Social media repertoires

by Eszter Hargittai on October 29, 2021

I’d like to blog more about my research, but not sure yet how to go about it (e.g., whether to write more about research already completed or about projects currently in the works or both.. feel free to voice your preference). Today, I’m posting a link to a paper that was just published (and is available open access so no paywall to battle): Birds of a Feather Flock Together Online: Digital Inequality in Social Media Repertoires, which I wrote with my friend Ágnes Horvát.

There is some work (not a ton, but a growing literature) on who adopts various social media platforms (e.g., are men vs women more likely to be on Facebook or on Pinterest, are more highly educated people more likely to be on Twitter or on Reddit), but as far as we can tell, no one has looked at the user base of pairs of such services. (I am always very cautious to claim that we are the first to do something as it’s nearly impossible to have a sense of all work out there, but we could not find anything related. Do let me know if we missed something.)

Why should anyone care who adopts a social network site (SNS) and what’s the point of knowing how user bases overlap across such sites? There are several reasons for the former and then by extension, the latter. I started doing such analyses back in the age of MySpace and Facebook finding socioeconomic differences in who adopted which platform even among a group of college students. More recent work (mine and others’) has continued to show differences in SNS adoption by various sociodemographic factors. This matters at the most basic level, because (a) whose voices are heard on these platforms matters to what content millions of people see and share and engage with; and (b) many studies use specific platforms as their sampling frame and so if a specific platform’s users are non-representative of the population (in most cases that is indeed the case) and the research questions pertain to the whole population (or all Internet users at minimum, which is again often the case) then the data will be biased from the get-go.

By knowing which platforms have similar users, when wanting to diversify samples, researchers can focus on including data from SNSs with lower overlaps in their user base without having to sample from too many of them. Also, for campaigns – these could be health-related, political, commercial – that want to reach diverse constituents, it is again helpful to know which sites have similar users versus reach different groups of people. Our paper shows (with graphs that I am hoping are helpful to interpreting the results) how SNS pairs differ by gender, age, education, and Internet skills.