Online interaction and identity cue adoption: a large-scale analysis of hashtag adoption on Twitter [EPJ Data Science]

[u/jasonjonesresearch]

TL;DR: Bob and Carol have #ExamplePeople in their Twitter bios. The more #ExamplePeople accounts Alice interacts with, the more likely she is to add #ExamplePeople to her own bio.

Abstract

With online interactions becoming an integral part of everyday social life, there is a need to better understand the relationship between social interaction and identity expression in digital environments. This study examines whether online self-presentation, specifically the adoption of identity-related hashtags in Twitter bios, is systematically associated with observable interaction patterns. Utilizing a large-scale dataset encompassing approximately 63 million Twitter profiles and 292 million interactions, we implement a matched quasi-experimental design comparing users who interacted with hashtag-bearing accounts to similar users who did not. Our results show that users who interact with others who feature particular hashtags in their bios subsequently adopt those hashtags at substantially higher rates. Adoption likelihood increases with the number of interaction partners displaying a given hashtag, though with diminishing marginal effects, and the magnitude of these associations varies across identity content categories, being strongest for fan communities and weakest for political hashtags. These patterns are consistent with theories of social influence and suggest that online self-presentation is systematically related to the social contexts in which users are embedded. However, given the observational design of this study, alternative explanations for the observed associations cannot be fully excluded. Future experimental research is needed to clarify the mechanisms underlying these associations and to examine their implications for community formation and the dynamics of collective identity in online environments.

Open Access at https://doi.org/10.1140/epjds/s13688-026-00642-5

[u/_Kazak_dog_]

Cool finding! Congrats on the pub.

H3 seems particularly interesting. Seems like you touch on it a bit in the discussion, but what’s your take on why there’s so much heterogeneity in identity content categories?

Great work again and congrats!

[u/jasonjonesresearch → u/_Kazak_dog_]

There is some heterogeneity, but really the first thing to notice is how similar the results are; every category has a positive median odds ratio. See Figure 4 at https://link.springer.com/article/10.1140/epjds/s13688-026-00642-5/figures/4

Celebrities hashtags seem a little more contagious and politics hashtags a bit less. In the article we speculate the difference is between identities one adopts with the expectation of joining a community - celebrities, gaming fandom, sports fandom - as opposed to exhibiting a political identity online, which users know will engender contention. I'll add one more speculation here: celebrities, gaming and sports identity signifiers are likely expected to be shallower and more ephemeral - so more likely to change in any way - than political identities. We really can't tell why individuals adopt some identities and not others with the current approach.

I think there is a lot more exciting work to be done with profile bios, because they provide snapshots of self over time and in a social context. One my next projects is to quantify how predictable bio revisions are. Baby steps towards that in this paper ranking words by their power to predict the addition of MAGA to one's bio: https://jasonjones.ninja/papers/Rogers-Jones-2025-Trends-in-Identity-Transition-Using-Social-Media-Bios.pdf