You already know bias and stereotypes can sit inside digital media. Today you connect that to something closer to home: your own feed.
Search is one place personalisation shows up; your social feed is another. You will compare the same search across two result lists, spot how the same query can look different depending on history, and decide when a narrow, personalised feed helps you and when it costs you.
A personalised feed is a stream of posts, videos or search results ordered for you, based on what you have watched, liked, searched or stayed on before. A filter bubble is what can happen when that personalisation keeps showing you more of the same: you see a narrower slice of what exists than someone with different habits would see. That narrower slice is one way bias gets stronger, not only what a post says, but what never shows up beside it.
Personalisation is not automatically good or bad. It can surface things you care about quickly. It can also hide other angles, other evidence, and other people's experience of the same topic.
When does a personalised feed help you, and when is it worth deliberately widening what you see?
Before the experiment, take 30 seconds. On a scrap of paper or in your head, name one topic your own feed already knows you like. Keep it. You will use it later.
Work in pairs. Compare the same search across two result lists and catch three concrete differences.
Pick one option. Do not add extra words. Look only at the top few results (titles and short descriptions).
Option 1: benefits of walking to school
List A (a profile that often opens fitness and product content):
List B (a profile that often opens local news and school content):
Option 2: best beginner tips for football
List A (a profile that often watches skills, drills and gear):
List B (a profile that often opens local and youth content):
If you pick either option without two different live devices, use the worked lists for that option. Do not invent results. Neither list is "the full truth". Together they show more of the topic than either list alone.
Apply the same idea to a topic a feed already knows well.
Use a topic your feed already knows you like, or pick one from this list if you prefer not to use a personal one: football highlights, gaming setups, K-pop, GAA, cooking shorts.
Before you finish, check:
Keep it honest and specific. You are not scoring a feed as good or bad. You are weighing help against cost on one real topic.
Look at the differences on the board from the experiment.
Keep the board sentence in view: one-angle feeds can strengthen assumptions even when each result looks ordinary. That is one way bias gets amplified, not only by what a single result says, but by what never appears next to it.
Personalisation still has real upsides you just named. One optional move when you want a wider slice: a different device, a fresh search, a source outside your usual apps, or a deliberate look for another angle.
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