Blue checkmarks are Twitter’s way of indicating verified user accounts. Other social media platforms like Instagram have similar features. I have absolutely zero inside knowledge about how the blue checkmark program works behind the scenes, but I’ve always found it to be an interesting feature from a security and anti-abuse perspective. What does the blue checkmark actually mean, and what do people think it means? What would happen (if anything) if Twitter had a bug where all the blue checkmarks disappeared for a day?
Twitter has gotten a lot more transparent recently about what the blue checkmark means and is meant to achieve. Their documentation says that it’s used to mark authentic accounts of public interest. But there is still a lot to ponder about what those words mean (what’s “public interest”? what’s “authentic”?) and why this is a useful feature. The verification program could be motivated by mis-/disinformation, harassment and abuse (e.g. preventing impersonation), scams and phishing, or some combination of the above. It’d be fascinating to know what Twitter’s internal success metrics (if any) are for the blue checkmark feature.
Here are some interesting recent examples of why account verification is a complex feature:
- FBI: a verified Twitter account for a TV show called “FBI”. My friend Eric Mill pointed me to this example and questioned why an account that doesn’t represent the actual Federal Bureau of Investigation should get to use the display name “FBI” with a blue checkmark next to it. Apparently, verified accounts are allowed to have display name collisions, or perhaps aren’t proactively identified.
- A mistakenly verified fake account of author Cormac McCarthy. Earlier this year, Twitter mistakenly verified a parody Cormac McCarthy account – and they did so proactively after a viral tweet from the account. Oops.
- Many examples of verified accounts spreading mis- or disinformation. If the goal of the blue checkmark is to help users identify high-quality sources of information, it clearly isn’t a perfect solution.
None of these examples serve to illustrate that the blue checkmark is a bad feature, but rather that it has a complex value proposition. There are at least two major sources of complexity. One is that the identity verification process itself is likely subject to human error and judgement, and quite possibly exploitable too. The other is that it’s not clear (to me, at least) how users interpret and act on the blue checkmark.
Does the blue checkmark have a threat model?
If you, like me, pay attention to the world of web browser security, the blue checkmark feature probably reminds you of a browser security feature: Extended Validation (EV) certificates. An EV certificate is a type of HTTPS certificate that entails an identity verification process beyond the normal domain name control validation that is more typically involved in obtaining an HTTPS certificate. Browsers used to show a special green indicator in the address bar for EV certificates, though recently there has been a trend towards making this identity indicator much more subtle.
EV indicators and blue checkmarks are both indications of some kind of identity verification process. They both have some ambiguity around their threat models and value. And they both have dual sources of complexity: the identity verification process itself and the question of how people comprehend the identity verification indicator.
For EV, most security experts define the threat model around phishing: in theory, users would not enter credentials into a phishing page because they would be tipped off to the attack by the absence of the EV indicator in the browser UI. Because EV has at least the semblance of a threat model, it’s possible to design experiments that measure whether the mechanism actually protects against the intended threats. On the identity verification process itself, researchers have obtained proof-of-concept certificates with misleading names and cross-jurisdiction name collisions. On comprehension of the EV indicator, early on in EV’s life, several lab studies failed to show that it achieved its anti-phishing goals. My team on Chrome ran a field study showing that experimentally removing the EV indicator didn’t change users’ interactions with websites – an unexpected result if you believe that the EV indicator helps users identify phishing attacks.
On the other hand, I’m not aware of much research (especially computer science research; send me links!) about the blue checkmark identity verification process or how users comprehend the blue checkmark. I suspect that if you asked 3 security experts about the blue checkmark’s threat model, you’d get 3 different flavors of answers: (1) impersonation and abuse, (2) mis-/disinformation, and (3) it’s not a security feature and doesn’t have a threat model. Without a well-defined threat model, it’s difficult to rigorously study the success of the feature. Nevertheless, if I had infinite time, resources, and power to indulge my blue checkmark fascination, there are a few experiments I would run.
My blue checkmark research agenda
If I were an academic researcher working in this area, I would work on understanding the identity verification process in a black-box way. The challenge is that this must be done in an ethical way, which probably means that you can’t, for example, attempt to actually get fake accounts verified. An empirical study (with proper disclosure practices) is likely to be both ethical and fruitful, e.g. gathering datasets of verified accounts on different social media platforms and identifying name collisions, categorizing their trustworthiness, or looking for patterns of misbehavior (such as spreading misinformation). This might give some insight into how robust account verification processes are.
The more interesting hypothetical research agenda is what I would do if I were a user researcher working at Twitter, with access to internal data and the ability to run in-product experiments. From this vantage point, I would study how users comprehend the blue checkmark.
I might start with a lab study where people view disinformation with or without a blue check, and survey them to understand if they are more skeptical of the information when the blue checkmark is absent. My guess is that Twitter has already done this study internally, and I would be very interested to see the results!
With access to internal data, I think it would be possible to understand whether the blue checkmark program helps with impersonation, harassment, and abuse. For example, one could look at whether people become less likely to report impersonation or harassment after they get verified. However, these results would need to be interpreted with a grain of salt, because the blue checkmark itself could make people more likely to become targets of this kind of abuse, among other possible conflating factors.
I’m not sure what the equivalent of Chrome’s EV removal field study would be for the blue checkmark. Twitter could experimentally remove the blue checkmark for a subset of verified accounts, but it’s not clear what to measure then – partially because the threat model isn’t clear, and partially because the intended effect of the blue checkmark may not be directly measurable. I suppose they could measure whether impersonation/harassment reports go up for that subset of accounts over time. Or they could compile a dataset of known misinformation websites (as in this paper) and identify verified accounts that spread that misinformation. They could then experimentally un-verify those accounts and determine if engagement with misinformation on those accounts drops.
Another flavor of experiment would be to remove the blue checkmark from all verified accounts for a subset of users, and measure how a variety of user metrics might change. But this is not a particularly scientific approach without a clear hypothesis about how we expect user behavior to change with the ablation of the blue checkmark feature. This lack of hypothesis differs from our EV study where we could clearly articulate and measure what we expected to change. (Namely, we expected that metrics such as form submissions and time spent on page would decrease on EV websites, since we hypothesized that users would think they were phishing pages due to the lack of EV indicator.) Perhaps in the blue checkmark experiment, we might hypothesize that users are more likely to share information from verified accounts than unverified. Thus we would expect users in the experiment group to retweet and share less from verified accounts and more from unverified accounts, compared to users in the control group.
I’ll close with the slightly cynical observation that I may be entirely overthinking the blue checkmark; it could be that blue checkmarks aren’t really about security, anti-abuse, or mis-/disinformation at all, but rather that they’re just a way to drive more engagement from particular users. Nevertheless, the identity verification problem on social media platforms is real, so I find it interesting to think about how that problem could be defined and solved, regardless of whether social media platforms are tackling it in earnest today.