estimating the influence of a tweet–now with 33% more causal inference!

Twitter is kind of a big deal. Not just out there in the world at large, but also in the research community, which loves the kind of structured metadata you can retrieve for every tweet. A lot of researchers rely heavily on twitter to model social networks, information propagation, persuasion, and all kinds of interesting things. For example, here’s the abstract of a nice recent paper on arXiv that aims to  predict successful memes using network and community structure:

We investigate the predictability of successful memes using their early spreading patterns in the underlying social networks. We propose and analyze a comprehensive set of features and develop an accurate model to predict future popularity of a meme given its early spreading patterns. Our paper provides the first comprehensive comparison of existing predictive frameworks. We categorize our features into three groups: influence of early adopters, community concentration, and characteristics of adoption time series. We find that features based on community structure are the most powerful predictors of future success. We also find that early popularity of a meme is not a good predictor of its future popularity, contrary to common belief. Our methods outperform other approaches, particularly in the task of detecting very popular or unpopular memes.

One limitation of much of this body of research is that the data are almost invariably observational. We can build sophisticated models that do a good job predicting some future outcome (like meme success), but we don’t necessarily know that the “important” features we identify carry any causal influence. In principle, they could be completely epiphenomenal–for example, in the study I linked to, maybe the community structure features are just a proxy for some other, causally important, factor (e.g., whether the content of a meme has sufficiently broad appeal to attract attention from many different kinds of people). From a predictive standpoint, this may not matter much; if your goal is just to passively predict whether a meme is going to be successful or not, it’s irrelevant whether or not the features you’re using are doing causal work. On the other hand, if you want to actively design memes in such a way as to maximize their spread, the ability to get a handle on causation starts to look pretty important.

How can we estimate the direct causal influence of a tweet on the downstream popularity of a meme? Here’s a simple and (I suspect) very feasible idea in two steps:

  1. Create a small web app that allows any existing Twitter user to register via Twitter authentication. On signing up, a user has to specify just one (optional) setting: the proportion of their intended retweets they’re willing to withhold. Let’s this the Withholding Fraction (WF).
  2. Every time (or at least some of the time) a registered user wants to retweet a particular tweet*, they do so via the new web app’s interface (which has permission to post to the user’s Twitter account) instead of whatever interface they’re currently using. The key is that the retweet isn’t just obediently passed along; instead, the target tweet is retweeted successfully with probability (1 – WF), and randomly suppressed from the user’s stream with probability (WF).

Doing this  would allow the community to very quickly (assuming rapid adoption, which seems reasonably likely) build up an enormous database of tweets that were targeted for retweeting by an active user, but randomly assigned to fail with some known probability. Researchers would then be able to directly quantify the causal impact of individual retweets on downstream popularity–and to estimate that influence conditional on all of the other standard variables, like the retweeter’s number of followers, the content of the tweet, etc. Of course, this still wouldn’t get us to true experimental manipulation of such features (i.e., we wouldn’t be manipulating users’ follower networks, just randomly omitting tweets from users with different followers), but it seems like a step in the right direction**.

I figure building a barebones app like this would take an experienced developer familiar with the Twitter OAuth API just a day or two. And I suspect many people (myself included!) would be happy to contribute to this kind of experiment, provided that all of the resulting data were made public. (I’m aware that there are all kinds of restrictions on sharing assembled Twitter datasets, but we’re not talking about sharing firehose dumps here, just a restricted set of retweets from users who’ve explicitly given their consent to have the data used in this way.)

Has this kind of thing already been done? If not, does anyone want to build it?


* It doesn’t just have to be retweets, of course; the same principle would work just as well for withholding a random fraction of original tweets. But I suspect not many users would be willing to randomly eliminate a proportion of their original content from the firehose.

** If we really wanted to get close to true random assignment, we could potentially inject selected tweets into random users streams based on selected criteria. But I’m not sure how many tweeps would consent to have entirely random retweets published in their name (I probably wouldn’t), so this probably isn’t viable.

2 thoughts on “estimating the influence of a tweet–now with 33% more causal inference!”

  1. Who would want to open up an entirely indepdent application just to retweet something? I sure wouldn’t, no matter how much Science might benefit. I think to make this work, you’d have to get Twitter to agree to publish a modified version of their app on both platforms (i.e., iOS and Android), which they might not want to do for various business reasons. And I think even then there would be few users willing to suppress even a small fraction of their RTs.

    Would it work if you made it go the other way, and suppressed a fraction of incoming RTs from clients’ timelines? I think more people would be willing put up with that. Of course, I regularly see the same tweet RT’ed by several of the people I follow in succession, although I suspect the Official Implementation may suppress duplicate RTs in some manner as I don’t recall seeing that on the mobile app.

    1. I agree it would be annoying to do on a regular basis, but I wasn’t really thinking of this as a permanent thing, more just something to do once in a while. Actually, it could even work on a one-off basis. Here’s a very minimalistic/low-effort version: as soon as the user agrees to participate (and authorizes access to Twitter), he/she is shown the current stream, and is asked to retweet a few tweets. Some proportion of those tweets are withheld; the rest are retweeted randomly over the next few hours. The user can log in again at any time to repeat the process, but even if they never do, one could still probably build up a pretty nice dataset given sufficient uptake. Does this seem more realistic?

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