The problem 🤬

The problem lies in particular with the major online platforms’ recommendation systems. Based on algorithmic systems, these sort through the content on the platforms and select what is displayed to users.

This takes place on the basis of specific criteria. The primary criterion underlying most platform recommendation systems is to maximize users’ interaction with the content. The algorithms calculate the probability that a piece of content will be interacted with, and make their recommendations on this basis.

The critical factor here is not the quality of the content, but that the piece of content is clicked on, liked, shared or commented on as much as possible.

The reason behind this is simple: If users spend more time on the platform, this generates more advertising revenue.

This leads to a problem. Psychologically, people tend to be especially alert when they perceive danger or suspect the presence of a threat – regardless of the truth of these perceptions. Divisive, hostile or sensationalist content has an easier time finding broad distribution in an environment where recommendation systems are designed to maximize interaction. Rational, constructive or deliberative content is left by the wayside.

Since platforms today play such a dominant role in our societal discourse, journalists, politicians and influencers are ultimately pushed into producing content that aligns with recommendation systems’ criteria – simply in order to gain reach and attention.
@DailyLoud

An active shooter has fired “hundreds of rounds” near Pittsburgh's UPMC children's hospital Wednesday morning, taking down at least two police drones as authorities urged residents to stay away from the “extremely active” scene.

@freemonotheist

I think breastfeeding should be done privately – not in public – if the woman’s private parts are exposed. We need to consider the effect it will have on other people, not just the mother. Decency matters.

@PopBase

Apple warns iPhone users about sleeping next to their phone while it’s charging, saying it could result in “fire, electric shock, injury, or damage to iPhone or other property.”

@Markus Krall

If masks, tests, or vaccination passes are required again for flying or taking the train, I suggest the following form of protest: Everyone buys the cheapest flight ticket they can find, regardless of the destination, even if they have no intention of going there. Then off to the airport or train station and, if they don’t let you on board, sit in the hallway and let yourself be carried away while singing dirty songs. If we do this by the thousands, or even better by the tens of thousands, the store will collapse. Even better than glue. #CivilDisobedience

👏❤️🔥👎🥳🙄🔥👏😩😆🤨🚀

The alternative 🤝

But there is another way. Platform recommendation systems don’t have to be designed only to maximize interaction with content. In the same way, recommendation systems could also give preference to other content. This means that instead of favoring particularly provocative or sensational content, the system would instead have a balancing effect that promotes constructive debate.

To achieve this, the algorithms would have to be designed so as to take additional criteria into account when recommending content, for example the probability that different groups agree with the content. and bridging algorithms work according to this principle, promoting mutual understanding and productive debate.

The ”Polis” principle

One such approach is being pursued by “”, a project of The Computational Democracy Project, a U.S.-based non-governmental organization. This initiative focuses on visualizing opinion groups in discussions to better identify major areas of agreement and disagreement across different groups.

Here’s how it works in practice:

1

A so-called conversation is started on a specific topic, with selected people allowed to participate. One example might be the following topic:

Car-free city centers in Germany

Cars and other motor vehicles are everywhere in many large cities in Germany. Given the rising use of personal transport, this leads to traffic jams, accidents and overcrowded city centers.

2

Moderators and/or participants write various statements on this topic, each expressing a point of view. Participants can then vote on these statements.

Nico C.

Commuters are dependent on their cars to get into the city centers.

Rate this statement

3

” then identifies the statements that are oriented toward the development of agreement between groups with otherwise different opinions. This means the software provides the highest ranking to posts that draw the most agreement from the greatest number of contributors who have differing opinions on other posts. This is also called .

Nico C.

Commuters are dependent on their cars to get into the city centers.

Claudia A.

Only emissions-free cars should be allowed to drive in the city center.

Marie-Anne B.

Delivery traffic, buses, ambulances and other public vehicles should continue to be allowed.

Toby G.

If city centers are made car-free, there should be good alternatives: more buses and public transportation, better cycle paths, and sufficient parking outside the center.

Ellen L.

Residents should still be able to drive to their homes by car and park there.

Laura B.

In the city center, drivers with their tricked-out SUVs are only interested in showing off anyway.

Max M.

Now the Greens want to ban us from driving, too.

Ana L.

A ban on car traffic in the city center would be a restriction on personal freedom.

Günther G.

The lack of parking options in city centers is detrimental to local businesses.

Robin S.

Car-free city centers lead to better air quality and less noise.

4

With algorithmic recommendation systems geared toward maximizing interaction – as is currently the case with many online platforms – the result after the so-called would look different:

bridging-based

Marie-Anne B.

Delivery traffic, buses, ambulances and other public vehicles should continue to be allowed.

Ellen L.

Residents should still be able to drive to their homes by car and park there.

Robin S.

Car-free city centers lead to better air quality and less noise.

Toby G.

If city centers are made car-free, there should be good alternatives: more buses and public transportation, better cycle paths, and sufficient parking outside the center.

Claudia A.

Only emissions-free cars should be allowed to drive in the city center.

However, are not equally effective in all situations.

They work particularly well with open-ended questions, and those which allow for a variety of responses.

They are less suitable for questions intended to evoke simple yes or no answers, or that require a ranking or prioritization of responses.

It is important that the questions or topics posed offer space for constructive conflict. In other words, participants must genuinely engage with and be interested in the perspectives of the opposing side. Topics that feature already strongly entrenched positions are less suitable.

Case studies ⚙️

Building mutual understanding and trust across divides by using works in practice as well as in theory. Bridging-based ranking systems have already been used in the following examples.

vTaiwan

Polis

In 2015, as part of a broader democratic process, the vTaiwan platform used the “Polis” system as a means of surveying the public’s opinion on the market launch of the UberX ride-hailing service.

Twitter/X

Community notes

With its “Community Notes” feature, X (formerly Twitter) has introduced a function that allows users to collaboratively add context to potentially misleading posts.

Outlook 🔍

Polarization and fragmentation within our digital discourse are serious problems.

Large online platforms are required to take more targeted action against criminal content, given the increase in political pressure and legal requirements such as the EU’s . However, it is also crucial to shift the focus towards the design and business model of the platforms, aiming to address not only the symptoms but more emphatically tackle the root of the problem.

The research on bridging algorithms has revealed the following insights:

1

Platforms use their recommendation algorithms to encourage the highest possible level of interaction.

They do this in order to increase the time users spend on their sites, thus increasing advertising revenue. But they could also design their algorithms differently, to do more to improve digital discourse. One possibility is the use of bridging algorithms, which have a balancing effect and promote constructive discussion. The two examples of “Polis” and X’s “Community Notes” demonstrate that bridging algorithms can work in practice, not just in theory.

2

The positive effect of bridging algorithms on digital discourse is well recognized within large online platform companies such as Meta.

In the “Facebook Papers” published by whistleblower Frances Haugen, there are references to various experiments that the company carried out with bridging algorithms. The result: Recommendation systems that place greater weight on agreement between people from different groups significantly improve the quality of discourse.

3

However, the online platforms are currently doing little to change their recommendation systems.

Meta, for example, has not yet followed up on its initial experiments with Facebook. The financial disadvantage of bridging algorithms is too obvious: Their use reduces the time spent on the platforms, and thus lowers advertising revenue.

For this situation to change, and for bridging algorithms to find wider application, three main things need to happen:

More evidence is needed showing which specific bridging criteria affect the quality of discourse quality, and how. The emergence of new platforms such as BlueSky, which allow users to freely choose which recommendation algorithms will be used, offers a useful window of opportunity to test bridging algorithms in practice.

Platforms need to live up to their social responsibility more fully, and facilitate better digital discourse with the help of their recommendation systems. This would not require a complete redesign, but merely the addition of bridging criteria to existing recommendation systems – such as the probability that two different opinion groups agree with the same content or statement.

In parallel, public and political pressure on online platforms must be increased. If providers do not fulfill their social responsibility on their own, regulation will be needed to bring about change.

These are not easy tasks. But this discussion is necessary. Recommendation algorithms represent a key point of technological leverage, and modifying them as discussed could allow the problem of polarization in the digital space to be addressed more effectively.

A different digital discourse is possible – and is essential in order to strengthen constructive discourse worldwide.

Contact

Do you have any questions or comments? Please feel free to contact us at any time.

Dr. Felix Sieker

Dr. Felix Sieker

Project Manager, Digitalization and Common Good Program

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