Algorithmic Microtargeting?

.title[
# Algorithmic Microtargeting?
]
.subtitle[
## How ad delivery algorithms influence distribution of political ads
]
.author[
### <b>Fabio Votta</b>, Tom Dobber, Natali Helberger, and Claes de Vreese <i>(University of Amsterdam)</i>, Benjamin Guinaudeau <i>(University of Konstanz)</i>
]
.date[
### favstats.github.io/algomicro/epsa2023 (Slides) <br> <svg viewBox="0 0 512 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:blue;" xmlns="http://www.w3.org/2000/svg"> <path d="M459.37 151.716c.325 4.548.325 9.097.325 13.645 0 138.72-105.583 298.558-298.558 298.558-59.452 0-114.68-17.219-161.137-47.106 8.447.974 16.568 1.299 25.34 1.299 49.055 0 94.213-16.568 130.274-44.832-46.132-.975-84.792-31.188-98.112-72.772 6.498.974 12.995 1.624 19.818 1.624 9.421 0 18.843-1.3 27.614-3.573-48.081-9.747-84.143-51.98-84.143-102.985v-1.299c13.969 7.797 30.214 12.67 47.431 13.319-28.264-18.843-46.781-51.005-46.781-87.391 0-19.492 5.197-37.36 14.294-52.954 51.655 63.675 129.3 105.258 216.365 109.807-1.624-7.797-2.599-15.918-2.599-24.04 0-57.828 46.782-104.934 104.934-104.934 30.213 0 57.502 12.67 76.67 33.137 23.715-4.548 46.456-13.32 66.599-25.34-7.798 24.366-24.366 44.833-46.132 57.827 21.117-2.273 41.584-8.122 60.426-16.243-14.292 20.791-32.161 39.308-52.628 54.253z"></path></svg> <span class="citation">@favstats</span> <br> <svg viewBox="0 0 448 512" style="height:1em;position:relative;display:inline-block;top:.1em;fill:#615ff7;" xmlns="http://www.w3.org/2000/svg"> <path d="M433 179.11c0-97.2-63.71-125.7-63.71-125.7-62.52-28.7-228.56-28.4-290.48 0 0 0-63.72 28.5-63.72 125.7 0 115.7-6.6 259.4 105.63 289.1 40.51 10.7 75.32 13 103.33 11.4 50.81-2.8 79.32-18.1 79.32-18.1l-1.7-36.9s-36.31 11.4-77.12 10.1c-40.41-1.4-83-4.4-89.63-54a102.54 102.54 0 0 1-.9-13.9c85.63 20.9 158.65 9.1 178.75 6.7 56.12-6.7 105-41.3 111.23-72.9 9.8-49.8 9-121.5 9-121.5zm-75.12 125.2h-46.63v-114.2c0-49.7-64-51.6-64 6.9v62.5h-46.33V197c0-58.5-64-56.6-64-6.9v114.2H90.19c0-122.1-5.2-147.9 18.41-175 25.9-28.9 79.82-30.8 103.83 6.1l11.6 19.5 11.6-19.5c24.11-37.1 78.12-34.8 103.83-6.1 23.71 27.3 18.4 53 18.4 175z"></path></svg> <span class="citation">@favstats</span><span class="citation">@fosstodon.org</span>                                                                                 24th June 2023 - EPSA 2023
]

---

---

### Online Political Microtargeting of Political Ads - the "bad actors"-story
 
 
.pull-left[
<img src="img/cambridge_analytica.png" width="100%">

]

---

### Online Political Microtargeting of Political Ads - the "bad actors"-story
 
 
.pull-left[
<img src="img/cambridge_analytica.png" width="100%">

*The explicit assumption here that advertisers typically have strong control over who sees which ad*

] 
  
  
--

**But there is more than *just* targeting criteria that decides who sees political ads:**

+ advertisers can set targeting *boundaries*

+ *ad delivery algorithms* "decide" which individual users get ads from which advertiser

]

---

<img src="img/plantuml00.png" width="80%">

---

<img src="img/plantuml01.png" width="80%">

---

---

---

<img src="img/plantuml05.png" width="80%">

---

### Who decides who sees which ad on Meta?

+ **Ad auctions** = an auction takes place that determines which ad by whom is shown

---

### Who decides who sees which ad on Meta?

+ **Relevance** = how relevant is the ad to the user

[(Meta Business Help Center, 2022)](https://www.facebook.com/business/help/430291176997542)

---

### Who decides who sees which ad on Meta?

+ **Ad auctions** = an auction takes place that determines which ad by whom is shown: based on *budget*

+ **Relevance** = how relevant is the ad to the user

##### *Ad delivery algorithms* finding *relevant* audiences for ads: we term this **algorithmic microtargeting**

---

### Who decides who sees which ad on Meta?

When targeting the same audience, at the same time, with the same budget:

+ Ad delivery is heavily skewed along gendered and racial stereotypes
  + even without the intent of the advertiser [(Ali et al., 2020)](https://dl.acm.org/doi/10.1145/3359301)

Regarding political ads [(Ali et al., 2021)](https://dl.acm.org/doi/pdf/10.1145/3437963.3441801):

+ **Skewed delivery**

+ Political ads more often delivered to ideologically congruent audience 
      + Bernie ads → higher % D; 
      + Trump ads → higher % R

+ **Increased cost**

+ Liberal ad to a liberal audience: *21 Dollar per 1000 users*; 
  + Conservative ad delivered to liberal audience: *40 Dollar per 1000 users*.

---

## Research Question

### How does the Meta ad delivery algorithm<br>influence the pricing & distribution of political ads<br>in the Netherlands?

---

# Research Design

---

### Research Design

+ Algorithm audit study

+ Place the same ads targeting the same audiences (9 different ones)

+ Collaborate with Dutch parties to place political ads

+ Final collaboration with 3:

1. GroenLinks (Green party)
  2. VVD (centre-right party of PM Rutte)
  3. PvdA (social democrats)

+ Place ads before nationwide local elections on March 16th 2022
  + 1st to 7th February 2022

+ Spend 2 Euros a day on 45 ad copies

+ in total: 630 Euro per party
  
  
--
  
+ Pre-registered research design and hypotheses

---

### Dependent Variables

+ Price per 1k users reached

+ this measure is an industry standard

+ Ad delivery share

+ Audience shares measured by % of ads delivered to specific group
  
  + "How many people were reached in Audience A vs. Audience B"
  
  + Pairwise setup which allows us to observe presence and absence of target audience
  
     + e.g. ~60% of audience was interested in politics vs. 40% was not

---

### Within- and between-party comparisons

The study design incorporates two *levels of comparisons*

<br>

Observing differences of **pricing and delivery**:

1. *between parties*
  
    + i.e. we expect pricing and delivery to differ by party

1. *within a party*
  
    + i.e. we expect pricing and delivery to differ by audience

---

### Ad Relevance

We theorize two different levels of (predicted) relevance:

1. Relevant audience for party (i.e. source of ad)

+ Ads from an environmentalist party more likely to be relevant for audience interested in environmentalism.

<br>

<ol start="2">
  <li>Relevant audience for ad content (e.g. political message)</li>
</ol>

+ Political message likely to be relevant for people interested in politics

---

### Hypotheses

![](img/relevant_quote.png)

[(Meta Business Help Center, 2022)](https://www.facebook.com/business/help/430291176997542)

> **H1:** **The more relevant** an audience is for an ad, **the cheaper is the cost** for reaching 1000 users in that audience.

> **H2:** **The more relevant** an audience is for an ad, **the more are ads delivered** to that audience.

We expect that ads by party with a greater share of supporters are less expensive (H3a) and reach more people (H3b)

> **H3a:** Parties with a greater share of supporters pay less for reaching 1000 users.

> **H3b:** Parties with a greater share of supporters reach more people than smaller parties.

---

### Targeting criteria (Sub-hypotheses for H1 & H2)

We used 9 different (paired) targeting criteria for our advertisements

1. Political interests
2. Excluding political interest

<ol start="3">
  <li>Higher educated audience</li>
  <li>Lower educated audience</li>
</ol>

]

**Relevant audiences for ad content**

> Targeting political ads to **politically interested** and **higher-educated** audiences

> *is less expensive*

> *deliver more*

> than targeting politically uninterested and lower-educated audiences.

]

---

### Targeting criteria (Sub-hypotheses for H1 & H2)

We used 9 different (paired) targeting criteria for our advertisements

1. Political interests
2. Excluding political interest
  
<ol start="3">
  <li>Higher educated audience</li>
  <li>Lower educated audience</li>
</ol>

<ol start="5">
  <li>Environmental interests</li>
  <li>Excluding environmental interests</li>
</ol>

<ol start="7">
  <li>Economic interests</li>
  <li>Excluding Economic interests</li>
</ol>

]

**Relevant audience for party**

> Targeting political ads to issues that party has issue ownership over

>  *is less expensive*

>  *delivers more*

compared to other parties

]

---

### Targeting criteria (Sub-hypotheses for H1 & H2)

We used 9 different (paired) targeting criteria for our advertisements

1. Political interests
2. Excluding political interest

<ol start="3">
  <li>Higher educated audience</li>
  <li>Lower educated audience</li>
</ol>

<ol start="5">
  <li>Environmental interests</li>
  <li>Excluding environmental interests</li>
</ol>

<ol start="7">
  <li>Economic interests</li>
  <li>Excluding Economic interests</li>
</ol>
  
<ol start="9">
  <li>No Targeting</li>
</ol>

]

---

# Ad Creative and Setup

---

## How the ad looked like on Desktop

---

## How the ad looked like on Desktop

---

## Results

---

### Between-party differences

`\(\rightarrow\)` we consistently find one party that pays less and reaches more people

---

#### Between-party differences (per individual ad)

.font80[PvdA pays the least (**10-12 cents less** or: 9-11%) & reaches more people (~**1.1 - 1.3k more** per ad)]

```
## # A tibble: 15 × 5
##    party      reach share targeting        relevance
##    <chr>      <dbl> <dbl> <chr>                <dbl>
##  1 PvdA       13138  52.1 Higher Education         1
##  2 PvdA       12917  51.7 Higher Education         1
##  3 GroenLinks 11938  51.7 Higher Education         2
##  4 VVD        11528  51.6 Higher Education         1
##  5 VVD        11845  51.6 Higher Education         1
##  6 GroenLinks 11622  51.6 Higher Education         2
##  7 PvdA       12860  51.6 Higher Education         1
##  8 GroenLinks 11727  51.4 Higher Education         2
##  9 PvdA       12729  51.1 Higher Education         1
## 10 GroenLinks 11486  51.1 Higher Education         2
## 11 VVD        11388  51.0 Higher Education         1
## 12 PvdA       12632  50.9 Higher Education         1
## 13 GroenLinks 11509  50.9 Higher Education         2
## 14 VVD        11344  50.8 Higher Education         1
## 15 VVD        11260  50.6 Higher Education         1
```

]

]

---

#### Between-party differences (per target audience)

---

.font80[*Exclude economic interests* or *target environmental interests*:<br>VVD reaches fewer people per ad than GL (**~380-450 fewer people**) and pay more (**4-5 cents more**)]

]

---

### Within-party differences

---

### Within-party differences

Let's compare now:

Ad price and ad delivery share of

+ **higher-educated** vs. **lower-educated** audience

+ Audience **interested in the economy** vs. **not interested**

+ Audience **interested in politics** vs. **not interested**

+ Audience **interested in the environment** vs. **not interested**

---

### Within-party differences - Price per 1k

Ads **cost less for**:

+ *higher-educated* vs. *lower-educated audience*

Ad price **does not statistically differ for**:

+ Audience *interested in the economy* vs. *not interested*

+ Audience *interested in politics* vs. *not interested*

Ads **cost more for**:

+ Audience *interested in the environment* vs. *not interested*

]

![](img/diffs1.png)

]

---

**18-24 year olds and women are reached less (and cost more to reach)**

![](img/priceshare.png)

---

## Summary

---

### Summary

Our findings do not always align with expectations.

H1: More "relevant" audiences were not always cheaper

H2: More "relevant" audiences were not always reached more

H3: Party with greatest audience did not reach more or get cheaper prices

**However:**

> We **still** find that Meta ad delivery algorithm prioritizes certain parties and audiences for political advertising

1. PvdA pays least and reach most
2. Lower-educated, people interested in environment, women and younger people more expensive to reach

---

### Implications

+ Unequal playing field

+ Meta (dis-)advantages certain parties

+  the findings presented in this paper show that political parties were not charged the same price for the same service
  
--

+ Potential for deepening political, social and geographical inequalities

+ Some groups of people and regions are **systematically** less likely to receive political advertisements and more expensive to reach

+ isolating these groups from receiving election-related information

+ Little to no transparency by Meta about these systematic biases

+ difficult to research and make visible instances of unequal treatment and price discrimination
  + highlighting importance of access to data
  
--
  
+ Simply "banning" microtargeting would be inadequate

+ more power to the black box algorithm
  
---

## Thank you for your attention!

Link to presentation: *favstats.github.io/algomicro/epsa2023*

![](https://c.tenor.com/Q9qk5zN5EesAAAAM/space-kitten.gif)
![](https://c.tenor.com/Q9qk5zN5EesAAAAM/space-kitten.gif)

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![](https://c.tenor.com/Q9qk5zN5EesAAAAM/space-kitten.gif)
![](https://c.tenor.com/Q9qk5zN5EesAAAAM/space-kitten.gif)

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---

## Appendix

---

### Within-party differences

Reach and cost **over time**

Potential *market shock* on February 4th?

---

### Within-party differences per day - Reach and Cost

---

### Within-party differences

Reach and cost **over time** and **per party**

`\(\rightarrow\)` party differences remain constant despite *"shock"*

---

## Price differences per day

We observe:

*Consistent results*

+ "Market shock" **hits all parties equally**

+ Environment audience consistently *more expensive*

+ Higher educated audience consistently *less expensive*

*Inconsistent results*

+ Audiences interested in economy & politics are typically cheaper except on the day of the spike

]

---

### Price differences per day

---

### Bulk Discount?

]

]

---

## Skewed delivery

in terms of gender, age and region

---

## Differences in delivery by gender

.pull-left[
+ *Line at zero* shows empirical equilibrium of target audiences (i.e. the observed share of men and women in target audience)

+ *Deviation from zero* are algorithmic biases

+ above zero: prioritization
  
  + below zero: de-prioritization

+ Ads *deliver to more men* for every party

+ However: bias towards men seems smaller for GroenLinks
]

---

## Differences in delivery by age group

+ Ads *deliver less to young people*

+ aged 18-24

+ Consistent for each party

]

---

## Region differences

+ Ads deliver more to some regions

+ for example: Limburg, Friesland, Drenthe

+ Ads deliver less to other regions

+ Utrecht, North Holland, North Brabant

+ Consistent for each party

]

---

.font80[If we exclude economic interests/target environmental interests: VVD reaches less people and cheaper than GL]

]