  Phone scams remain a difficult problem to tackle due to the combination of
protocol limitations, legal enforcement challenges and advances in technology
enabling attackers to hide their identities and reduce costs. Scammers use
social engineering techniques to manipulate victims into revealing their
personal details, purchasing online vouchers or transferring funds, causing
significant financial losses. This paper aims to establish a methodology with
which to semi-automatically analyze scam calls and infer information about
scammers, their scams and their strategies at scale. Obtaining data for the
study of scam calls is challenging, as true scam victims do not in general
record their conversations. Instead, we draw from the community of ``scam
baiters'' on YouTube: individuals who interact knowingly with phone scammers
and publicly publish their conversations. These can not be considered as true
scam calls, however they do provide a valuable opportunity to study scammer
scripts and techniques, as the scammers are unaware that they are not speaking
to a true scam victim for the bulk of the call. We applied topic and time
series modeling alongside emotion recognition to scammer utterances and found
clear evidence of scripted scam progressions that matched our expectations from
close reading. We identified social engineering techniques associated with
identified script stages including the apparent use of emotion as a social
engineering tool. Our analyses provide new insights into strategies used by
scammers and presents an effective methodology to infer such at scale. This
work serves as a first step in building a better understanding of phone scam
techniques, forming the ground work for more effective detection and prevention
mechanisms that draw on a deeper understanding of the phone scam phenomenon.
