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Anafo, C., & Ngula, R. S. (2020). On the grammar of scam: transitivity, manipulation and deception in scam emails. WORD, 66(1), 16-39.

Schaffer, D. (2012). The language of scam spams: linguistic features of" Nigerian fraud" e-mails. ETC: A Review of General Semantics, 157-179.

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https://docs.google.com/spreadsheets/d/19sutnenOrODxvWkhc8rRv5ka2YGZbBwOcmxgGt9lIUk/edit#gid=0

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Suggestions to Avoid or Reduce Malicious Spam

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Suggestions to Avoid or Reduce Malicious Spam

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don't respond to spam

do not click on any suspicious links

check address

check for anomalies, e.g. typos, symbols, fake logos

refrain from sharing personal information electronically

check anti-scam websites, check official website

report suspicious e-mails

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not every spam mail is scam (content wise) → had to filter it first manually

quantity of scam during research period

counting words (method, complex)

general work with excel (logistics)

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Themes:

payment, finance

security, warnings, alerts, reminders

prize, rewards

health, fitness, dietary

job offfer

delivery

Outcome:

total no. of words counted 214 -> you/ your most frequent

most frequent word classes:

verbs and nouns

possessive determiners and pronouns

Functions

Adverbs

create pressure, stress urgency and call for immediate action

Possessive determiners (your) / personal pronouns (you)  used by scammers

to win trust of recipients and encourage immediate action by addressing them directly

to position the scammer and target recipients differently ("me-&-you" - dimension)

possessive det. + nouns as reference to material possessions (e.g. your payment, your account₎

(Anafo, Ngula 2020, 35)

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Please take a quick look into your spam folder from your mails. Do our results correlate with what you can see in your spam?

Have you ever fallen victim to a scam? Please explain.
What would you do differently now?
What do you do to protect yourself from spam?

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Corpus-driven / inductive approach

mainly quantitative

write down all words from subject lines of 40 emails over two weeks

count frequency of words

with excel, COUNTA in pivot table

mixed with qualitative method

assign word classes to words

→ detect pattern

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spam = „‘Unsolicited bulk emails’“

first spam sent in 1978

100 billion spam messages daily (2008)

scam = fraudulent emails

(Schaffer 2012, 158)

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Which word classes commonly occur within scam email subject lines and how are they used to create deception?