Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit

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The design of online platforms is both critically important and challenging. Any changes to large complex socio-technical systems may lead to unintended consequences, and it can be hard to predict how users will react. Here we conduct a case study of a particularly important real-world platform design change: Twitter's decision to change their signature feature by doubling the character limit from 140 to 280 characters to soothe users' need to ''cram'' or ''squeeze'' their tweets. The platform change had been informed by Twitter's modeling of historical user behavior, estimating that with a 280-character limit, over 99% of English tweets would require no cramming. In our analysis, we contrast Twitter's anticipated pre-intervention predictions about user behavior with actual post-intervention user behavior: Did the platform design change lead to the intended user behavior shifts, or did a gap between anticipated and actual behavior emerge? Did different user groups react differently? We find that even though users do not "cram" as much under 280 characters as they used to under 140 characters, emergent "cramming" at the new limit seems to not have been taken into account when designing the platform change. Furthermore, investigating textual features, we find that, although post-intervention "crammed" tweets are longer, their syntactic and semantic characteristics remain similar and indicative of "squeezing". Applying the same approach as Twitter policy-makers, we create updated counterfactual estimates and find that the character limit would need to be increased further to reduce cramming that re-emerged at the new limit. We contribute to the rich literature studying online user behavior with an empirical study that reveals a dynamic interaction between platform design and user behavior, with immediate policy and practical implications for the design of socio-hyp technical systems. Our results highlight the need to carefully consider multiple factors when designing a platform change, including the dynamic nature of user response and the impact the change will have on different populations depending on users' languages and devices.