  This paper studies marijuana-related tweets in social network Twitter. We
collected more than 300,000 marijuana related tweets during November 2016 in
our study. Our text-mining based algorithms and data analysis unveil some
interesting patterns including: (i) users' attitudes (e.g., positive or
negative) can be characterized by the existence of outer links in a tweet; (ii)
67% users use their mobile phones to post their messages while many users
publish their messages using third-party automatic posting services; and (3)
the number of tweets during weekends is much higher than during weekdays. Our
data also showed the impact of the political events such as the U.S.
presidential election or state marijuana legalization votes on the
marijuana-related tweeting frequencies.
