Group identity strengthens online through four steps. Three of them are good or unavoidable, and one is a design decision — which is where any useful argument has to point.
Find, sort, harden, distort — and the one step that is a choice.
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People find each other
Genuinely valuable, especially for anybody whose group is rare where they live.
The ranking shows more of the same
A by-product of measuring engagement. Nobody chose to narrow anything.
The group defines itself against another
Material about the other group holds attention better, so the ranking favours the version with an enemy in it.
Why the third step is the one to argue about: Finding each other is a gain and sorting is hard to avoid. What can be changed is what the system rewards — and at present it rewards the version of a group identity that has somebody to be against.
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Real-world examples you can name
The Christchurch attack livestream and the Christchurch Call — attack March 2019; Call launched May 2019
An attacker livestreamed a mass shooting; copies were re-uploaded faster than moderation systems removed them. New Zealand and France then convened governments and platforms around a shared commitment to eliminate terrorist content online.
Who it affected: The victims' communities, and moderators and users exposed to the footage.
#BookTok and algorithmic taste — from 2020
A community of readers posting short book videos whose recommendations, amplified by TikTok's ranking, put backlist titles onto bestseller lists years after publication and changed how publishers market fiction.
Who it affected: Readers, authors and booksellers — an industry reshaped by a recommender.
Government internet shutdowns — tracked annually by Access Now since 2016
States order operators to suspend mobile data or the whole internet, usually during protests, elections or exams. India has recorded the most shutdowns of any country for several consecutive years.
Who it affected: Everyone in the affected region — including businesses, hospitals and students.
| Effect | Where it helps | Where it harms |
|---|---|---|
| Finding people like you | Isolated or minority groups | Groups organised around harm |
| Shared language and rules | Belonging and support | A group that cannot be argued with |
| Amplification | A cause nobody would have funded | Material designed to spread |
How this is tested — questions about polarisation appear constantly, and the marks are in the mechanism rather than in the description. It comes up two ways:
Paper 1 — structured question
- Part c: evaluate platforms' effect on social division
- Part b: explain how online groups become hostile to each other
Paper 2 — source-based question
- Q4: synthesise sources on polarisation
- HL Paper 3: recommend something that does not break the good case
The trap: “social media divides people”: Say which step does it. Finding each other does not divide anybody — what is rewarded does, and that is changeable.
Explain one way a recommendation system can increase hostility between groups.
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.
To what extent do digital platforms cause social division?
Model answer plan
See the mark-by-mark plan — for / against / judgement, with marking guidance — in study mode.