Many modern Internet applications, like content moderation and recommendation on social media, require reviewing and score a large number of alternatives. In such a context, the voting can only be sparse, as the number of alternatives is too large for any individual to review a significant fraction of all of them. Moreover, in critical applications, malicious players might seek to hack the voting process by entering dishonest reviews or creating fake accounts. Classical voting methods are unfit for this task, as they usually (a) require each reviewer to assess all available alternatives and (b) can be easily manipulated by malicious players.This paper defines precisely the problem of robust sparse voting, highlights its underlying technical challenges, and presents Mehestan, a novel voting mechanism that solves the problem. Namely, we prove that by using Mehestan, no (malicious) voter can have more than a small parametrizable effect on each alternative's score, and we identify conditions of voters comparability under which any unanimous preferences can be recovered, even when these preferences are expressed by voters on very different scales.