We’re excited to talk about Search Atlas: Visualizing Divergent Search Results Across
Geopolitical Borders.
The goal of our project is to question the politics of technological infrastructures
by showing what happens when you search across borders.
This is a quick preview of our tool, which shows a user searching for the query “god”
in three different locations, showing divergent results.
Our tool highlights the information that the user would have missed.
It highlights the most distinctive words in the results for each location and language
pair.
The more often a word appears in each list of results, the stronger its color as a mixture
of red, green, and blue.
Search engines like Google claim to know everything.
For example, Google claims on its website that it contains “more info than in all
the world’s libraries put together.”
Yandex’s search bar simply says that it “finds everything.”
This impression is only reinforced by the minimal design of these search engines'
interfaces, which omit their “partial perspective,” that is, the combination of choices that inevitably
exclude some points of view in favor of others.
Search results are products not only of the users’ own interests but also of complex
struggles among the state, corporations, and academia.
Search algorithms encode the cultural assumptions and performance metrics of the designers and
engineers, which in turn are shaped by corporate profit motives, state regulations, and academic
methods and theories.
For a particular query, many search engines tailor results according to geolocation, language,
and other user profiling.
In this sense, the internet is full of “information borders” that users cannot easily cross.
To enable users to see and cross these borders, we present a critical intervention in the
design of search interfaces.
Our project consists of three parts:
A tool that enables users to search for any query in any three Google-supported countries
with accompanying languages, returning Google’s text and image results for each set of parameters.
A collection of image maps that show the image results for selected queries in almost every
Google-supported country.
A collection of cluster maps that reveal “information regions” and “information borders” in
text results for selected queries worldwide.
Our design work is guided by critical scholarship on how technological infrastructures concentrate
power and how they play a key role in the spread of false information and racist and
sexist speech.
We are also inspired by several lines of work that incorporate critical concerns into artistic
and technical interventions.
Overall, our work aims to “study up”: to appropriate the tools of the powerful,
which are typically deployed against more vulnerable groups, to instead hold the powerful
to account.
We “study up” by applying standard data analysis techniques employed by search engines
(like tf-idf), as well as cutting-edge visualization techniques (like UMAP) to search engine results.
Our goal is to open up the search engine to critical interrogation.
Mainstream search interfaces like Google’s make the design assumption of a single language
and a single location per user.
Our interface questions this assumption by encoding values of cultural and linguistic
multiplicity.
Every time you’re searching, you have to think about the places you’re searching
from, as well as those that you’re not.
For example, Google only shows a monotheistic Christian god to US English users, whereas
our interface shows the Christian god, Arabic renditions of Allah, and various Buddhas depending
on the location.
Now, we’ll show you the results for sample queries that reveal provocative differences
between locations and languages.
Our first example involves Crimea, the site of a territorial dispute between Ukraine and
Russia.
These are the results for a search for “Crimean annexation” in Russia, Ukraine, and the
Netherlands.
In Russia, the results frame the issue in terms of whether Crimea belongs to the “Russian
Federation.”
In Ukraine, they frame the issue as an “occupation.”
In the Netherlands, they focus on the European Union’s sanctions on Russia.
Our tool also supports image search.
Continuing with a search about a politically disputed topic, these are the results for
a search for “Tiananmen Square” in the United Kingdom, Singapore, and China.
In the United Kingdom and in Singapore, the results surface photographs of tanks and soldiers
in the 1989 protests, which were widely circulated in the international press.
In mainland China, they present touristic and promotional images.
(Google is blocked by the Chinese government, but it is possible to circumvent the block
and use Google with China as the country setting.)
Examining the results for one query in a few carefully chosen locations gave us an incisive
picture of just how different the results can be on the ground.
The next step is to widen the field of view.
For example, we could do an image search in each country and overlay the result over its
geographic location.
Here, we do it for “god.”
These are the top image results for translations of “god” in the default languages of most
Google-supported countries.
To determine the default languages, we scrape the google.com homepage with each possible
country parameter and detect the language parameter set as the default for each.
It is important to note that Google tends to select state-sanctioned and colonial languages
as the default for a country.
Looking at this image map, some patterns align with what one might expect.
Christian-majority countries tend to show images for a Christian god, while Muslim-majority
countries tend to show images for Allah (in written Arabic).
We also see Buddhist, Hindu, Shinto, and other kinds of imagery.
But other patterns are more surprising.
For example, countries as far apart as Bhutan and Mauritania lie in the same “information
region”: searchers in both countries would find similar images of a Western god looking
at a kneeling Jesus.
On the other hand, countries as close as Egypt and Sudan lie in different information regions:
searchers in Sudan would find the same Christian image, whereas searchers in Egypt would find
a calligraphic representation of Allah.
Regardless of the underlying causes, there is some kind of “information border” between
these regions.
Such borders are shaped and reinforced by the search engine.
We can identify these borders automatically.
We can detect clusters of countries based on the similarity of their text results.
This is what we did for a search about climate change, which is also a geopolitically important
topic.
We machine translate the query “how to combat climate change” into the Google-determined
default language for each country, then translate the results back into English.
We measure the similarity of the results using tf-idf and generate the visualization using
UMAP.
These are worldwide results for searches for “how to combat climate change.”
Each country is colored according to its continent, as indicated by the map and legend on the
right.
The information borders of climate change seem to be defined largely along island versus
continental lines.
In the cluster comprising high-income countries in continental Europe, such as Germany, Liechtenstein,
and Luxembourg, the top words suggested preemptive measures on “climate protection,” such
as “protection” and “sensible.”
Yet, the top words in a few island countries that form part of another cluster, ranging
from Mauritius in the Indian Ocean to Trinidad and Tobago in the Caribbean Sea, suggested
much greater immediate threats.
Top words included “vital,” “signs,” “harmful,” “vulnerability,” “enormity,”
“daunting,” and “dispiriting.”
How to interpret those new information regions and borders?
The underlying infrastructure offers few answers.
To return results, Google Search relies heavily on novel deep learning systems whose decisions
are notoriously difficult to interpret, even by Google’s own researchers.
Moreover, our results are limited by the difficulty of comparing results across geopolitical borders.
Our results may be influenced by confounding factors related to language, which are hard
to account for in this kind of global investigation, or by mediating factors such as Google’s
machine translation and the UMAP algorithm.
We’ve noticed two common temptations in interpreting the results of the tool.
One is to interpret the results as straightforward reflections of cultural differences among
users in different countries.
Another is to interpret the results as unambiguous outcomes of political bias or manipulation
by algorithm designers.
We encourage our readers to resist both of these temptations.
Our results underscore how search engines are products both of cultural patterns and
of algorithm design choices.
Ultimately, Search Atlas asks how information borders can be exposed & reshaped in more
democratic ways.
Thank you.
To learn more about our work, please visit searchatlas.org.