Social Media Algorithms: Quick Quiz

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Test your understanding of recommendation systems.

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Abdelrahman Abutouq

United Arab Emirates

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Social Media Algorithms: Quick Quiz


Froggy Jumps Social Media Algorithms: Quick QuizOnline version Test your understanding of recommendation systems. by Abdelrahman Abutouq 1 Why do social media companies keep their recommendation recipes secret? a They think users will abandon apps entirely if they know the recipes. b They want to protect their business model and keep users on the app longer to make more money. c They plan to share the recipes only with trusted partners. 2 If an algorithm is designed to keep you watching, what is it LEAST likely to show you? a A high-energy video that is currently trending worldwide. b A video similar to one you just liked. c A topic that challenges your current opinions and makes you feel uncomfortable. 3 What is the main danger of an Echo Chamber created by an algorithm? a Your phone's speakers might start to echo when you play music. b You only see content that agrees with you, making it harder to understand other points of view. c The algorithm will stop showing you any new videos at all. 4 Why does the algorithm keep showing outrageous or fake headlines when you click them? a The algorithm knows the news is fake and is warning you. b The algorithm cannot tell the difference between true and fake; it only sees engagement. c The algorithm is programmed to teach you a lesson about misinformation. 5 Which is a proactive way to train your algorithm to be more helpful? a Follow every suggested person. b Leave the app open all night. c Click Not Interested or Dislike on harmful or repetitive content. 6 A video app notices Sara watches cooking videos until the end. What will the algorithm MOST LIKELY do next? a Show random videos only b Stop showing cooking videos c Recommend more cooking videos 7 Which data helps an algorithm learn the MOST about a user’s interest? a The color of the phone b How long a video is watched c The day of the week 8 Two students watch the same app but get different recommendations. Why? a The students have different interests and data b The app is broken c The algorithm is guessing 9 Which rule best explains how a recommendation algorithm works? a Choose randomly each time b Follow user data and past choices c Show everything to everyone 10 Evaluating fairness: what could happen if an algorithm shows only one type of content? a The internet stops working b Users may miss other viewpoints c Users learn many new ideas 11 If a user skips many videos quickly, what should the algorithm learn? a The user likes everything. b The user is bored c The user wants longer videos 12 Which is the BEST description of a “digital recipe” for recommendations? a list of random videos b A set of rules using data to decide c A video playlist made by friends 13 Why is it important to understand how recommendation algorithms work? a To become a programmer b To control the internet c To make smart choices online 14 Which example BEST shows an algorithm learning over time? a A book that never changes b A teacher giving homework c A music app improving playlists as you listen

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Social Media Algorithms: Quick Quiz

Froggy Jumps

About this activity

Created by

Download the paper version to play

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Social Media Algorithms: Quick QuizOnline version

by Abdelrahman Abutouq

Why do social media companies keep their recommendation recipes secret?

If an algorithm is designed to keep you watching, what is it LEAST likely to show you?

What is the main danger of an Echo Chamber created by an algorithm?

Why does the algorithm keep showing outrageous or fake headlines when you click them?

Which is a proactive way to train your algorithm to be more helpful?

A video app notices Sara watches cooking videos until the end. What will the algorithm MOST LIKELY do next?

Which data helps an algorithm learn the MOST about a user’s interest?

Two students watch the same app but get different recommendations. Why?

Which rule best explains how a recommendation algorithm works?

Evaluating fairness: what could happen if an algorithm shows only one type of content?

If a user skips many videos quickly, what should the algorithm learn?

Which is the BEST description of a “digital recipe” for recommendations?

Why is it important to understand how recommendation algorithms work?

Which example BEST shows an algorithm learning over time?