On social media, due to complex interactions between users' attention and recommendation algorithms, the visibility of users' posts can be unpredictable and vary wildly, sometimes creating unexpected viral events for `ordinary’ users. How do such events affect users' subsequent behaviors and long-term visibility on the platform? We investigate these questions following a matching-based framework using a dataset comprised of tweeting activities and follower graph changes of 17,157 scientists on Twitter. We identified scientists who experienced `unusual' virality for the first time in their profile lifespan (`viral' group) and quantified how viral events influence tweeting behaviors and popularity (as measured through follower statistics). After virality, the viral group increased tweeting frequency, their tweets became more objective and focused on fewer topics, and expressed more positive sentiment relative to their pre-virality tweets. Also, their post-virality tweets were more aligned with their professional expertise and similar to the viral tweet compared to past tweets. Finally, the viral group gained more followers in both the short and long terms compared to a control group.