IEEE Technology and Society Magazine - March 2023 - 14

Book Review
pillars-trashing, pretexting, bullshitting, and penetration-and
how they apply specifically to mass and
interpersonal social engineering. Gehl and Lawson
argue that " masspersonal " social engineers use the
same techniques as these earlier social engineers to
great effect on individual and societal levels, all made
possible by " the unique affordances of the internet and
social media platforms. " Masspersonal social engineering,
they claim, not only describes the strategies of the
IRA and Cambridge Analytica, but also presents a new,
unique cause for societal concern. Unfortunately, what
the book fails to capture is perhaps more important:
what allows for rampant online manipulation, and how
we can release its hold over us.
Let us start with what the book gets right. As far as
tools to describe online manipulation, this book is
a decent place to start. The provided framework for
masspersonal social engineering highlights the steps
necessary to create what is essentially immersive
online propaganda. In doing so, it gives one the tools
to compare instances of online manipulation and to
distill what the bad actors are trying to do. The book
takes a roundabout path to deliver the framework-it is
not until page 165 of the 225-page book that the authors
turn from the earlier social engineers, leaving time to
discuss only two examples of masspersonal social engineering.
But the lengthy history does fulfill the authors'
promise to connect masspersonal social engineering
to its ancestors, and various digressions on the toxic,
hypersexualized language used by computer hackers
and garbage as an overlooked byproduct of knowledge
production may be of interest to some.
In the last chapters, to showcase masspersonal
social engineering, the authors apply it to the IRA
and Cambridge Analytica. The framing for the IRA's
actions is cohesive enough, but the application to
Cambridge Analytica raises some potential issues with
the four pillars of their framework. The authors treat
these four pillars-trashing, pretexting, bullshitting,
and penetrating-as essential ingredients of masspersonal
social engineering. For Cambridge Analytica,
however, they manipulate the functions of these
pillars to better fit the framework to the event. The
authors recap how Cambridge Analytica captured
the data of 87 million Facebook users (i.e., " trashing " )
using innocuous appearing games and web applications-strategies
which the authors describe as " pretexting. "
This use of pretexting to facilitate trashing is
different from the function of pretexting elsewhere in
the book, which is typically to gain credibility with
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the target during active manipulation. This malleability
of the four pillars suggests that the framework as
a whole might not be so precise. That said, one can
imagine applying it to examples not discussed in the
book, such as guerrilla marketing campaigns, so the
concept probably has broader use.
The book presents a reasonable framework for discussing
online manipulation, but the authors go one
step further than presenting a new framework. Gehl and
Lawson claim that masspersonal social engineering presents
a unique threat to society. The events highlighted
by the book are certainly representative of a great disinformation
crisis facing our nation. But masspersonal
social engineering fails to fully capture and address the
core, systemic problems contributing to disinformation,
instead focusing on individual behaviors. Specifically,
the authors fail to address the unique, critical role that
social media companies like Facebook play in making
disinformation schemes possible.
The authors mention that Facebook is incentivized
to sell advertising space to anyone with the
cash, and they correctly call for stronger data protection
laws in the United States to match those in
Europe. But the real reason behind events like these
is not that bad actors can advertise on Facebook
or that Facebook makes personal data more accessible;
addressing these problems alone would be
too superficial to prevent manipulation. To explain
why, it is necessary to illustrate how social media
companies have intentionally created platforms that
actively spread disinformation, and why they will
continue to do so without major outside regulation.
Social media websites such as Facebook make
use of machine-learning models to decide what content
should be served to users. In order to maximize
user engagement, and therefore profits, Facebook's
models are specifically configured to spread polarizing,
inflammatory, and often misleading content like
wildfire. In an expose on Facebook's machine-learning
models, Karen Hao of the MIT Technology Review
confirmed exactly this:
" A former Facebook AI researcher who joined in
2018 says he and his team conducted 'study after
study' confirming the same basic idea: models
that maximize engagement increase polarization.
They could easily track how strongly users agreed
or disagreed on different issues, what content
they liked to engage with, and how their stances
changed as a result. Regardless of the issue, the
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