible that this step introduced measurement error, so I am cautious about the results. In all, 234 of the 860 posts in Anton’s data appeared in the top 2000 TMC posts, for an overall probability of .27. Here is how the number of page views, tweets, likes, and comments, are distributed over those 234 posts:

Reader Activity in 234 Monkey Cage Posts

Note that these data do NOT constitute a random sample–they are the 234 posts since May 2011 (the farthest that Anton went back) that are also in the top 2000 most popular Monkey Cage posts. Also, recall that TMC switched over to WordPress hosting in early May, 2011, which complicated the title processing somewhat. The R code for the plot above is:


fancy_density = function(Variable, Name, Color) {
  mu_var = round(mean(Variable, na.rm=T), digits=1)
  sig_var = round(sd(Variable, na.rm=T), digits=1)
  plot(density(Variable, na.rm=T),
    xlab=bquote(paste(N==234,' ', mu==.(mu_var),' ', sigma==.(sig_var))))
  polygon(density(Variable, na.rm=T), col=Color)

fancy_density(monkey1$Visits, "Page Views", 'grey')
fancy_density(top2ktweets, "Tweets", 'blue')
fancy_density(top2klikes, "Likes", 'red')
fancy_density(top2kcomments, "Comments", 'green')

OK, enough with the setup–now that we know the distribution of the data, we can can begin modeling. If we wanted to model the relationship between the count of page views and the other three variables, we would use a negative binomial model. Let’s make our task a bit simpler by just looking at the probability that the post is in the top 2000 (in other words, that it had over 20 direct page views, as distinct from visitors to the TMC’s homepage).

[Skip this paragraph if not familiar with statistical analysis.] To do this, I ran a logistic regression of page views on tweets, likes, and comments. I then created 3 scenarios in which one of the variables is at its mean (rounded to the nearest integer) and the other two are at zero. For example, the tweets scenario (blue in the plot below) estimates the probability that a post is in the top 2000 given that it was tweeted 8 times, liked 0 times, and commented on 0 times. These scenarios account for uncertainty in the coefficient of interest using 1000 simulations. Note that the means used differ from those displayed with the density plots above since I used the mean for the whole sample, not just the most popular posts.

So how are tweets/likes/comments associated with the popularity of a post? As the plot below shows, an increased level of one form of reader activity predicts that a post is twice as likely as average to be ranked among the most popular–even while holding the other two levels of reader activity at zero.

Predicted probability that a TMC post is among the most popular, based on comments (green), tweets (blue), and likes (red).

As the plot shows, likes have the closest correlation with the popularity of posts, followed closely by tweets (indicated by the overlap of the probability densities). Comments have a similar but slightly lower association, but this could be due to the fact that comments fall within a much narrower range than tweets and likes (see above). This association is fortunate because it indicates that comments can serve as a suitable proxy for the popularity of posts when we consider data from the other websites, for which page view and tweet/like data have not been collected. Next time, we will consider how the individual attributes of a post (length, images, etc.) are associated with its popularity.