  Hashtag has emerged as a widely used concept of popular culture and
campaigns, but its implications on people's privacy have not been investigated
so far. In this paper, we present the first systematic analysis of privacy
issues induced by hashtags. We concentrate in particular on location, which is
recognized as one of the key privacy concerns in the Internet era. By relying
on a random forest model, we show that we can infer a user's precise location
from hashtags with accuracy of 70\% to 76\%, depending on the city. To remedy
this situation, we introduce a system called Tagvisor that systematically
suggests alternative hashtags if the user-selected ones constitute a threat to
location privacy. Tagvisor realizes this by means of three conceptually
different obfuscation techniques and a semantics-based metric for measuring the
consequent utility loss. Our findings show that obfuscating as little as two
hashtags already provides a near-optimal trade-off between privacy and utility
in our dataset. This in particular renders Tagvisor highly time-efficient, and
thus, practical in real-world settings.
