Moving forward: RQ2. How do we know what Facebook affects?


“Trump didn’t win because of X” has become a popular genre of punditry in the last two and a half weeks, along with any number of declarations backed up by little or no specific data.[1] In the wake of concern about “fake news” and partisan echo chambers online, fed by both Russian intelligence and American hoaxsters, Facebook (and social media more broadly) has become the focal point. Keith Hampton and Eszter Hargittai make this point, but like most such analyses, don’t have data specific to actual voters; instead, they note that the demographics of Trump support are negatively correlated with social media use, and that most people don’t click through from headlines in their Facebook feeds.

But this sort of supposition ignores a range of ways that we know information filters through even pre-Internet social networks, let alone the supercharged networking that is the core function of Facebook. The point here is not to say that Facebook did or didn’t do anything, but that stitching together population-level generalities is not going to provide anything like compelling evidence.

So how do we figure out what Facebook affected, if anything, and how it did it? It’s important to have some handle on what we mean here, because no what matter we do there are going to be lots of variables tangled up in a mess of colinearity. We also need to note that getting a look at actual Facebook content is difficult to impossible, but the online environment presents a lot of problems along these lines. Survey respondents might be able to recall how frequently they visited a major source; can they recall whether or not they ever read something from one of the minor partisan sources that use Facebook as their primary distribution platform?

If actual content is out, we’re going to need to contextualize Facebook use. One way to do this is at the model level, putting Facebook use for news into an mediation model with other media use, and online and offline political discussion. Some co-authors and I have a paper in development that takes one approach to this, essentially wrapping an online version of the communication mediation model in a Facebook-based container. We find no direct effects of Facebook news use on any outcomes outside of Facebook, but significant indirect effects running through links to other media and discussion behaviors. This sort of thinking also suggests examining the relationship of Facebook shares to prominence in other media, and especially major partisan media. Facebook may act as a conduit for stories that bubble up from 4chan, Reddit, or Twitter to make their way to Fox News and conservative talk radio, for example.

Understanding potential Facebook effects at the individual level requires understanding individuals within their network contexts, as both senders and receivers of information. This helps us get at the central complicating factor of measuring Facebook’s effects, which is that everyone’s Facebook experience is different. Unlike a measurement of how often one watches network news broadcasts, for example, just asking for Facebook use frequency tells us basically nothing. However, what if we also knew something about people’s networks? In a survey this would be imperfect self-reported data, but we could ask questions about political homogeneity of one’s network, along with things like tendency to engage with agreeing or disagreeing others. An interaction term between frequency of Facebook use for news and network homogeneity would give us a measure of Facebook as a filter bubble or echo chamber; putting that in a model with reflection, elaboration, and talk would start us toward a model of how a variety of influences affect individuals’ attitudes. I have another paper in progress that utilizes an interaction term like this, and one problem with it is that it’s basically an impossible measure to validate. But that’s a problem for another day!


[1] This is especially weird given the ultimate closeness of the election. Anything that could have cost Clinton 100,000 total votes across Pennsylvania, Michigan, and Wisconsin could be said to be the reason Trump won. The existence of multiple “but for” causes doesn’t make any single one invalid.

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