Abstract: Email has become a fast and cheap means of online
communication. The main threat to email is Unsolicited Bulk Email
(UBE), commonly called spam email. The current work aims at
identification of unigrams in more than 2700 UBE that advertise
body-enhancement drugs. The identification is based on the
requirement that the unigram is neither present in dictionary, nor is a
slang term. The motives of the paper are many fold. This is an
attempt to analyze spamming behaviour and employment of wordmutation
technique. On the side-lines of the paper, we have
attempted to better understand the spam, the slang and their interplay.
The problem has been addressed by employing Tokenization
technique and Unigram BOW model. We found that the non-lexicon
words constitute nearly 66% of total number of lexis of corpus
whereas non-slang words constitute nearly 2.4% of non-lexicon
words. Further, non-lexicon non-slang unigrams composed of 2
lexicon words, form more than 71% of the total number of such
unigrams. To the best of our knowledge, this is the first attempt to
analyze usage of non-lexicon non-slang unigrams in any kind of
UBE.
Abstract: e-mail has become an important means of electronic
communication but the viability of its usage is marred by Unsolicited
Bulk e-mail (UBE) messages. UBE consists of many types
like pornographic, virus infected and 'cry-for-help' messages as well
as fake and fraudulent offers for jobs, winnings and medicines. UBE
poses technical and socio-economic challenges to usage of e-mails.
To meet this challenge and combat this menace, we need to
understand UBE. Towards this end, the current paper presents a
content-based textual analysis of more than 2700 body enhancement
medicinal UBE. Technically, this is an application of Text Parsing
and Tokenization for an un-structured textual document and we
approach it using Bag Of Words (BOW) and Vector Space Document
Model techniques. We have attempted to identify the most
frequently occurring lexis in the UBE documents that advertise
various products for body enhancement. The analysis of such top
100 lexis is also presented. We exhibit the relationship between
occurrence of a word from the identified lexis-set in the given UBE
and the probability that the given UBE will be the one advertising for
fake medicinal product. To the best of our knowledge and survey of
related literature, this is the first formal attempt for identification of
most frequently occurring lexis in such UBE by its textual analysis.
Finally, this is a sincere attempt to bring about alertness against and
mitigate the threat of such luring but fake UBE.