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How Recommendation Algorithms Shape What Looks Popular
A plain-language look at how ranking systems decide what to show, and why that makes apparent popularity partly a product of platform design.
Digital Life
Platforms, Algorithms and Attention
How platform design shapes what appears popular.
Attention Spans and Why the Popular Claim Is Hard to Test
Why claims that attention is shrinking are hard to measure: loose definitions, task-dependent focus, and the gap between lab tests and everyday behaviour.
Bots, Fake Accounts and Inflated Popularity
How artificial activity inflates follower and engagement counts, and the methods and limits researchers face when trying to detect bots and fake accounts.
Engagement Metrics and What a Like Actually Measures
Likes, shares, comments and views each capture different actions. This article explains what each signal does and does not indicate about real interest.
Filter Bubbles and Echo Chambers and What the Evidence Supports
A review of the filter bubble and echo chamber ideas, how researchers test them, and why studies on isolated information spaces tend to give mixed results.
Hashtags and Memes as Units of Cultural Spread
How researchers follow viral memes and hashtags across the web, which methods they use to trace spread, and where those methods run into limits.
Platform Rule Changes and Sudden Shifts in Trend Data
How a policy, design or definition change on a platform can create a jump or drop in a chart that looks like a change in human behaviour, and how to check.
Trending Lists and What Makes a Topic Trend
How platforms decide what appears on trending lists, why velocity matters more than volume, and why a trending list is not a measure of public interest.
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