*both*.

The story so far: a

*Monkey Cage*post proposed some hypotheses for what characteristics of a post made it more likely to be tweeted than liked.

*Causal Loop*did the analysis (linked at the

*Monkey Cage*) using a composite index. Laudable as the analysis was (and how different Political Science is from the 1990s), I think I can improve upon it.

First, there are 51 (of 860 total) posts with zero likes and zero tweets. This is important information: these are posts that no one thought worthy of social media attention. Unlike

*Causal Loop*, I want to keep these data in my dataset.

Second, instead of a ratio of likes to tweets (or more precisely, an index based on a modified ratio), I'll estimate separate models for likes and tweets, with comparable specifications. To see the problem with ratios consider the following three posts

Post A: 4 tweets, 2 likes

Post B: 8 tweets, 2 likes

Post C: 400 tweets, 200 likes

A ratio metric treats posts A and C as identical, while separating them from post B. But intuitively we expect a post like C, which generates a lot of social media activity in aggregate, to be different from posts A and B, which don't. (This scale insensitivity is a general characteristic of ratio measures.) This is one of the reasons I prefer disaggregate models. Another reason is that adding

To test various hypotheses one can use appropriate tests on the coefficients of the independent variables in the models or simulations to test inferences when the specifications are different (and a Hausman-like test isn't conveniently available). That's what I would do for more serious testing. With identical specifications one can compare the z-values, of course, but that's a little too reductive.

Since the likes and tweets are count variables, all that is necessary is to model the processes generating each as the aggregation of discrete events. For this post I assumed a Poisson process; its limitations are discussed below.

I loaded

*Causal Loop*'s data into Stata (yes, I could have done it in R, but since the data is in Stata format and I still own Stata, I minimized effort) and run a series of nested Poisson models: first with only the basic descriptor variables (length, graphics, video, grade level), then adding the indicator variables for the authors, then adding the indicator variables for the topics. The all-variables-included models results (click for bigger):

A few important observations regarding this choice of models:

**1.**First and foremost, I'm violating the Prime Directive of model-building: I'm unfamiliar with the data. I read the

*Monkey Cage*regularly, so I have an idea of what the posts are, but I didn't explore the data to make sure I understood what each variable meant or what the possible instantiations were. In other words, I acted as a blind data-miner.

**Never do this! Before building models always make sure you understand what the data mean**. My excuse is that I'm not going to take the recommendations seriously and this is a way to pass the morning on Saturday. But even so, if you're one of my students,

**do what I say, not what I just did.**

**2.**The choice of Poisson process as basis for the count model, convenient as it is, is probably wrong. There's almost surely state dependence in liking and tweeting: if a post is tweeted, then a larger audience (

*Monkey Cage*readers) gets exposed to it, increasing the probability of other tweets (and also of likes -- generated from the diffusion on

*Monkey Cage*who then like posts to

**3**. I think including the zeros is very important. But my choice of a non-switching model implies that the differences between zero and other number of likes and tweets is only a difference of

*degree*. It is possible, indeed likely, that they are differences of

*kind*or

*process*. To capture this, I'd have to build a switching model, where the determinants of zero likes or tweets were allowed to be separate from the determinants of the number of tweets and likes conditional on their being nonzero.

With all these provisos, here are some possible tongue-in-cheek conclusions from the above models:

- Joshua Tucker doesn’t influence tweetability, but his authorship decreases likability; ditto for Andrew Gelman and John Sides. Sorry, guys.
- James Fearon writes tweetable but not likable content.
- Potpourri is the least tweetable tag and also not likable; International relations is the most tweetable but not likable; Frivolity, on the other hand is highly likable. That says something about
*Facebook*, no? - Newsletters are tweetable but not likable… again Nerds on
*Tweeter*, Airheads on*Facebook*.

Given my violation of the Prime Directive of model building (make sure you understand the data before you start building models), I wouldn't start docking the -- I'm sure -- lavish pay and benefits afforded by the

*Monkey Cage*to its bloggers based on the numbers above.