A review on Facebook's EdgeRank algorithm's impact on ideological segregation

1. Introduction
Recently, there has been much debate about Facebook’s impact on ideological segregation. The world’s
most popular social media network has been accused of becoming a designation for biased information.
Much of this debate is due to the evolution of algorithms in the information age that we live in.
Algorithms today evaluate what we want to watch, read or purchase by analyzing our previous behavior.
It started with Google’s PageRank algorithm, which was developed by Larry Page and Sergey Brin in
1996 as part of their research project at Stanford University. The algorithm was designed to rank
websites in Google’s results pages. The algorithm ranked sites on the search engine’s results page by the
amount and quality of the backlinks they each received. The more backlinks an individual site received,
the more authoritative the site appeared to Google, thus increasing its ranking. Other major tech
companies such as Amazon, Netflix, and Facebook have also developed algorithms that deliver
personalized online experiences.
The word algorithm is derived from the name of the 9th-century Persian mathematician Abu Abdullah
Muhammad ibn Musa Al-Khwarizmi (780-850 AD). By definition, an algorithm is “any procedure
involving a series of steps that is used to find the solution to a particular problem, e.g. to solve a
mathematical equation” (Chambers Dictionary).
Terms such as “Echo Chambers” and “Filter Bubbles” have recently circulated around in the media,
especially during the US elections cycle in 2016. “Echo Chambers” is a metaphorical term that relates to
the limited exposure people get to ideologically challenging information within a system. Whereas,
“Filter Bubbles” relates to people’s exposure to algorithmically selected content, as a result of their
previous behavior. Due to the personalized experiences that algorithms are delivering, there is this
growing notion that the internet is becoming a biased and non-democratic place for news and
information. There is also growing concern that these algorithms are making decisions on behalf of
human actors.
The scope of the paper will also elaborate on the factors that influence the results on the Facebook
News Feed and will further expand on the algorithm’s perceived threats in fostering ideological
segregation.
Understanding Algorithms
It’s imperative that we look at algorithms as more than mere technical objects made up of lines of code,
but as factors that play significant roles in our everyday lives. The subject of algorithms has become a
major talking point over the last few years, especially since there's a growing perception that algorithms
are influencing people's decision making. As Beer (2016) mentions “Within these notions of the
algorithm, we are likely to find broader rationalities, knowledge-making and norms – with the concept
of the algorithm holding powerful and convincing sway in how things are done or how they should be
done.” Analyzing large amounts of data in today's world is ubiquitously difficult. The biggest advantage
of living in the information technology era is that many decisions can now be made much quicker by
computers as explained by Williamson and Shmoys (2011). Understanding why algorithms are created is
necessary for the general public to know. Facebook’s EdgeRank algorithm was designed to keep users
interested with the content shown on their News Feed. Google designed its PageRank algorithm to
deliver the most relevant links with respect to users search queries. Netflix designed its algorithm to aid

2. users by recommending what to watch next based on previously watched movies. As such, different
algorithms have different purposes but they all rely on processing data to deliver the desired outcomes.
EdgeRank Algorithm
How information is organized on the News Feed is of particular importance, since users spend a
considerable amount of their time on that section of the platform. Facebook’s EdgeRank algorithm
decides what to show users on their News Feed; it fundamentally structures the flow of information on
the News Feed.
The News Feed forms a crucial part of the Facebook experience; it is the first-page users view when they
log into the site. The News Feed sits at the center of the main page and is continually being updated
with a list of stories from friends or pages users have decided to follow. Edge Rank ranks content on the
News Feed by factoring how often a user likes, comments or shares certain material, and which friends
or organizations the user engages with regularly. As Kincaid (2010) explains in detail, every piece of
content that appears on the News Feed is an “Object” such as a status update, uploaded video or
uploaded picture. Every interaction with the "Object" such as a like, comment or share creates what
Facebook calls an “Edge.” The EdgeRank algorithm determines what is shown on a user’s News Feed by
factoring different elements related to the Edges. There are at least three distinct components in
determining the rank of an Edge:
1. Affinity: If a user sends a friend lots of Facebook messages and visits their profile often, then he or
she will have a higher affinity score with that friend than someone else in their friends list that he or she
has not engaged with in a very long time
2. Weight: Each Edge has a certain weight, a comment most likely has a higher edge weight than a like
because it takes more effort from a user
3. Time: Possibly the most prominent component which relates to the freshness of the edge. The older
the edge the less valuable it becomes
Butcher (2012) explains "EdgeRank is calculated based on the multiplication of the Affinity, Weight and
Time Decay scores for each Edge”. Thus, becoming newsworthy or relevant depends on the
computation of many factors by the algorithm. Some of the known factors are mentioned whereas
some other factors remain a secret.
Butcher (2012) further explains "The higher the rank, the more likely it will be that an Object appears in
the user’s News Feed." Therefore, if an object scores high according to the algorithm, it will have a
better chance of appearing on the News Feed with regards to other objects.
Whenever a user interacts with an Edge, it increases his or her affinity with the Edge creator. The
algorithm assumes that not all your friends are created equally. Some friends will have a “higher value”
than others. The ones that are of greater value are the people with whom a user interacts with on a
regular basis or on a more personal level such as exchanging messages through the messenger.
The downside to how the algorithm functions, is that the content of the "Object" does not influence
ranking. There was much concern about the spread of fictitious news articles during the US Elections
cycle which reportedly could have influenced the outcome. Since then, Facebook announced the
algorithm would factor in when many users flag a certain post or delete it. Even though Facebook

3. censors content that includes terrorism, nudity of pornography it's had to make adjustments to filter out
misleading information.
Facebook’s EdgeRank algorithm analyses its users’ previous behaviors before it decides what to show on
their News Feeds. Facebook stores data on its millions of users and processes the information through
its algorithms. The algorithm was designed to mine through large amounts of user behavior data and
sort out the relevant content on the News Feed. The creators of the algorithm were instructed to build
software that serves a particular purpose. The creators wanted to deliver an engaging user experience
that compels users to visit the site regularly and that keeps the users on the platform for as long as
possible.
Visibility on the News Feed
The way EdgeRank operates has drawn criticism as Butcher (2012) explains “What can be seen and
heard to a large extent has become a question of software.” The content that is visible on a user’s News
Feed is a result of its EdgeRank score, the higher the score, the more likely this content will be visible on
a user’s News Feed. Therefore, the frequency in which a user engages with a friend’s content affects
how likely the user is to see his or her friend’s posts. The algorithm computes that this user has regularly
shown interest in his or her friend’s content and as such gives it a priority in the user’s News Feed.
However, if the user abstains from engaging with a certain friend’s content for a substantial period of
time, then the algorithm will filter out the friend’s content from appearing on the user’s News Feed. This
dynamic is similar to the assertion made by the French philosopher Foucault (1977) on disciplinary
power “what is specific to the disciplinary penalty is nonobservance that which does not measure up to
the rule that departs from it.”
With that Butcher (2012) states “not conforming to the rules set out by the architectural program is thus
punishable.” As such not participating on Facebook will get you punished by making you invisible.
Butcher (2012) also highlights a crucial point when she mentions “EdgeRank does not treat subjects
equally as it prioritizes some above others.” Which means that becoming visible on the News Feed is not
a given but instead it is something a user would aspire to. Visibility becomes a result of a user’s frequent
participation and interaction on the platform. A user who abstains from participating on the platform
faces the threat of invisibility. As such to have your content visible, Facebook created certain rules of
engagement that users will have to adhere to, should they want their ideas, content, and voices to be
heard on the platform.
The EdgeRank algorithm emphasizes stories that generate many comments and likes which in turn
incentivizes more users to like or comment as well. Thus, Facebook increases the engagement rates by
making it appear as if everybody is participating and communicating all the time.
These rules are especially important for businesses and organizations who want to reach out to a large
audience to promote their products or services. As a result of such rules, strategies and tactics are
utilized by brands around the world to make sure their content is visible in the News Feed and that it
reaches out to the vast majority of their audiences. The implications of this approach mean that
companies will have to produce compelling content continuously and will have to engage with their
users to be visible regularly. This approach could be seen in positive light from a consumer’s standpoint.
However, this approach can also be manipulated to promote certain political ideologies and propaganda
as we saw in the latest US elections with the spread of fake news.

4. The rules also mean that content which scores low according to the algorithm will not be visible and it
will just go unnoticed. That's problematic since this material could be very pertinent to a particular
audience but it will not receive any attention since it does not meet the requirements set by the
algorithm.
The “Filter Bubble” phenomenon
Facebook’s objective is to deliver an engaging user experience on its platform. Facebook wants users to
stay on their platform and engage with their friends, and with the pages as much as possible. Facebook
does that by showing users content similar to the kind they have previously engaged with through their
likes, comments, and shares
According to Pariser (2011), EdgeRank creates a personalized ecosystem of information within the
platform. This ecosystem is a result of the data the algorithm has gathered about the users and later
processed. This process has raised concerns that users are no longer viewed as individuals but as mere
clusters of data being served information through algorithmic computations. This ecosystem of
personalized information according to Pariser (2011) becomes problematic when it comes to news
articles. According to a Pew Research, "61 percent of millennials use Facebook as their primary source
for news about politics and government." In his book “Filter Bubble,” Pariser (2011) discusses that
instead of Facebook being a neutral platform that delivers views and opinions from both sides of the
political spectrum, it has created ideological silos within its ecosystem.
Therefore, the algorithm according to Pariser (2011), further widens the ideological divide by
automatically displaying content an individual is likely to agree with while hiding content the user
ideologically opposes. As such Pariser (2011) claims the system reinforces established prejudices and
convictions.
To explain this further Pariser (2011) gave an example in Ted Talks in 2011 by referring to the US
political system. If a user’s political orientation leans towards Liberals, he or she is most likely to engage
with content that is posted on their News Feed by friends with similar political orientation. After
interacting with the same friends over a given period, the algorithm calibrates and begins to exclusively
show the same friends’ content on the user’s News Feed. The reason this happened was because the
algorithm has gathered a substantial amount of data and noticed a particular behavioral pattern about
the user’s interaction with specific friends. Pariser (2011) ends his talk by mentioning how this
technology has created personalized information ecosystems by permitting exposure only to content
from like-minded individuals. As such, the side effect of personalization is the filtration of content
shared by people with contradicting or opposing political views from appearing on the user’s News
Feed. This byproduct of EdgeRank’s design that has led to the filtration of opposing political views on the
platform has led to the emergence of the “Filter Bubble” phenomenon, a term coined by Pariser (2011).
It’s critical to understand to what extent this phenomenon has contributed in widening the gap between
advocates of opposing sides of the political spectrum.
The Other Side of the Story
The order in which users see stories on their News Feed depends on many factors such as how often
they visit Facebook, how much interaction they do with certain friends and how often users have clicked
on certain links in the News Feed in the past. A study that was done by Bakshy, Messing, and Adamic

5. (2015) examined how 10.1 million U.S. Facebook users interacted with shared news. After factoring in all
the ranking criteria, the study showed “there is on average slightly less cross-cutting content:
conservatives see approximately 5% less cross-cutting content compared to what friends share, while
liberals see about 8% less ideologically diverse content.” That translates to the fact that the algorithm
made only a negligible impact in filtering out cross-cutting content.
As such, the real impact is not as significant as others, such as Pariser (2011) has claimed it to be in
damaging political discourse. The study also mentions that many friendships have opposing ideological
views on a user’s friends list “on average more than 20 percent of an individual’s Facebook friends who
report an ideological affiliation are from the opposing party.” That leaves significant room for exposure
and dialogue with individuals who possess opposing viewpoints. The Bakshy, Messing, and Adamic
(2015) report also shows that the makeup of our friend's list on Facebook is the most important factor in
limiting the mix of content variation observed on the platform. The sharing that is done by users on the
platform is not symmetrical “liberals tend to be connected to fewer friends who share conservative
content than conservatives.”
Another study that was done by Flaxman, Goel, and Rao (2016) highlighted that with regards to online
news consumption, 75 percent of online traffic is direct traffic towards mainstream news outlets. Which
means that the vast majority of online news consumption is done by users directly visiting the home
pages of their favorite mainstream news organizations. Online news consumption mimics users’ offline
consumption habits. Therefore algorithmic technology plays a little role in ideological segregation. The
study also showed that social media sites expose users to more variety of news articles across the
political divide.
Analysis
According to the latest data from Statista, Facebook has around 1.79 billion monthly active users and
according to a Pew Research report, two-thirds of Facebook users get their news on the site. The
platform’s user base has been steadily growing since its inception, and there’s no sign of it slowing
down. It’s the most popular social media platform on the planet and has become a major source of news
content.
As such, Facebook has become a crucial player in the consumption of news and political opinions.
Technological advancements such as algorithms have raised concerns about their impact on online
segregation. We have seen reports with opposite views regarding the severity in which Facebook’s
algorithm is widening the political and ideological divide. Facebook’s EdgeRank algorithm was created to
deliver a highly engaging experience for its users on the platform. It was devised to show users more of
what they have been interacting with to increase engagement rates and dwell times on the platform.
Spending more time on the platform and engaging with content is a lucrative business for Facebook. As
any large multinational corporation, Facebook wants to increase its profit margins and satisfy its
shareholders. For Facebook, the more time users spend on the platform, the likelier they are to view ads
and engage with advertisers. In other words, more time devoted to the platform equates to higher
profit generated for the company.
At the end of the day, Facebook doesn’t charge users to use its platform, and it been forced to come up
with alternate ways to finance its operations and satisfy shareholders.

6. The side effects of their algorithm have been the subject of much debate. There’s been much talk about
the “Filter Bubble” and its impact in deepening the political divide amongst people. However, there’s
been empirical research that suggests these claims are being exaggerated and that their impact isn’t as
far reaching as reported. A study done by Bakshy, Messing, and Adamic (2015) suggested: “that the
power to expose oneself to perspectives from the other side in social media lies first and foremost with
individuals.” The report highlighted that the biggest factor influencing the nature of the political content
on a user’s News Feed is the political makeup of the user’s friends list and not the algorithm.
To illustrate further, let us assume a user regularly interacts with content a friend on his list posts on
Facebook; which could be because he or she finds the content compelling or relevant enough to his or
her interests or field of work. Let us also assume the same friend adheres to an ideology of an opposing
political party. During the elections cycle, he will be subjected to the political content he or she shares
by virtue of how the EdgeRank algorithm works. As such, the user will be exposed to a political view
other than his or her own on the News Feed. Also on Facebook, users might also click on a link posted by
a friend with an opposing political view just out of curiously, and they might disregard it completely or
they might just even read the summary without interacting with the post. Whichever route they decide
to take is irrelevant, since the fact that they have been subjected to political content that opposes their
own convictions weakens the notion that Facebook is fostering political segregation. The study done by
Goel, Mason, and Watts (2010) showed that a substantial amount of ties on social networks are with
individuals with opposing political views, thus increasing the chances of diverse content discovery.
When looking at these findings, the results suggest that social media networks have little impact on
ideological segregation. Such findings mitigate the extent of the impact that algorithms are said to have
on deepening the political divide and fostering unhealthy political discourse. As such, Facebook has not
altered the way online news was getting consumed to a degree many have feared or hoped it would
have. The notion that algorithms are contributing to more extreme attitudes and leading to
misperception of facts about current events has been exaggerated by media pundits to serve their
agenda. Also, the issue with the "Filter Bubble" metaphor is that it assumes people are completely
isolated from different perceptions. People still refer to other sources for news and information. People
watch the news on TV, read the news online and engage in discussions with friends who have opposite
views. As such it is impossible to be alienated from opposing opinion in today’s world due to the
plethora of news sources a person is subjected to.
Finally, research has shown that the majority of news related content consumed online is the result of
direct traffic to the news outlets. People with liberal political views would target liberal news outlets and
the same applies to the conservatives. Research has also shown that the makeup of a user's friend's list
has the biggest impact on the diversity of political content visible on his or her News Feed etc. In
conclusion, the findings are contrary to what Pariser (2011) claimed about Facebook's significant role in
ideological segregation. Empirical research showed Facebook has negligible impact in fostering political
polarization,
Conclusion:
Facebook has revolutionized the way people communicate with each other and the way they share their
ideas. Public debate about how social algorithms are influencing lives is very healthy and should
continue to take place. That is how the general public becomes more informed and makes better
decisions. People must come to terms with the idea that algorithms are influencing their decision

7. making in almost every major aspect of our lives. These technological objects are still in their beginning
stages, and they are only going to evolve and get better over the years. The explosion of data in the
digital age has cemented the importance of algorithms in managing and processing large amounts of
data. Today people are connected through social media in unprecedented numbers, 1.18 billion people
log onto Facebook daily and there are about 300 million photo uploads per day. The scale in which data
is being shared across the platform from all over the globe is only growing year on year.
The best way to understand algorithms and their functionality is to carefully and continuously examine
them as they evolve. Public scrutiny is particularly important to keep the technology in check and to
make sure it serves the public’s best interest first and foremost. Ultimately the technology’s purposes
are to make lives easier and decision making quicker. The impact of social algorithms is still being
researched and understood, and it is still not very clear whether Facebook has contributed or hindered
political discussion relative to a pre-Facebook world. As such, there should be more empirical research
done to understand the degree to which Facebook impacts ideological segregation.
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