# Exploring the Online Micro-targeting Practices of Small, Medium, and Large
Businesses

Salim Chouaki  Univ. Grenoble Alpes, CNRS, Grenoble INP, LIGFrance
<EMAIL_ADDRESS>,  Islem Bouzenia  Univ. Grenoble Alpes,
CNRS, Grenoble INP, LIGFrance<EMAIL_ADDRESS>,  Oana
Goga  Univ. Grenoble Alpes, CNRS, Grenoble INP, LIGFrance oana.goga@univ-
grenoble-alpes.fr and  Béatrice Roussillon  Univ. Grenoble Alpes, GAELFrance
<EMAIL_ADDRESS>
###### Abstract.

Facebook and other advertising platforms exploit users’ data for marketing
purposes by allowing advertisers to select specific users and target them with
well-crafted messages (the practice is being called micro-targeting). However,
advertisers such as Cambridge Analytica have maliciously used these targeting
features to manipulate users in the context of elections. The European
Commission plans to restrict or even ban some targeting functionalities in the
new European Democracy Action Plan act to protect users from such harms. The
difficulty in finding appropriate restrictions is that we do not know the
economic impact of these restrictions on regular advertisers. In this paper,
to inform the debate, we take a first step by understanding who is advertising
on Facebook and how they use the targeting functionalities of ad platforms.
For this, we asked 890 U.S. users to install a monitoring tool on their
browsers to collect the ads they receive on Facebook and information about how
these ads were targeted. By matching advertisers on Facebook with their
LinkedIn profiles, we could see that 71% of advertisers are small and medium-
sized businesses with 200 employees or less, and they are responsible for 61%
of ads and 57% of ad impressions. Regarding micro-targeting, we found that
only 32% of small and medium-sized businesses and 30% of large-sized
businesses in our datasetet micro-target at least one of their ads. These
results should not be interpreted as micro-targeting not being useful as a
marketing strategy, but rather that advertisers prefer to outsource the micro-
targeting task to ad platforms. Indeed, to deliver ads, Facebook is employing
optimization algorithms that exploit user data to decide which users should
see what ads; which basically means ad platforms are performing an
algorithmic-driven micro-targeting. Hence, when setting restrictions on micro-
targeting, legislators should take into account both the traditional
advertiser-driven micro-targeting as well as algorithmic-driven micro-
targeting performed by ad platforms.

Facebook advertising system, online advertising practices, micro-targeting,
enterprises, advertising regulations

††ccs: Social networks Online advertising

##  1\. Introduction

Digital advertising is an industry of billions of dollars that is
outperforming other traditional advertising industries. According to Statista,
356 billion U.S. dollars were spent on digital advertising in 2020 (Statista,
2021b) while only 172 billion dollars were spent on offline advertising
(Statista, 2021a). The enormous success of online advertising platforms is
generally justified by the precise targeting features they offer (Facebook,
2021c). Ad platforms exploit the data they collect on users to infer their
interests and demographics, allowing advertisers to target audiences with
precise characteristics. For instance, advertisers can target groups of users
as small as hundreds and as specific as “people interested in ¡¡Environmental
impact of meat production¿¿ that have a ¡¡Doctoral degree¿¿ and who recently
moved to Paris”. Facebook proposes to advertisers over 240k such attributes to
select their audiences (Speicher et al., 2018b). We call this form of
targeting, advertiser-driven micro-targeting as the advertisers themselves
select the precise characteristics of the audience they want to reach. A
complementary and lesser-known type of micro-targeting is algorithmic-driven
micro-targeting, where ad platforms themselves exploit user data (going beyond
user’s interests and demographics) to decide which users should receive what
ads. For example, when delivering ads, Facebook computes how “relevant” ads
are to specific users and which users will bring more value if they see the ad
(adr, 2021; Ali et al., 2021, 2019b). Effectively, it is not the advertiser
but the ad platform that decides the right audience for a particular ad.
Algorithmic-driven micro-targeting can be done standalone or in conjunction
with advertiser-driven micro-targeting (fac, 2021e).

From an economics perspective, improving the matching between consumers and
products through micro-targeting increases consumer surplus and decreases
producer costs (Goldfarb, 2014; Bergemann and Bonatti, 2011). Consumers save
time and effort searching for the right product and information, while
producers, obtain a high return on investment by reaching interested consumers
at small costs. In addition, micro-targeting is seen as a marketing tool that
levels up the competition between large-seized businesses (BEs) and small and
medium-sized businesses (SMEs), leading to a more fair market (Goldfarb, 2014;
Goldfarb and Tucker, 2019).

On the other hand, form a security and privacy perspective, micro-targeting
can be weaponized by malicious third parties to influence user’s decisions, by
targeting messages that resonate well with users beliefs and personalities
(Merrill and Kozlowska, 2019; Wong, 2019; Shane, 2017; Lewandowsky et al.,
2020; Susser et al., 2019). The Cambridge Analytica scandal and the Russian
interference in the U.S. 2016 election are concrete examples of how micro-
targeting can be used to engineer polarization, promote voter disengagement,
or manipulate voters (Shane, 2017; Penn, 2017; Wong, 2019; Confessore, 2018).

As a result, many governments and regulators are studying how to regulate
online advertising and micro-targeting. For example, the European Commission
is currently working on two acts: the Digital Services Act and the European
Democracy Action Plan (DSA, 2021; EDA, 2021). One controversial solution on
the European Commission’s table is to ban altogether or restrict the ability
to micro-target ads online, especially if they contain political messages
(Lomas, 2020). Another solution supported by many privacy advocacy groups is
to restricting web tracking, limiting this way the possibility to build user
profiles.111Solution supported by the European Digital Rights (EDRi) in their
report “Targeted online: An industry broken by design and by default” and
governmental agencies such as the Norwegian Consumer Council in their report
“Time to ban surveillance-based advertising” (Lemoine et al., 2021; Council,
2021). While these restrictions are desirable from a security and privacy
perspective, the difficulty in finding appropriate regulations is that we do
not know their economic impact on regular advertisers and how they will impact
their economic growth, customer acquisition, and, more broadly, the fairness
of the market. For example, banning micro-targeting might unevenly impact
small and medium-sized businesses compared to large-sized businesses, and
Facebook has brought harming small-sized businesses as the main argument
against the new Apple’s iOS 14 restrictions on cookie tracking (Ha, 2020; fb_,
2021). While these economic considerations are a barrier in adopting
regulations, there is a lack of data and studies on the use of micro-targeting
technologies by small, medium, and large-sized businesses and the usefulness
of web tracking.

In this paper, we take a first step toward understanding the potential
economical impact of such restrictions by exploring to which extent small and
medium-sized businesses make use of online advertising and analyzing which
micro-targeting practices they employ. We focus this study on Facebook as it
is one of the most used online advertising platform and accounts for 19.3% of
the market share (Liberto, 2019). To audit advertisers and their micro-
targeting, we developed a monitoring tool that users can install on their
browsers. This monitoring tool observes and sends, to our servers, the ads
seen by users in their Facebook timeline and the targeting options used by the
advertisers to target the ads sourced from the “Why am I seeing this ad?”
feature (Andreou et al., 2018) (Section 3). Using a crowdsourcing platform, we
recruited 890 users across multiple states in the U.S. to install our
monitoring tool for at least six weeks. In total, we collected 102k ads,
coming from 40k advertisers targeted during Nov 2020 to Jan 2021. The data
collection is covered by an IRB and is compliant with GDPR procedures. The
data collected from the monitoring tool provides us with information about the
name, website, and sometimes the address of the advertisers; however, it does
not provide data about the size of the corresponding business. To get such
information, we propose a method to match the Facebook profiles of advertisers
with their corresponding LinkedIn profiles (Section 3). We estimate the
accuracy of our matching method to 86.5%. As a result, we found a LinkedIn
profile for 66% of advertisers in our database.

Our results, in Section 4, show that 71% of advertisers in our matched dataset
(i.e., advertisers for which we found a LinkedIn profile) are SMEs with 200
employees or less, and they are responsible for 61% of ads and 57% of ad
impressions. In addition, we observed that out of a sample of 100 unmatched
advertisers we manually checked (with no LinkedIn profile), the majority
correspond to individual entrepreneurs: 28% corresponded to personal Facebook
pages (e.g., photographer, model), 24% were Facebook pages for individual
services (e.g., house keeper, pet trainer), and another 24% were online stores
run by individuals. Hence, our results show that the large majority of
advertisers on Facebook are small and medium-sized businesses. This supports
economic models that predict that the relatively low cost of micro-targeting
enables smaller businesses to access advertising markets from which they were
previously excluded (Taiminen and Karjaluoto, 2015; Bagwell, 2007; Anderson,
2006; Goldfarb, 2014).

The second question we address in this paper is to which extent businesses
employ advertiser-driven micro-targeting strategies (Section 5). We consider
that if advertisers only specify the age, gender, and location of the users
they want to reach, they are not micro-targeting their ads. Otherwise, we
consider that advertisers are micro-targeting their ads if they target their
ads to users that have specific attributes (e.g., users interested in organic
food, users who are parents) or to users that have previously interacted with
their business (e.g., users who bought from their shop, users who visited
their website). Our results show that only 32% of small and medium-sized
businesses and 30% of large-sized businesses use micro-targeting in at least
one of their ads. In addition, 46% of advertisers with no LinkedIn profile
used advertiser-driven micro-targeting. Hence, these results show that most
businesses do not seem to rely on advertiser-driven micro-targeting. This is a
significant shift from a study in 2019 that found that 64% of the ads
collected were micro-targeted by advertisers (Andreou et al., 2019). It is
important to understand, however, that these results do not imply that micro-
targeting is not useful as a marketing strategy; they rather suggest that the
advertisers prefer to outsource the micro-targeting task to the ad platform.
Indeed, Facebook claims to perform algorithmic-driven micro-targeting for
every ad impression (fac, 2021e). The shift toward algorithmic-driven micro-
targeting could be explained by ad platforms’ algorithms getting better than
advertisers at finding the right audiences to target ads.

We finally investigate the link between web tracking and micro-targeting on
Facebook as the platform already has a lot of information about the behavior
of users inside the platform (Section 6). Facebook claims that user’s
interests are only inferred from user’s activity inside Facebook (fac, 2021f),
while “ad relevancy” scores are based on both inside and outside Facebook data
(fac, 2021d). Hence, most advertiser-driven micro-targeting options (except
retargeting) do not seem to rely on web tracking, while algorithmic-driven
micro-targeting relies on web tracking. This could be a possible explanation
for the popularity of algorithmic-driven micro-targeting among advertisers. To
understand the extent to which advertisers contribute to Facebook’s data
acquisition through web tracking, we investigate what fraction of advertisers
have installed the Facebook pixel on their website. The Facebook pixel is a
piece of code that allows Facebook to know which Facebook users have visited a
particular website (pix, 2021). Our results show that 81% of SMEs and 69% of
BEs we checked have deployed one or more Facebook pixels on their websites,
hence being a widespread practice. Nevertheless, only 2.6% of the ads sent by
SMEs and 2.7% of the ones sent by BEs were targeted explicitly with the
Facebook pixel, to reach people that visited the advertiser’s website. This
raises questions of whether there are any benefits for advertisers from
installing a Facebook pixel, besides retargeting visitors.

These findings have several implications. First, from a restrictions
perspective, the focus has been on restricting advertiser-driven micro-
targeting; however, there needs to be a discussion on whether the algorithmic-
driven micro-targeting done by ad platforms themselves is a practice that is
more acceptable or should be restricted as well. Second, from a fairness
perspective, previous works have shown that algorithmic-driven micro-targeting
can lead to discrimination across gender and political affiliation (Ali et
al., 2021). Especially if advertiser-driven micro-targeting is restricted, it
is important to study whether algorithmic-driven micro-targeting does not lead
to unfairness across advertisers. Third, from a transparency point of view,
the information in the “Why am I seeing this ad?” features is incomplete as it
only provides information about the advertiser-driven micro-targeting but not
about the algorithmic-driven micro-targeting, i.e., we know the
characteristics of the users the advertisers chose to target, but we do not
know the characteristics of users the ad platform choses to deliver the ads
to. Given the shift towards algorithmic-driven micro-targeting, this is an
important information to have. We hope these results will contribute to the
debate on finding adapted restrictions for online advertising and steer
research in devising methods to evaluate the economic impact of advertising
regulations. To support future research, the list of advertisers as well as
the details we collected about their businesses can be found on this link.

##  2\. Background

###  2.1. Advertising on Facebook

At a high level, advertising on Facebook works as follows: Facebook infers the
characteristics of its users; these characteristics are made available to
advertisers to target their ads; upon the creation of an ad, Facebook delivers
the ad to users in a way that maximizes the total value which is a combination
of the advertisers profit and the user’s quality of experience on the
platform. Next, we describe the important notions to know about the platform’s
actors (e.g., users, advertisers) and the process performed or triggered by
them (e.g., ad creation, ad delivery). We use quotes from Facebook’s help
pages, to illustrate precisely the current descriptions.

#### Users.

From an advertising process perspective, a Facebook user is characterized by a
set of interests (e.g., pancakes, swimming, guitar) and demographics (e.g.,
age, gender, location). Facebook infers these characteristics based on data
about the activity of the user (fac, 2021f):

> “Ads they click; Pages they engage with; Activities people engage in on
> Facebook related to things like their device usage, and travel preferences;
> Demographics like age, gender and location; The mobile device they use and
> the speed of their network connection.”

Hence, while Facebook is able to observe the activity of users both inside and
outside Facebook (through web tracking (pix, 2021)), Facebook claims to only
use inside data to infer the interests and demographics of users (Andreou et
al., 2018).

#### Advertisers.

To run ads on Facebook, one needs to create an advertising account. There are
two types of advertising accounts: personal and business. To create a personal
advertising account, the user only needs a Facebook account and a valid credit
card. A personal advertising account allows the user to create multiple
Facebook pages and run ads associated with them. The associated Facebook pages
do not need to be registered businesses. They can represent anything, be it
commercial or non-commercial, and range from personal cooking blog and TV Show
to a retail or clothing company. To create a business advertising account, the
user needs to prove the existence of their business to Facebook, by giving the
official registered number in the local business license for example. A
business account can be used by a group of people and can associate multiple
advertising accounts and pages. Both types of accounts can use the same
targeting options, the only difference is for custom audience-based micro-
targeting where a business account can have up to 100 pixels (trackers) while
a personal account can only have one (fac, 2021g). Furthermore, a business
account offers advantages in terms of advertising management by allowing
multiple people and external collaborators or contractors to create ads on
behalf of the business.

When a user receives an ad on Facebook, s/he will know that the ad comes from
(one of the) Facebook pages associated with the advertiser, but s/he will not
know whether the advertiser behind uses a personal or business account, and
what is the name of the person or business that is responsible for the ad.

#### Ad creation.

Facebook provides advertisers with an interface to mange and create ads,
called Ads Manager (ads, 2021). The _first_ step in configuring an advertising
campaign is to choose the objective of the campaign. Facebook proposes three
main categories of objectives: awareness, consideration, and conversion (obj,
2021). Brand awareness is about reaching new people and inform them about the
services of the business. Consideration campaigns target people who are most
likely to consider checking the business and look for more information about
it. Conversion campaigns target users who are most likely to buy a product or
subscribe to a service. In the _second_ step, the advertisers need to
configure the campaign in terms of budget, running time (start and end date),
targeted audience (the characteristics of users they want to reach), placement
of the ad (e.g., Facebook, Instagram, mobile, computer), frequency of ad
delivery (how many times a user should see the ad per day), and bid control
(the maximum budget to pay for one ad impression). _Finally_ , the advertiser
needs to input the _content_ of the ad in terms of the ad text, media (e.g.,
image, video), landing URL (e.g., website, Facebook page), and call-for-action
button (e.g., Buy now, Learn more).

#### Ad delivery.

The Facebook ad delivery system uses ad auctions and machine learning
algorithms to determine where, when and to whom to show an ad. These processes
work together to maximize value for both users and advertisers by taking into
consideration three factors:

  1. (1)

Bid: What the advertiser can pay to achieve the desired outcome.

  2. (2)

Estimated action rates: An estimate of whether a particular person engages
with or converts from a particular ad.

  3. (3)

Ad quality: A measure of the quality of an ad as determined from many sources,
including feedback from people viewing or hiding the ad and assessments of
low-quality attributes in the ad, such as withholding information,
sensationalized language, and engagement bait.

Facebook’s estimation of how relevant an ad is to a user plays a significant
role in the actual ad delivery. In Facebook’s words (fac, 2021e):

> “Each time there’s an opportunity to show an ad to someone, an ad auction
> takes place to determine which ad to show.” …“Since we want each person to
> see relevant ads, the ad auction considers predictions of each ad’s
> relevance for the particular person. This means that ads with higher
> relevance can win ad auctions at lower costs.” … “Relevance predictions
> estimate how likely the person is to consider the ad high quality and take
> the advertiser’s desired action.”

An ad that is relevant to a person could win an auction against ads with
higher bids. Each time an ad is displayed, the delivery system learns more
about delivery optimization. Thus, the more an ad is displayed, the better the
delivery system optimizes to whom to send the ad. This may have important
implications regarding the fairness of competition and it is further discussed
in the economics of advertising section.

###  2.2. Types of micro-targeting

In this paper, we point out that there are two broad types of micro-targeting
based on whether micro-targeting is driven by the advertiser or by the ad
platform’s algorithms.

Advertiser-driven micro-targeting results from the ad creation process when
advertisers choose the characteristics of the audience they want to reach. We
group the targeting options offered to advertisers in four categories:

  1. (1)

Generic targeting only: Corresponds to targeting using generic parameters such
as: age, gender, location, and language. These parameters can be used in
combination with other targeting parameters (see below); in this category we
include ads with generic targeting only. We do not consider this targeting as
micro-targeting since these categories are very broad.

  2. (2)

Attribute-based micro-targeting: Advertisers can send their ads to users who
match with specific attributes such as interests (e.g., online shopping,
movies, music), behaviors (e.g., engagement in political content, frequent
travelers), or demographics (e.g., relationship status, work, education). We
define an ad as targeted using attribute-based micro-targeting when the
advertiser chose one or more combinations of these attributes to target its
ads.

  3. (3)

Custom-audience micro-targeting: Advertisers can target users that previously
interacted with their business through custom-audiences. An advertiser can
form custom-audiences from different data sources: data files containing a
list of emails or users ids, website visits detected by a hidden Facebook
pixel, users who engaged with the advertiser’s page, video, or Instagram
profile. Re-targeting is part of this category as it contains users that
already interacted with the business. We consider this as micro-targeting
since the advertisers give a specific list of users, they want to reach, as
input to Facebook.

  4. (4)

Lookalike audience: While targeting existing customers is an important
requirement in online advertising, expending the customer base is equally
important. Facebook proposes a targeting mechanism called “lookalike
audiences” that allow advertisers to target users similar to the ones existing
in their custom audiences (Facebook, 2021d). When creating a lookalike
audience, it is Facebook’s algorithms that decide who are the similar users.
Thus, the advertisers do not know what are the characteristics used by
Facebook to extend the initial custom audience. We consider this as a form of
explicit algorithmic-driven micro-targeting, as the advertiser outsource the
work of finding relevant customers to Facebook, and has little control over
the characteristics of users in the lookalike audience.

Algorithmic-driven micro-targeting results from the optimization processes
used by the ad platform during ad delivery. When there is an opportunity to
show an ad, Facebook looks at all the ads that it could deliver to the
particular user, and chooses to show the one for which it has calculated the
highest ad relevancy score (Facebook, 2021a). Compared to lookalike audience,
this is an implicit form of algorithmic-driven micro-targeting. Facebook
markets algorithmic-driven micro-targeting as a tool to find the right
customers (fac, 2021h):

> “And unlike search and retargeting, we can identify a wide range of explicit
> and implicit intent signals to enable marketers to generate new demand and
> fulfill existing demand so you can reach potential valuable customers at the
> start of their journey…” “Today’s fastest growing brands don’t wait for
> customers to come to them. They don’t wait for purchase intent, they create
> purchase intent and Facebook makes it easier to accurately and predictably
> pair the right products to the right people to generate demand and sales at
> an enormous scale.”

It is important to note that Facebook performs algorithmic-driven micro-
targeting for every ad regardless of whether it has been micro-targeted, or
the advertiser only used generic targeting (fac, 2021e, d):

> “During ad set creation, you chose a target audience … and an optimization
> event … We show your ad to people in that target audience who are likely to
> get you that optimization event”.

> “For example, imagine that the owner of a hair salon wants to reach women
> aged 20-60 in the city where the salon is located. She creates an ad on
> Facebook to show to the people in that area who are the most likely to be
> interested in the salon. Our system can show the hair salon’s ads to the
> people in that area who are most likely to be interested in the salon.
> Interests are determined based on activity on Facebook, such as Pages or
> posts that people like, posts or comments they make and activity off
> Facebook from apps they use and websites they visit. If someone sees the ad,
> it’s because they’re in the group of people that seems to fit this criteria
> set by the advertiser. This process can happen without Facebook having to
> tell the advertiser a person’s name or contact info.”

Facebook chooses to whom to send an ad based on the objectives of the campaign
(e.g., reach, calls, store traffic, etc.), and the quality of users’
experience with the advertiser’s ads.

> “When you make your Optimization for Ad Delivery choice for an ad set,
> you’re telling us to get you that result as efficiently as possible. In
> other words, your optimization choice is the desired outcome that our system
> bids on in the ad auction. For example, if you optimize for link clicks,
> your ads are targeted to people in your audience who are most likely to
> click the ads’ links.”

Besides some high-level explanations of the importance of algorithmic-driven
micro-targeting for advertisers, there is not much transparency around how
precisely it is done and what are all the data sources that enter the equation
(e.g., the role of web tracking or the content of the ad).

##  3\. Data Collection

Ideally, we would like analyze a representative sample of ads that users see
on Facebook and how these ads have been targeted. Facebook provides an Ad
Library (fac, 2021b) where one can search whether a specific advertiser has
any active ad campaigns. Sadly, the Ad Library does not provide historic data,
and we can only query ads for advertisers who’s names we already know; hence
making it impossible to get a representative sample of ads. The Ad Library
also lack information about how the ad was targeted. We opted to collect data
through a browser extensions that can observe the ads users receive on
Facebook, and augment the data with information from LinkedIn.

###  3.1. Data from Facebook

We developed a browser extension222CheckMyNews that is available on the Chrome
store and that users can install. The tool extracts all the ads a user
receives on their Facebook timeline. Our tool collects the text of the ad, the
media (e.g., image, video), and the advertiser who sent the ad (i.e., its
corresponding Facebook page). Our tool also collects the targeting information
shown in the “Why am I seeing this ad?” explanation. Facebook presents in this
ad explanation the characteristics of the audience chosen by the advertiser
(Hutchinson, 2019). It includes age, gender, location, targeted interests, and
type of targeted audience (e.g., custom audience, lookalike audience); see
Figure 3 in Appendix. As in previous works (Andreou et al., 2019) to identify
the ads, we search for objects that contain the pattern “Sponsored” in the
HTML of the Facebook timeline of a user. To get the information in the “Why am
I seeing this ad?” feature, we send a request to the Facebook API for each of
the collected ads.333There are, in fact, numerous technical difficulties that
come when trying to collect such data. For example, Facebook often takes an
adversarial approach where they change the HTML markup of their page to break
extensions such as ours. We developed a suite of strategies to cope with such
changes and quickly detect if the extension no longer collects data well. We
omit these details from the paper, but the code of our collection method can
be found here.

#### Quality of “Why am I seeing this ad?” explanations.

Previous work has shown that ad explanations provided by the “Why am I seeing
this ad?” feature show only one (out of potentially many) attributes selected
by advertisers to target their ads (Andreou et al., 2019). This limitation
does not affect our targeting types analysis in Section 5 since we just need
to detect one attribute to consider that the ad was micro-targeted.
Nevertheless, we launched several campaigns (reproducing the approach by
Andreou et al. (Andreou et al., 2018)) where we targeted the users that
installed our extension and had specific interests and demographics and
investigated if the ad explanations accurately portrayed the parameters we
chose to target our ads. The ad explanations correctly reflected our targeting
parameters. Indeed, Facebook announced they revamped this feature, and they
seem to have solved some of the limitations (fac, 2021j).

#### User recruitment.

We recruited 890 U.S. users on Prolific and asked them to install our
extension on the computer where they usually connect to Facebook and keep it
active, at least, for six weeks. To incentivize the users to follow our
requests, we gave them an initial payment of 0.5£ right after the installation
and a more significant bonus of 4.1£ at the end of the six weeks if they had
satisfied the minimum required activity level over the six weeks.444Payments
on Prolific are in £ irrespective of the country of origin of users.
Unfortunately, many users were concerned about installing software on their
computers and chose not to participate in our study. Being able to collect
data from 890 users is a remarkable effort. Some users kept the extension for
less than six weeks, while others kept it longer. Our dataset includes data we
collected over three months, from Nov 2020 until the end of Jan 2021.

#### Representativeness.

While we cannot guarantee Representativeness, we tried to limit biases by
recruiting users from various U.S. states and ethnicities. To validate that
the majority of users are from the U.S., we examined information in the “Why
am I seeing this ad?”; this ad explanation contains information about the
location that the advertiser targeted. The location was set to ”The United
States” or a U.S. state in 99% of the ads. We found 41 users that received at
least one ad where the location was set to another country. For 16 of them,
more than 70% of their ads were not targeted to the U.S.; we ignore these
users and their ads in the analysis (they represent less than 0.02% of all
ads). For the 25 other users, less than 10% of the ads they received were
targeted outside the U.S.; we consider only the ads targeted to U.S.
locations. Figure 5 (in Appendix.) presents the location of the users in our
dataset mapped from their IP addresses. Moreover, we infer the age and gender
of users from ad explanations. Our users are 65% men and 35% women (compared
to 55% men, 45% for U.S. users on Facebook), and 70% are between the age of 20
and 40. Figure 5 (in Appendix) shows the distribution of our users by age
compared to the one of Facebook.

#### High-level data description

In total, our users received 102k unique ads from 40k different advertisers in
their Facebook timelines. We call them the timeline-advertisers. The 102k ads
got 185k impressions. Impressions represent the number of times an ad has been
delivered to users. For example, if a user received the same ad twice or two
different users received the same ad, we say that the ad had two impressions.
We differentiate between ads using the unique ID attributed by Facebook to
each sent ad that we can find in the HTML content.

We were able to collect ad explanations for 45% of ads. The task to collect
the explanation of all ads is not easy because Facebook considers it an
abnormal behavior when bursting the platform with ad explanation queries, and
such behavior triggers a security check from Facebook. To avoid such an
annoying experience, we built a scheduler that waits five minutes between two
requests of ad explanations. This leads to loose 55% of the ad explanations.

In addition, our browser extension collects, once a day, the information
present in the Ad Settings page of the user (fac, 2021c). This page contains
information about the “Advertisers You’ve Seen Most Recently”. We call them
the adsettings-advertisers. The adsettings-advertisers nicely complement our
timeline-advertisers because they contain advertisers who target the user on
other devices where our extension is not installed. There are 53k unique
adsettings-advertisers. The overlap between timeline-advertisers and
adsettings-advertisers is 14k, and the total number of advertisers in our
dataset is 79k. Having two sets of advertisers, we obtained using two
different methods, is helpful to assess the generality of our results.

###  3.2. Data from LinkedIn

To identify the size of the business of each advertiser in our dataset, we
developed a method to match advertisers on Facebook with their corresponding
LinkedIn profiles. LinkedIn provides information about the number of employees
for each business in the about page. Our matching method consists of taking
the list of advertisers from our dataset and searching the company name on
Google by doing a query of the form: “NAME company LinkedIn” where NAME is the
name of the advertiser we have in our database. We take the first three search
results on Google; then, we look for the first search result that matches a
company profile on LinkedIn by checking if the URL is in the following form:
www.linkedin.com/company/name-of-company/about. When we find a match, we
extract the company’s profile page present in the about section of the
company. We found a match for 66% of our 79k advertisers (82% for timeline-
advertisers and 50% for adsettings-advertisers), which we call the matched-
advertisers. We call the unmatched-advertisers the remaining 34%.

To investigate why we did not find matches for all advertisers, we took a
random sample of 100 unmatched-advertisers and manually investigated if they
have a LinkedIn profile. We could not find a LinkedIn profile for any of them,
and they seem to correspond to personal service providers (e.g., pet training,
hair cutting) that do not seem to be registered businesses. This means that or
matching method does not only have a recall of 66%, but that unmatched-
advertisers simply do not have LinkedIn profiles.

To investigate the precision of our matching method, we randomly selected 200
Facebook advertisers for which our method found a match, and we manually
verified if the match was correct. The match was correct in 86.5% of the
cases. The mismatches correspond mostly to advertisers without a LinkedIn
profile for which our algorithm matched with the company that has the most
similar name. Furthermore, we investigated the matching accuracy across
businesses of different sizes by taking separate samples of 100 ads for small,
medium, and large-sized businesses. We found out that most mismatches happen
with small-sized businesses (25% error rate) while medium and large-sized
businesses have lower error rates (4% for medium-sized businesses, 1% for
large ones). The majority of small-sized businesses wrongly matched, are
matched with other small-sized businesses.

###  3.3. Data collection compliance and limitations

#### IRB and Ethic considerations

We only collected information about the ads and the corresponding ad
explanations. We did not collect any personally identifiable information such
as names, emails, or phone numbers of users, and we did not collect their
personal messages or personal posts. We clearly stated what data we collected
to the Prolific users, and before they installed the extension, they had to
sign a consent. All data collection that we present in this paper was reviewed
and approved by the Ethical Review Board of Inria and approved by the local
Data Protection Officer. The data collection is GDPR compliant, and users had
to give explicit consent (that has been previously validated by the Data
Protection Officer) to participate in the study, and they were able to leave
and ask for their data to be removed at any point in the study. Our data
collection does violate the Terms of Service of Facebook. Our IRB committee is
aware of this and has decided that the benefits of the research we conduct are
sufficiently justified to collect such data. The code of our browser extension
is open source as it can be viewed in the client’s machine, as any Chrome and
Firefox extension. Data collections such as ours were violating Prolific’s ToS
in October 2020. We contacted Prolific, and they agreed to allow us to run the
experiment under their strict supervision. Prolific is now allowing other
researchers to perform such tasks (pro, 2021).

#### Limitations

Even though the data collection we propose in our work is extremely valuable
and provides a unique opportunity towards analyzing advertisers and their
practices on Facebook. It also has some limitations:

  * (1) Our work is mainly based on data collected from a desktop browser extension that can capture the ads users receive on Facebook. However, many users also use Facebook through their phones and receive ads that we cannot collect. It is technically challenging (if not impossible) to provide such a tool for mobile phones. However, to provide a complete picture, we also analyze advertisers that appear in the ad settings pages of users (i.e., the “Advertisers you’ve seen most recently”), which contains the names of advertisers that targeted the users on other devices.

  * (2) We had to use a scheduler that waits five minutes between two ad explanation requests to avoid security checks from Facebook because bursting the platform with ad explanation queries is considered abnormal behavior. As a result, we could only collect the ad explanation for 45% of ads. The missing explanations are randomly spread across users, advertisers, and time. We did not observe any biases between ads for which we have explanations and ads for which we do not.

  * (3) Our study is limited to U.S. users and mostly U.S. advertisers. However, we make our code for collecting ads publicly available which might help further research in other countries and populations.

  * (4) Ad explanations from the “Why am I seeing this ad?” might have limitations. However, we described that previously known limitations do not affect our analysis. In addition, we performed several controlled experiments, and the ad explanations correctly presented our targeting parameters.

  * (5) While we cannot guarantee the representativeness of users and advertisers, we tried to limit biases by recruiting users from various U.S. states, genders, and ethnicities.

##  4\. Advertisers Characterization

One of the main goals of this paper is to understand to which extent small and
medium-sized businesses (SMEs) are using the Facebook ad platform compared to
large-sized businesses (BEs) and analyze their type of activity.

###  4.1. Analysis of businesses by size

LinkedIn provides information about the number of employees for each matched
advertiser. Figure 2 presents the histogram of the size of businesses that
advertise on Facebook for both timeline-advertisers and adsettings-advertisers
(for which we have a matching LinkedIn profile). The figure shows that 71% of
timeline-advertisers and 74% adsettings-advertisers are businesses with 200
employees or less (SMEs). The numbers are consistent across the two
advertiser’s datasets, which provides confidence the results generalize.
Besides, these fractions do not account for the unmatched-advertisers that are
likely small businesses as well. If we consider all timeline-advertisers, 58%
are SMEs, 24% are BEs, and 18% do not have a LinkedIn profile. If we consider
all adsettings-advertisers, 37% are SMEs, 13% are BEs, and 50% do not have a
LinkedIn profile.

While, in numbers, SMEs clearly account for the majority of the advertisers in
our dataset, the question is whether SMEs, in aggregate, spend more or less
money than BEs on advertising on Facebook. While we do not have information
about the amount of money spent on ads by advertisers, we have data about the
number of ad impressions (i.e., the number of times the ad appeared on the
screen of users in our dataset). If we assume that BEs did not pay more to
reach the users that installed our browser extension than SMEs, ad impressions
can be considered as a proxy for the ad budget.

The 32.5k timeline-advertisers with a LinkedIn profile, are responsible for
78k ads (out of 102k) and for 130k ad impressions (out of 185k). The 7.5k
timeline-advertisers with no LinkedIn profile, are responsible for a total of
24k ads (23% of the total ads in our dataset) and for 55k ad impressions (30%
of the total ad impressions in our dataset). Figure 2 presents the fraction of
ads and the fraction of ad impressions that come from businesses of different
sizes in timeline-advertisers (for which we found a match on LinkedIn). The
figure shows that 61% of the ads and 57% of ad impressions come from SMEs
while 39% ads and 43% ad impressions come from BEs. Hence, SMEs do seem to
account for the majority of ad impressions in our dataset.

If we consider all timeline-advertisers, 40% of ad impressions come from SMEs,
30% come from BEs, and 30% from advertisers without a LinkedIn profile. These
results support economic theories that predict micro-targeting to be a
powerful marketing tool for SMEs.

In our dataset, the median number of ads sent by SMEs is 1, while for BEs is
3. Hence, BEs send in general more ads than SMEs. This is expected as BEs
might have more products and bigger advertisement expenditure. In terms of ad
impressions, the median number of impressions for SMEs is 1, while for BEs is
5. In median, unmatched-advertisers have 1 ad and 1 ad impression which is
similar with SMEs.

Figure 1. Fraction of advertisers with different business size for timeline-
advertisers and adsettings-advertisers.

Figure 2. Fraction of ads and fraction of ad impressions by business size for
timeline-advertisers.

###  4.2. Analysis of businesses by activity

Each advertiser needs to create a Facebook page and select categories that
best describes their activity. We took a random sample of 1000 matched-
advertisers and we collected the categories mentioned on their Facebook
pages.555The Facebook API limits our requests to 250/day, it would take more
than 160 days to collect categories for all advertisers. Table 1 shows the
most frequent categories mentioned by SMEs and BEs in our dataset. The
granularity of the categories mentioned varies across advertisers, with many
choosing not to give more precise information than “Product/Service”. The
table shows that a wide range of industries are advertising on Facebook
ranging from “Education” to “Clothing” and to “News & Media”.

Table 1. Distribution of advertisers by activity (across both timeline-advertisers and adsettings-advertisers). Activity/Industry | SME (%) | BE (%) | UNMATCHED (%)
---|---|---|---
Product/Service | 21 | 21 | 24
Clothing | 10 | 7 | 0
Beauty & Cosmetics | 9 | 9 | 0
Retail & Store | 7 | 16 | 24
News & Media | 6 | 7 | 1
Nonprofit & Community | 6 | 5 | 10
Education | 4 | 2 | 0
Entertainment & Art | 3 | 4 | 5
Health & Medics | 3 | 3 | 1
Home | 3 | 1 | 0
App | 2 | 2 | 2
Other | 26 | 23 | 33

Table 1 presents in 4th column the categories listed by the unmatched-
advertisers (we took a sample of 500). The top three categories are:
“Product/Service” (24%), “Retail and Store” (24%) and “Non profit and
Community” (10%). Overall, these categories, differ from SMEs and BEs. Since
these categories are quite broad, we took a sample of 100 unmatched-
advertisers and check manually their business. We observed that 28
corresponded to personal Facebook pages, 24 to services offered by individuals
like pet training, housekeeping, 24 to personally owned stores, and 9 to non-
profit organizations. These activities seem to represent fringe activities or
individual entrepreneurs that are probably too small or too occasional to
necessitate the creation of a LinkedIn profile. However, despite their size,
they put effort and money in advertising on Facebook.

Takeaways: The main benefits cited in the economics literature is that the
relative low cost of micro-targeting enables smaller businesses to access
advertising markets from which they were previously excluded. This increases
the fairness of competition between small and large-sized businesses and lead
to a more competitive market. Our analysis shows that, indeed small and
medium-sized businesses account for the majority of advertisers on Facebook
(as captured by our dataset), and they account for the majority of the ad
budget spend on Facebook (under the assumption that the price for an ad
impression is not higher in average for BEs than SMEs). Moreover, an increase
in the variety of products a user is exposed to, as well as the easiness to
find rare and niche products are also important benefits that lead to consumer
surplus. Our results show that there is a large diversity of activities
promoted on Facebook including a fraction of advertisers who are individual
entrepreneurs that promote various fringe activities.

##  5\. Targeting Characterization

This section analyzes to which extent SMEs and BEs employ micro-targeting
strategies when they send their ads on Facebook. Our tool collects both the
ads and the ad explanations given by Facebook in the “Why am I seeing this
ad?” option (Facebook, 2021f). These explanations (recall from Section 3)
contain information about the advertiser-driven micro-targeting (i.e., what
are the parameters chosen by the advertiser to target the ad). However, they
do not provide information about algorithmic-driven micro-targeting (i.e.,
what users the ad platform deems “relevant” to see the ad). Nevertheless, this
data is enough for this section as our focus is on how advertisers use the
system.

Out of the different targeting options provided to advertisers, recall from
Section 2, we consider that advertiser-driven micro-targeting happens when
advertisers are creating their audience either using attributes such as
demographics and interests or use custom audiences. If advertisers only
specify the age, gender, and location of the users they want to reach, we do
not consider it as advertiser-driven micro-targeting. Finally, we consider
lookalike audiences as a form of explicit algorithmic-based micro-targeting.

Table 2 shows for the 45k ads in our timeline-advertisers dataset for which we
collected the ad explanation how many have been targeted using: generic
targeting only, attribute-based micro-targeting, custom audience-based micro-
targeting, and lookalike audiences. The table shows usage statistics
separately for SMEs, BEs, and unmatched-advertisers. We can see that generic
targeting only is used in 72% of ads sent by SMEs, 69% of ads sent by BEs and
60% of ads sent by unmatched-advertisers. The table also shows that 12% of the
ads of SMEs, 15% of the ads of BEs, and 21% of ads of unmatched-advertisers
are attribute-based micro-targeted. Regarding custom-audience-based micro-
targeting, both SMEs, BEs and unmatched-advertisers target about 7% of their
ads using custom audiences. Hence, the majority (67%) of ads for which we have
an explanation in our dataset were not advertiser-driven micro-targeted. This
is a significant shift from a study in 2019 that found that 64% of the ads
collected were micro-targeted by advertisers (Andreou et al., 2019). This
suggests that the way advertisers are employing the Facebook ad platform is
evolving.

If we look at the 33K advertisers for which we have at least one ad
explanation, 29% of SMEs, 30% of BEs, and 46% of unmatched-advertisers micro-
targeted at least one of their ads. Hence, most of the advertisers in our
dataset do not use advertiser-driven micro-targeting. Nevertheless, the
unmatched-advertisers seem to be the ones that rely the most on advertiser-
driven micro-targeting.

Table 2. Usage frequency of different targeting options for ads send by small and medium-sized businesses (SMEs), large-sized businesses (BEs), and unmatched-advertisers. Targeting type | SME (%) | BE (%) | Unmatched (%)
---|---|---|---
Generic targeting only | 72.3 | 69.5 | 60.6
Attribute-based micro-targeting | 12.0 | 15.1 | 21.5
Custom audience-based micro-targeting | 6.9 | 6.8 | 6.6
Lookalike audiences | 8.8 | 8.6 | 11.2

Targeting based on lookalike audiences is a good example where ad platform’s
algorithmic-driven micro-targeting interferes explicitly in the micro-
targeting process. While the advertiser gives the seed audience, it is
Facebook’s algorithm who selects similar users to create the final lookalike
audience. Besides specifying the initial seed audience, advertisers have no
control over how this audience is created, and have no idea what are the
characteristics of the resulted audience. In our dataset, 9% of ads from SMEs
and BEs, and 11% of ads from unmatched-advertisers are targeted using
lookalike audiences. Hence, in our dataset, 9% of ads for which we have the
explanation are targeted using explicit algorithmic-driven micro-targeting.

#### Zoom on generic targeting

Generic audiences are defined using location, language, age, and gender. The
targeting explanation that Facebook provides with ads allows us to study
generic attributes in detail. 81.7% of ads target all gender groups, while
14.3% of the ads target women only. As for the age, an advertiser can specify
the minimum and maximum age of the targeted audience. Half of the ads target
users between 18 and 65, which is the default configuration on Facebook ads
manager; while the other half have custom configurations of age. In 92% of the
ads, the location was set to the whole country. The 8% left is distributed
over different U.S. states. There is no particular difference between
different types of advertisers regarding the use generic parameters.

#### Zoom on attribute-based micro-targeting

In the whole period of study, 12k matched-advertisers (for which we have at
least one ad with an ad explanation) targeted users using 4947 different
attributes while the 5k unmatched-advertisers targeted users with 6219
attributes. SMEs used 3082 distinct attributes to target their ads and BEs
used 2949. In median, an advertiser uses three different attributes to target
users across different ads. The top five targeted interests for SMEs are
online shopping, physical fitness, cats, shopping and fashion and amazon; for
BEs are online shopping, culture, shopping and fashion, learning and shopping;
and for unmatched-advertisers are online shopping, shopping and fashion,
physical fitness, yoga and cooking.

#### Zoom on custom audience-based micro-targeting

An advertiser can form custom audiences from different data sources: a data
file containing a list of emails, phone numbers, or users ids (e.g., contact
information collected from previous clients), website visits detected by a
hidden Facebook pixel (i.e., also traditionally known as re-targeting), users
who engaged with the advertiser’s page, video, or Instagram profile. Table
LABEL:tab:cas shows the different data sources used by advertisers to
construct custom audiences. The table shows that data files are more used by
unmatched-advertisers while the Facebook pixel is more used by BEs.

Table 3. Usage of different data sources for ads targeted using custom audiences. Data source | SME (%) | BE (%) | Unmatched (%)
---|---|---|---
Data file | 35 | 33 | 54
Website pixel | 38 | 40 | 33
Page engagement | 17 | 12 | 6
Video engagement | 7 | 9 | 4
Instagram engagement | 3 | 6 | 3

Takeaways: Our results show that only 28% small and medium-sized businesses,
30% of large-sized businesses, and 46% of advertisers without a LinkedIn
profile micro-targeted at least one of their ads. In fact, 67% of the ads in
our dataset have been targeted with only age, gender, location, and language
parameters. However, it is important to understand that these results do not
imply that micro-targeting is not useful as a marketing strategy; they rather
suggest that the advertisers prefer to outsource the micro-targeting task to
the ad platform. Recall from, Section 2 that Facebook claims to perform
algorithmic-driven micro-targeting for every ad impression. The shift away
from advertiser-driven micro-targeting could be explained by ad platforms’
algorithms getting better than advertisers at finding the right audiences to
target ads.

Previous work showed that advertisers who target broad audiences might end up
ceding platforms even more influence over which users ultimately see the ads
(Ali et al., 2019c), which indicates that algorithmic-driven micro-targeting
has a more significant role in ads targeted at large audiences. Our work shows
that most ads on Facebook use large audiences, pointing to algorithmic-driven
micro-targeting as a significant driver of delivering ads.

##  6\. The role of web tracking in micro-targeting on Facebook

Civil societies are pushing lawmakers to ban web tracking that is intended for
advertising (Lemoine et al., 2021) and big platforms such as Apple and Google
are also taking technical steps to make it harder to track users online. While
the role of web tracking in online advertising is widely acknowledged (Mayer
and Mitchell, 2012), the role of web tracking in advertising on Facebook is
less clearly understood as the platform already has a lot of information about
the behavior of users on their social network.

###  6.1. Mentions of web tracking in Facebook’s policies and help pages

To better understand the role of web tracking in micro-targeting on Facebook
we first turned to the policies and help-pages provided by Facebook:

For _attribute-based advertiser-driven micro-targeting_ , Facebook states in
their “Detailed Ad Targeting” help page that the demographics, interests and
behaviors made available to advertisers to select their audiences are based on
activity _inside_ Facebook such as their comments and the interactions with
different contents, and do not mention any outside signals coming from web
tracking (fac, 2021f):

> “Ads they click; Pages they engage with; Activities people engage in on
> Facebook related to things like their device usage, and travel preferences;
> Demographics like age, gender and location; The mobile device they use and
> the speed of their network connection.”

Hence, attribute-based advertiser-driven micro-targeting _a priori_ , does not
rely on web tracking.

When looking at _custom audiences-based advertiser-driven micro-targeting_ ,
the only option that employs web tracking is the option of targeting users
that visited the advertiser’s website, i.e., retargeting. This is done through
the Facebook pixel, a piece of code that advertisers can install on their
website and that allows Facebook to track what users are visiting on the
advertiser’s website.

We could not find any information regarding the use of web tracking in the
_lookalike audience_.

Regarding _algorithmic-driven micro-targeting_ we found two mentions of the
use of web tracking. First mention is in the “Ad principles” page that clearly
states that Facebook uses information about what websites users visits to
choose to whom to send an ad (fac, 2021d):

> “Our system can show the hair salon’s ads to the people in that area who are
> most likely to be interested in the salon. Interests are determined based on
> activity on Facebook, such as Pages or posts that people like, posts or
> comments they make and activity off Facebook from apps they use and
> _websites they visit_.”

The second mention is in the “Facebook Business Tools Terms” (i.e., the terms
a business needs to sign to use the Facebook pixel or other social plugin such
as the Like button) that mention they use information gathered from businesses
through Event Data to determine the relevance of ads to people (fac, 2021i):

> “We may correlate that Event Data to people who use Facebook Company
> Products to support the objectives of your ad campaign, improve the
> effectiveness of ad delivery models, and determine the relevance of ads to
> people. We may use Event Data to personalize the features and content
> (including ads and recommendations) that we show people on and off our
> Facebook Company Products.”

Hence, it seems that web tracking does not play a big role in advertiser-
driven micro-targeting, but web tracking is used in algorithmic-driven micro-
targeting. Since Facebook claims to perform algorithmic-driven micro-targeting
for every ad delivery, web tracking seems to be an important source of data
for micro-targeting every ad on Facebook (fac, 2021e).

###  6.2. Prevalence of the Facebook pixel among advertisers

Web tracking data can come either from websites that are implementing
Facebook’s tools such as the Like button, either from advertisers that install
a Facebook pixel. To understand the extent to which advertisers contribute to
Facebook’s data acquisition through web tracking, we investigate what fraction
of advertisers installed the Facebook pixel on their website. Facebook pixel
is a tracker that allows advertisers to track and analyze visitors’ activity
on their websites (Facebook, 2021e). When the user visits the advertiser’s
website, the pixel sends the event of “page view” to Facebook. If the user
clicks on the purchase or the buy button, the pixel generates a purchase event
(Facebook, 2021b). All the data about different events on the website (visits,
purchases, subscriptions) are sent to Facebook. The advertisers have access to
a report about those events on the Facebook Ads Manager. Advertisers can use
the data gathered from their installed Facebook pixels to target the visitors
of their website.

To estimate the fraction of businesses that have deployed a pixel on their
websites we took a random sample of 600 advertisers (61% SMEs, 39% BEs) from
our database, and we checked their websites manually for potentially installed
pixels. To see if a website uses a Facebook pixel or not, Facebook provides a
browser extension called Facebook Pixel Helper (fac, 2021a). We only took a
limited sample of 600 advertisers because we cannot fully automate checking
the pixel. When visiting a website for the first time, a user must accept
cookies and consent to the terms of the website’s privacy policy, including
accepting third-party trackers like Facebook Pixel. Accepting cookies cannot
be automated easily since each website implements consent forms (cookies
configuration) differently. Thus, we were obliged to do it manually and hence
the limitation.

Our analysis shows that 81% of SMEs and 69% of BEs have deployed one or more
pixels on their websites (19% have more than one pixel). 91% of advertisers
only track the website visits (page view) while 9% configure their pixels to
track more events like purchase.

Takeaways: Facebook mentions the use of web tracking in algorithmic-driven
micro-targeting but not in attribute-based advertiser-driven micro-targeting.
This could factor in why advertisers seem to prefer algorithmic-driven micro-
targeting over advertiser-driven micro-targeting. Furthermore, 81% of the SMEs
and 69% of the BEs we checked in our dataset are sending data about their
website visitors to Facebook. Nevertheless, only 2.6% of the ads sent by SMEs
and 2.7% of the ones sent by BEs were targeted explicitly with the Facebook
pixel to reach people that visited the advertiser’s website. This raises
questions of whether advertisers have any benefits from installing the
Facebook pixel, besides targeting people who visited their website.

##  7\. Related Works

###  7.1. Economics of online advertising

The economics literature on online advertising can be split in two categories:
(1) studies on modeling online advertising and its benefits and consequences
for users and market competition, and (2) studies about the risks brought by
highly concentrated marketing structures with only a few players that offer
online advertising services.

From a conceptual perspective, advertising can be characterized as $i$)
persuasive, altering consumer tastes; $ii$) informative, reducing the cost of
information acquisition by consumers; $iii$) complementary to the advertised
product, increasing the consumption value of a product without altering
underlying preferences (Bagwell, 2007). Online advertising fits into this
definition, and many researchers advocate that the fundamental difference
compared to offline advertising is the development of micro-targeting that
leads to a substantial reduction in the cost.

Many theoretical models have portrayed the potentially positive economic
consequences of micro-targeting development:

  1. (1)

Increasing the demand: By improving the content and efficiency of messages,
micro-targeting should lead to an increase in demand as consumers are ready to
pay more for a product that perfectly fits their preferences (Athey and Gans,
2010).

  2. (2)

Decreasing the prices of products: The relatively low cost of micro-targeting
enables smaller businesses to access advertising markets from which they were
previously excluded (Anderson, 2006). This can help in decreasing the prices
as it increases the competition between advertisers.

  3. (3)

Increasing the market competition: The micro-targeting expansion and, more
specifically, the consecutive reduction in consumer search costs should result
in consumers being better informed, which allows them to compare prices and
products more quickly and contributes to the competition between advertisers.

  4. (4)

Finding unknown products: The decrease in consumer search costs also means
that it is easier to find relatively unknown, rare, and niche products (Yang,
2013; Zhang et al., 2017).

These models rely, however, on simplified assumptions. They assume zero search
costs for consumers, whereas empirical results suggest otherwise. Structural
estimates of the cost of an extra click in the consumer search process suggest
they are more significant than assumed (Honka, 2014; De et al., 2019). This
means that consumers stop searching sooner than predicted by models that
assume search costs close to zero. Thus micro-targeting may have a smaller
beneficial impact than the theoretical models suggest as it does not lead to a
perfectly informed consumer. Moreover, privacy-focused consumers react
negatively to advertisements that are both targeted and obtrusive (Goldfarb
and Tucker, 2011a). Laboratory studies and surveys have even documented
consumer discomfort with targeted advertising (Malheiros et al., 2012; Tsai et
al., 2011; Turow et al., 2009). Finally, the consumers are facing a trade-off
between stopping searching and making up their minds with the available
information (knowing that there is maybe a better product if they keep looking
or continue to search) and endure an effort in continually searching for the
perfect fit.

Finally, the source of targeting, namely large amounts of information about
consumers, may often lead to highly concentrated market structures, as is the
case in search and social networks such as with Google and Facebook (Bergemann
and Bonatti, 2011). Overall, ad platforms are becoming subject to more
antitrust scrutiny (Goldfarb and Tucker, 2011a, b). The arguments for this
regulatory attention rely on privacy data matters and on the targeting
techniques used by the search and social networks. While online advertisements
and micro-targeting are especially valuable to smaller advertisers that do not
have easy access to offline advertising, the configuration of the algorithms
employed by ad platforms to deliver ads may substantially impact smaller
advertisers. For example, Brynjolfsson et al. (2003) showed that online
advertising might alter the fairness of competition between advertisers as it
can produce different consumer search costs according to the recommendation
algorithm or the micro-targeting strategies used. Furthermore, using data from
eBay, Dinerstein et al. (2018) emphasizes how the design of the search
algorithm on eBay affects markups charged by eBay sellers. Hence, micro-
targeting should be accessible for all advertisers in the same way to ensure
fair competition. Thus, on top of the micro-targeting risk to be weaponized
for political purposes, there is a clear call for more antitrust scrutiny
regarding the fairness of use and access of micro-targeting by advertisers,
especially small and medium-sized businesses.

###  7.2. Empirical studies on online advertising

Many works have been published in recent years about the Facebook advertising
platform. These works can be categorized into: (1) studies about the Facebook
advertising ecosystem in general (Andreou et al., 2019; Dehghani and Tumer,
2015; Brettel et al., 2015; Hutter et al., 2013; Erkan et al., 2019), (2)
studies about the targeting of political ads on Facebook (Ghosh et al., 2019;
Ali et al., 2019c; Silva et al., 2020; Sosnovik and Goga, 2021), and (3)
studies about the risks brought by online advertising (Ali et al., 2019a;
Speicher et al., 2018a; Cabañas et al., 2021).

The closest to our work is the study by Andreou et al. (Andreou et al., 2019).
In their work, they analyzed the data collected from 600 users who installed
the AdAnalyst browser extension 666https://adanalyst.mpi-sws.org, similar to
our extension. Their results describe the usage of Facebook targeting options
by different advertisers. They provided a very detailed statistical summary of
the targeted population, targeting options, and the activity of advertisers.
Some works conducted studies on the effectiveness of Facebook advertising
platform on increasing purchases (Dehghani and Tumer, 2015; Brettel et al.,
2015) and brand awareness (Hutter et al., 2013; Erkan et al., 2019).

Many recent works focused specifically on political ads: (Ghosh et al., 2019)
worked on the targeting of such ads and found that well-funded advertisers
tend to use privacy-sensitive targeting features more frequently while less-
well-funded advertisers tend to more narrowly target their audiences
geographically. Also, Ali et al. (Ali et al., 2019c) investigated the impact
of Facebook’s ad delivery algorithms on political ads. They found that these
algorithms differentiate the price of reaching a user based on their inferred
political alignment with the advertised content, inhibiting political
campaigns’ ability to reach voters with diverse political views, which leads
to political content polarization. Silva et al. (Silva et al., 2020) studied
the detection of political ads; after monitoring the political ads during the
2018 Brazilian elections, they noted that a significant fraction of political
ads they detected were not present in the Facebook political ad library, which
means that they were not declared as political. This emphasizes the importance
of enforcement mechanisms for declaring political ads and the need for
independent auditing platforms. Finally, Sosnovik et al. (Sosnovik and Goga,
2021) showed that there is a significant disagreement between ad platforms,
ordinary people, and advertisers on what is political. This indicates how
difficult it is to detect political ads and distinguish them from non-
political ones reliably.

Finally, more broadly, several works have looked at other risks with online
advertising platforms. Ribeiro et al. (Ribeiro et al., 2019) studied how the
Russian Intelligence Research Agency (IRA) has interfered in the 2016 US
presidential elections. They showed that the very efficient ad targeting done
by the Russian Intelligence Research Agency (IRA) was to a good extent enabled
by the enormous amount of personal data aggregated by Facebook on users and
made available for advertisers. Another risk of using online advertising is
discrimination by targeting only specific groups with interesting
opportunities. Ali et al. (Ali et al., 2019a) show that the market and
financial optimization of Facebook’s algorithms can lead to skewed delivery
outcomes. Also, Speicher and al. (Speicher et al., 2018a) investigate in their
work the different targeting methods offered by Facebook and showed that some
of them could be used by a malicious advertiser to create highly
discriminatory ads without specifying any sensitive attributes. This proves
that the measures taken by Facebook - disallowing the use of attributes such
as ethnic affinity from being used by advertisers - are not sufficient to stop
targeting discrimination. To limit these risks, some data protection
regulations restrict the processing of some categories of personal data
(ethnic origin, religious beliefs, etc.) due to the privacy risks associated
to such information. However, Cuevas et al. (Cabañas et al., 2021) show that
this enforcement had a negligible impact by showing that the users labeled
with such personal interest remained the same few months after the GDPR was
enacted.

While other works focus on separate sides of the platforms and sometimes
limited to an actor or a process, our study makes the link between all those
in a data analysis setup. Using data, we were able to give a clear idea of the
nature and the activity of advertisers on the platform and their targeting
practices. This is a first step towards understanding the benefits of online
advertising platforms from an economical perspective.

##  8\. Concluding Discussion

In this work, we conducted a study about advertisers on Facebook and their
targeting practices. Combining the data collected by our browser extension and
the data from LinkedIn, we could profile advertisers based on their company
size and targeting activity on Facebook. Our study included 40K advertisers
who targeted 890 U.S. users. We discovered a strong presence of small and
medium-sized businesses (SMEs) on Facebook, accounting for 71% of advertisers
in our dataset. SMEs are also responsible for 57% of ad impressions. However,
neither SMEs nor B.E.s rely heavily on micro-targeting; 67% of ads in our
dataset were targeted by advertisers using only age, gender, location, and
language attributes.

Facebook is optimizing ad delivery for every ad on top of the targeting
specified by the advertisers. One possible explanation for why advertisers
choose not to micro-target their ads to specific groups of users is that
Facebook’s algorithms are doing a better job at finding the right users to
show the ads without the input of advertisers. While most discussions until
now have focused on micro-targeting done by advertisers, our work shows that
it is equally important to give attention to micro-targeting done by ad
platforms. The current lack of transparency of algorithmic-driven micro-
targeting leads to an inability to assess the fairness and safety of this type
of micro-targeting for both users and advertisers.

Our paper also contributes to a better understanding of the role of web
tracking in Facebook’s micro-targeting options. While web tracking does not
seem to contribute a lot to advertiser-driven micro-targeting, it does seem to
play a role in algorithmic-driven micro-targeting. Our analysis showed that
the majority of the advertisers we checked, irrespective of their size, are
allowing Facebook to track users that visit their websites by installing a
Facebook pixel. This pixel is an excellent source of data for Facebook as it
gives information about the behavior of users outside Facebook and provides
precious information about what users are interested in at the current moment.

Our work does not confirm the usefulness or non-usefulness of micro-targeting
for advertisers. However, it suggests that on Facebook, the bulk of the micro-
targeting might be done by the ad platform and not by the advertisers
themselves. It is important to take this knowledge into account in future
legislation and not focus solely on micro-targeting done by advertisers. From
an economics perspective, our work also opens questions on whether
algorithmic-driven micro-targeting leads to a competitive market (e.g., is it
exposing consumers to more options and allowing them to make more
environmental-friendly choices), and what are the new risks for both users and
advertisers.

## Acknowledgements

This research was supported in part by the French National Research Agency
(ANR) through the ANR-17-CE23-0014 and the MIAI@Grenoble Alpes
ANR-19-P3IA-0003 grants and the European Union’s Horizon 2020 research and
innovation program under grant agreements No 101021377 and No 952215.

## References

  * (1)
  * pix (2021) last accessed: 28 January 2021.  _About Facebook pixel_.  https://en-gb.facebook.com/business/help/742478679120153
  * fac (2021a) last accessed: 28 January 2021a.  Chrome Web Store - Facebook Pixel Helper.  https://chrome.google.com/webstore/detail/facebook-pixel-helper/fdgfkebogiimcoedlicjlajpkdmockpc
  * DSA (2021) last accessed: 28 January 2021.  Digital Services Act.  https://ec.europa.eu/digital-single-market/en/digital-services-act-package
  * EDA (2021) last accessed: 28 January 2021.  European Democracy Action Plan.  https://ec.europa.eu/commission/presscorner/detail/en/IP_20_2250
  * fac (2021b) last accessed: 28 January 2021b.  Facebook Ad Library.  https://www.facebook.com/ads/library/
  * fac (2021c) last accessed: 28 January 2021c.  Facebook Ad Preferences - Advertisers.  https://www.facebook.com/adpreferences/advertisers
  * ads (2021) last accessed: 28 January 2021.  _Facebook Ads Manager: Ads management for Facebook, Instagram_.  https://en-gb.facebook.com/business/tools/ads-manager
  * fac (2021d) last accessed: 28 January 2021d.  Facebook Business - Ad Principles.  https://www.facebook.com/business/about/ad-principles
  * fac (2021e) last accessed: 28 January 2021e.  Facebook Business Help Center - About Ad Delivery.  https://www.facebook.com/business/help/1000688343301256?id=561906377587030
  * fac (2021f) last accessed: 28 January 2021f.  Facebook Business Help Center - About detailed Targeting.  https://www.facebook.com/business/help/182371508761821?id=176276233019487
  * adr (2021) last accessed: 28 January 2021.  Facebook Business Help Center - Facebook Ad Relevance Score.  https://www.facebook.com/business/help/436113280262012
  * fac (2021g) last accessed: 28 January 2021g.  Facebook Business Help Center - How to Create a Meta Pixel in Business Manager.  https://www.facebook.com/business/help/314143995668266?id=1205376682832142
  * fac (2021h) last accessed: 28 January 2021h.  Facebook Business News - Deliver Relevance and Discover Growth with Facebook.  https://www.facebook.com/business/news/deliver-relevance-and-discover-growth-with-facebook
  * fac (2021i) last accessed: 28 January 2021i.  Facebook Business Tools Terms.  https://www.facebook.com/legal/technology_terms
  * fac (2021j) last accessed: 28 January 2021j.  Facebook News - Why Am I Seeing This? We Have an Answer for You.  https://about.fb.com/news/2019/03/why-am-i-seeing-this/
  * obj (2021) last accessed: 28 January 2021.  _Help: Choosing the right advertising objective_.  https://en-gb.facebook.com/business/help/1438417719786914
  * pro (2021) last accessed: 28 January 2021.  Prolific Researcher Help - Study Details.  https://researcher-help.prolific.co/hc/en-gb/articles/4407456015250-Study-details
  * fb_(2021) last accessed: 28 January 2021.  Small businesses deserve to be heard.  https://www.facebook.com/business/apple-ios-14-speak-up-for-small-business
  * Ali et al. (2019a) Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019a.  Discrimination through Optimization.  _Proceedings of the ACM on Human-Computer Interaction_ 3, CSCW (Nov 2019), 1–30.  https://doi.org/10.1145/3359301
  * Ali et al. (2019b) Muhammad Ali, Piotr Sapiezynski, Miranda Bogen, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019b.  Discrimination through optimization: How Facebook’s ad delivery can lead to biased outcomes.  _Proceedings of the ACM on Human-Computer Interaction_ 3, CSCW (2019).  https://doi.org/10.1145/3359301 arXiv:1904.02095
  * Ali et al. (2019c) Muhammad Ali, Piotr Sapiezynski, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2019c.  Ad Delivery Algorithms: The Hidden Arbiters of Political Messaging.  arXiv:1912.04255 [cs.CY]
  * Ali et al. (2021) Muhammad Ali, Piotr Sapiezynski, Aleksandra Korolova, Alan Mislove, and Aaron Rieke. 2021\.  Ad Delivery Algorithms. In _Proceedings of the 14th ACM International Conference on Web Search and Data Mining_. ACM, New York, NY, USA, 13–21.  https://doi.org/10.1145/3437963.3441801 arXiv:1912.04255
  * Anderson (2006) Chris Anderson. 2006\.  _The long tail: Why the future of business is selling less of more_.  Hachette Books.
  * Andreou et al. (2019) Athanasios Andreou, Marcio Silva, Fabrício Benevenuto, Oana Goga, Patrick Loiseau, and Alan Mislove. 2019\.  Measuring the Facebook Advertising Ecosystem. In _NDSS 2019 - Proceedings of the Network and Distributed System Security Symposium_ (San Diego, United States, 2019-02). 1–15.  https://doi.org/10.14722/ndss.2019.23280
  * Andreou et al. (2018) Athanasios Andreou, Giridhari Venkatadri, Oana Goga, Krishna P. Gummadi, Patrick Loiseau, and Alan Mislove. 2018.  Investigating Ad Transparency Mechanisms in Social Media: A Case Study of Facebook’s Explanations.  _NDSS 2018 - Proceedings of the Network and Distributed System Security Symposium_ February (2018).  https://doi.org/10.14722/ndss.2018.23191
  * Athey and Gans (2010) Susan Athey and Joshua S Gans. 2010.  The impact of targeting technology on advertising markets and media competition.  _American Economic Review_ 100, 2 (2010), 608–13.
  * Bagwell (2007) Kyle Bagwell. 2007\.  The economic analysis of advertising.  _Handbook of industrial organization_ 3 (2007), 1701–1844.
  * Bergemann and Bonatti (2011) Dirk Bergemann and Alessandro Bonatti. 2011.  Targeting in advertising markets: implications for offline versus online media.  _The RAND Journal of Economics_ 42, 3 (2011), 417–443.
  * Brettel et al. (2015) Malte Brettel, Jens-Christian Reich, Jose M Gavilanes, and Tessa C Flatten. 2015.  What drives advertising success on Facebook? An advertising-effectiveness model: Measuring the effects on sales of “Likes” and other social-network stimuli.  _Journal of Advertising Research_ 55, 2 (2015), 162–175.
  * Brynjolfsson et al. (2003) Erik Brynjolfsson, Yu Hu, and Michael D Smith. 2003.  Consumer surplus in the digital economy: Estimating the value of increased product variety at online booksellers.  _Management science_ 49, 11 (2003), 1580–1596.
  * Cabañas et al. (2021) José González Cabañas, Ángel Cuevas, Aritz Arrate, and Rubén Cuevas. 2021.  Does Facebook use sensitive data for advertising purposes?  _Commun. ACM_ 64, 1 (Jan 2021), 62–69.  https://doi.org/10.1145/3426361
  * Confessore (2018) Nicholas Confessore. 2018\.  Cambridge Analytica and Facebook: The scandal and the fallout so far.  _The New York Times_ (2018).  https://www.nytimes.com/2018/04/04/us/politics/cambridge-analytica-scandal-fallout.html
  * Council (2021) Norwegian Consumer Council. 2021\.  Time to ban surveillance-based advertising.  _Forbrukerradet_ (2021).  Retrieved 28 January 2021 from https://www.forbrukerradet.no/side/new-report-details-threats-to-consumers-from-surveillance-based-advertising/
  * De et al. (2019) Abir De, Paramita Koley, Niloy Ganguly, and Manuel Gomez Rodriguez. 2019.  Regression under human assistance.  _arXiv_ (2019), 1–16.  https://doi.org/10.1609/aaai.v34i03.5645 arXiv:1909.02963
  * Dehghani and Tumer (2015) Milad Dehghani and Mustafa Tumer. 2015.  A research on effectiveness of Facebook advertising on enhancing purchase intention of consumers.  _Computers in Human Behavior_ 49 (2015), 597–600.
  * Dinerstein et al. (2018) Michael Dinerstein, Liran Einav, Jonathan Levin, and Neel Sundaresan. 2018.  Consumer price search and platform design in internet commerce.  _American Economic Review_ 108, 7 (2018), 1820–59.
  * Erkan et al. (2019) Ismail Erkan, Mehmet Gokerik, and Fulya Acikgoz. 2019\.  The impacts of facebook ads on brand image, brand awareness, and brand equity.  In _Handbook of Research on Entrepreneurship and Marketing for Global Reach in the Digital Economy_. IGI Global, 442–462.
  * Facebook (2021a) Facebook. 2021a.  _About ad relevance diagnostics_.  https://en-gb.facebook.com/business/help/403110480493160
  * Facebook (2021b) Facebook. 2021b.  _Conversion Tracking - Facebook Pixel - Documentation_.  https://developers.facebook.com/docs/facebook-pixel/implementation/conversion-tracking/
  * Facebook (2021c) Facebook. 2021c.  _Facebook advertising targeting options_.  Retrieved October 28, 2021 from https://en-gb.facebook.com/business/ads/ad-targeting
  * Facebook (2021d) Facebook. 2021d.  _Facebook advertising targeting options_.  https://en-gb.facebook.com/business/ads/ad-targeting
  * Facebook (2021e) Facebook. 2021e.  _Facebook Pixel - Documentation_.  https://developers.facebook.com/docs/facebook-pixel/
  * Facebook (2021f) Facebook. 2021f.  _Why am I seeing ads from an advertiser on Facebook? | Facebook Help Centre_.  https://www.facebook.com/help/794535777607370
  * Ghosh et al. (2019) A. Ghosh, Giridhari Venkatadri, and A. Mislove. 2019\.  Analyzing Political Advertisers’ Use of Facebook’s Targeting Features.
  * Goldfarb (2014) Avi Goldfarb. 2014\.  What is different about online advertising?  _Review of Industrial Organization_ 44, 2 (2014), 115–129.
  * Goldfarb and Tucker (2011a) Avi Goldfarb and Catherine Tucker. 2011a.  Advertising bans and the substitutability of online and offline advertising.  _Journal of Marketing Research_ 48, 2 (2011), 207–227.
  * Goldfarb and Tucker (2011b) Avi Goldfarb and Catherine Tucker. 2011b.  Online display advertising: Targeting and obtrusiveness.  _Marketing Science_ 30, 3 (2011), 389–404.
  * Goldfarb and Tucker (2019) Avi Goldfarb and Catherine Tucker. 2019.  Digital economics.  _Journal of Economic Literature_ 57, 1 (2019), 3–43.
  * Ha (2020) Anthony Ha. 2020\.  Facebook highlights small businesses as it ramps up Apple criticism.  _Techcrunch_ (2020).  Retrieved 28 January 2021 from https://techcrunch.com/2020/12/16/facebook-apple-small-business
  * Honka (2014) Elisabeth Honka. 2014\.  Quantifying search and switching costs in the US auto insurance industry.  _The RAND Journal of Economics_ 45, 4 (2014), 847–884.
  * Hutchinson (2019) Andrew Hutchinson. 2019\.  Facebook’s Updating it’s ’Why Am I Seeing This?’ Ad Info with More Specific Detail.  _Social Media Today_ (2019).  Retrieved 28 January 2021 from https://www.socialmediatoday.com/news/facebooks-updating-its-why-am-i-seeing-this-ad-info-with-more-specific/547859/
  * Hutter et al. (2013) Katja Hutter, Julia Hautz, Severin Dennhardt, and Johann Füller. 2013.  The impact of user interactions in social media on brand awareness and purchase intention: the case of MINI on Facebook.  _Journal of Product & Brand Management_ (2013).
  * Lemoine et al. (2021) Laureline Lemoine, Ella Jakubowska, Andreea Belu, and Diego Naranjo. 2021.  Targeted Online: An industry broken by design and by default.  _European Digital Rights (EDRi)_ (2021).  Retrieved 28 January 2021 from https://edri.org/wp-content/uploads/2021/03/Targeted-online-An-industry-broken-by-design-and-by-default.pdf
  * Lewandowsky et al. (2020) Stephan Lewandowsky, Laura Smillie, David Garcia, Ralph Hertwig, Jimm Weatherall, Stefanie Egidy, Ronald E. Robertson, Cailin O’Connor, Anastasia Kozyreva, Philipp Lorenz-spreen, Yannic Blaschke, and Mark Leiser. 2020.  _Technology and Democracy: Understanding the influence of online technologies on political behavior and decision-making_.  Joint Research Center Science for Policy.  https://doi.org/10.2760/709177
  * Liberto (2019) Daniel Liberto. 2019\.  _Facebook, Google Digital Ad Market Share Drops as Amazon Climbs_.  Retrieved October 28, 2021 from https://www.investopedia.com/news/facebook-google-digital-ad-market-share-drops-amazon-climbs/
  * Lomas (2020) Natasha Lomas. 2020\.  Europe to put forward rules for political ads transparency and beef up its disinformation code next year.  _TechCrunch_ (2020).  https://techcrunch.com/2020/12/03/europe-to-put-forward-rules-for-political-ads-transparency-and-beef-up-its-disinformation-code-next-year/?guccounter=1
  * Malheiros et al. (2012) Miguel Malheiros, Charlene Jennett, Snehalee Patel, Sacha Brostoff, and Martina Angela Sasse. 2012\.  Too close for comfort: A study of the effectiveness and acceptability of rich-media personalized advertising. In _Proceedings of the SIGCHI conference on human factors in computing systems_. 579–588.
  * Mayer and Mitchell (2012) Jonathan R. Mayer and John C. Mitchell. 2012.  Third-Party Web Tracking: Policy and Technology. In _2012 IEEE Symposium on Security and Privacy_. 413–427.  https://doi.org/10.1109/SP.2012.47
  * Merrill and Kozlowska (2019) Jeremy Merrill and Hanna Kozlowska. 2019.  How Facebook fueled a precious-metal scheme targeting older conservatives.  _QUARTZ_ (2019).  https://qz.com/1751030/facebook-ads-lured-seniors-into-giving-savings-to-metals-com/
  * Penn (2017) Mark Penn. 2017\.  You Can’t Buy the Presidency for $100,000.  _The Wall Street Journal_ (2017).  https://www.wsj.com/articles/you-cant-buy-the-presidency-for-100-000-1508104629
  * Ribeiro et al. (2019) Filipe N. Ribeiro, Koustuv Saha, Mahmoudreza Babaei, Lucas Henrique, Johnnatan Messias, Fabricio Benevenuto, Oana Goga, Krishna P. Gummadi, and Elissa M. Redmiles. 2019\.  On Microtargeting Socially Divisive Ads.  _Proceedings of the Conference on Fairness, Accountability, and Transparency_ (Jan 2019).  https://doi.org/10.1145/3287560.3287580
  * Shane (2017) Scott Shane. 2017\.  These Are the Ads Russia Bought on Facebook in 2016\.  _The New York Times_ 1 (2017).  https://www.nytimes.com/2017/11/01/us/politics/russia-2016-election-facebook.html
  * Silva et al. (2020) Márcio Silva, Lucas Santos de Oliveira, Athanasios Andreou, Pedro Olmo Vaz de Melo, Oana Goga, and Fabrício Benevenuto. 2020\.  Facebook Ads Monitor: An Independent Auditing System for Political Ads on Facebook.  arXiv:2001.10581 [cs.SI]
  * Sosnovik and Goga (2021) Vera Sosnovik and Oana Goga. 2021.  Understanding the Complexity of Detecting Political Ads.  _Proceedings of the Web Conference 2021_ (Apr 2021).  https://doi.org/10.1145/3442381.3450049
  * Speicher et al. (2018a) Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P Gummadi, Patrick Loiseau, and Alan Mislove. 2018a.  Potential for Discrimination in Online Targeted Advertising. In _FAT 2018 - Conference on Fairness, Accountability, and Transparency_ , Vol. 81. New-York, United States, 1–15.  https://hal.archives-ouvertes.fr/hal-01955343
  * Speicher et al. (2018b) Till Speicher, Muhammad Ali, Giridhari Venkatadri, Filipe Nunes Ribeiro, George Arvanitakis, Fabrício Benevenuto, Krishna P Gummadi, Patrick Loiseau, Alan Mislove, Sorelle A Friedler, and Christo Wilson. 2018b.  Potential for Discrimination in Online Targeted Advertising.  _Proceedings of Machine Learning Research_ 81 (2018), 1–15.
  * Statista (2021a) Statista. 2021a.  _Offline media spend in the U.S. 2021_.  Retrieved October 28, 2021 from https://www.statista.com/statistics/1028853/offline-media-ad-spend-us/
  * Statista (2021b) Statista. 2021b.  _U.S. digital advertising industry - statistics & facts_.  Retrieved October 28, 2021 from https://www.statista.com/topics/1176/online-advertising/
  * Susser et al. (2019) Daniel Susser, Beate Roessler, and Helen Nissenbaum. 2019\.  Technology, autonomy, and manipulation.  _Internet Policy Review_ 8, 2 (2019), 1–22.  https://doi.org/10.14763/2019.2.1410
  * Taiminen and Karjaluoto (2015) Heini Maarit Taiminen and Heikki Karjaluoto. 2015.  The usage of digital marketing channels in SMEs.  _Journal of Small Business and Enterprise Development_ (2015).
  * Tsai et al. (2011) Janice Y Tsai, Serge Egelman, Lorrie Cranor, and Alessandro Acquisti. 2011.  The effect of online privacy information on purchasing behavior: An experimental study.  _Information systems research_ 22, 2 (2011), 254–268.
  * Turow et al. (2009) Joseph Turow, Jennifer King, Chris Jay Hoofnagle, Amy Bleakley, and Michael Hennessy. 2009.  Americans reject tailored advertising and three activities that enable it.  _Available at SSRN 1478214_ (2009).
  * Wong (2019) Julia Carrie Wong. 2019\.  The Cambridge Analytica scandal changed the world–but it didn?t change Facebook.  _The Guardian_ 18 (2019).  https://www.theguardian.com/technology/2019/mar/17/the-cambridge-analytica-scandal-changed-the-world-but-it-didnt-change-facebook
  * Yang (2013) Huanxing Yang. 2013\.  Targeted search and the long tail effect.  _The RAND Journal of Economics_ 44, 4 (2013), 733–756.
  * Zhang et al. (2017) Feifei Zhang, Jennifer Stromer-Galley, Sikana Tanupabrungsun, Yatish Hegde, Nancy McCracken, and Jeff Hemsley. 2017.  Understanding discourse acts: Political campaign messages classification on facebook and twitter.  _Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)_ 10354 LNCS (2017), 242–247.  https://doi.org/10.1007/978-3-319-60240-0_29

##  Appendix A Appendix

###  A.1. Ad explanations

Generic targeting explanation (only age, gender and location)

Attribute-based targeting explanation

Custom-audience pixel-based targeting explanation

Figure 3. Screenshot of different ads explanations as shown to users.

###  A.2. Representativeness

Figure 4. Comparison of users ages between our set of users and Facebook
users.

Figure 5. Location of our users.