  Once an email problem, spam has nowadays branched into new territories with
disruptive effects. In particular, spam has established itself over the recent
years as a ubiquitous, annoying, and sometimes threatening aspect of online
social networks. Due to its prevalent existence, many works have tackled spam
on Twitter from different angles. Spam is, however, a moving target. The new
generation of spammers on Twitter has evolved into online creatures that are
not easily recognizable by old detection systems. With the strong tangled
spamming community, automatic tweeting scripts, and the ability to massively
create Twitter accounts with a negligible cost, spam on Twitter is becoming
smarter, fuzzier and harder to detect. Our own analysis of spam content on
Arabic trending hashtags in Saudi Arabia results in an estimate of about three
quarters of the total generated content. This alarming rate makes the
development of adaptive spam detection techniques a very real and pressing
need. In this paper, we analyze the spam content of trending hashtags on Saudi
Twitter, and assess the performance of previous spam detection systems on our
recently gathered dataset. Due to the escalating manipulation that
characterizes newer spamming accounts, simple manual labeling currently leads
to inaccurate results. In order to get reliable ground-truth data, we propose
an updated manual classification algorithm that avoids the deficiencies of
older manual approaches. We also adapt the previously proposed features to
respond to spammers evading techniques, and use these features to build a new
data-driven detection system.
