Do you know how your apps work? Are you aware of what tech companies are doing in the back with your data? And what’s more revealing: do you know which of your action are actually influenced by those apps? When you take a trip with Uber, buy stuff on Amazon, or watch a movie on Netflix: when are you consciously deciding and when are you being heavily influenced? Tech companies are not passively observing your behavior and acting as a consequence: they are . By conditioning your actions, companies can predict outcomes in a better way, and know better what to sell you. influencing your behavior so you become more predictable Every breath you take Systems and apps make use of massive amount of data like user’s location, maps, browser’s interests, and data streams coming from mobile and wearable devices, in an era of unprecedented power for firms who are no longer merely appealing to our innate desires, but programming our behaviors. The site to find out exactly what they know about us, and the result is scary. It’s not the information you are used to share (e.g. your name or email address), but all the information you wouldn’t consider sharing that makes this frightening. Big tech companies record data like income level, political and religious views, credit card information, your calendar events, all your search history and visited websites, and all the content you viewed or engaged with. True People Search probed the privacy policies of 11 of the biggest tech companies in the world (What information are giant tech companies collecting from you? Source: ) TruePeopleSearch , including their location, gender, spending history, contacts, phone battery level, whether they are on the way home from a one-night stand, and . Uber to determine the most effective strategies for keeping them on the road as long as possible. stores massive amounts of data from its users Uber even if they are drunk or not has also experimented with its drivers In order to place the right content in front of the right people, logs everything you have ever watched and how you watch: every time you click, watch, search, play, pause, what programs you consider watching but choose not to, and when you’re most likely to rerun a show. To better identify users’ preferences, content is categorized into , and then paired with a user’s viewing history. : the rows, the titles in those rows, and the order of those titles within the rows are all deeply considered. Netflix tens of thousands of micro-genres Everything you see on Netflix is a recommendation But is a data powerhouse taken to a whole new level. They , from your product searches, what you look at but don’t buy, what you look at next, how you pay, how you prefer your shipping, your interactions with Alexa, or your requests to Echo. And the shocking thing is the level of detail they store: they capture what device you use, how many items you subsequently clicked on after selecting a product, your location, and the reading sessions and exact time of day for each tap on your Kindle device. Amazon capture absolutely everything For Amazon, every mouse click and every twist and turn through its websites, apps and platforms are commodities that carry huge value. When Amazon convinced third-parties to sell their items via their own marketplace, data collection skyrocketed and allowed the company to see way beyond their eye-sight: they can now access to any market they’ve ever wanted to, and see how customers behave in each one of them. Tell me what I want Each day you are influenced by algorithms that guide your decisions and choices. based on taking an input and conducting a sequence of specified actions to reach a result. Since the explosion of modern technologies, they have expanded, sophisticated and replicated everywhere, having a central role in places like social media platforms. Algorithms are a step by step method for solving a problem or reaching a goal, The goal of several social media and content selection algorithms is to . They are designed to show or recommend stuff that will increase the probability of users clicking on it, since clicking is what generates revenue for the platforms. maximize clicks For example, a click-through optimization algorithm is more profitable if it can better predict what people are going to click on, so it can feed them exactly that. So, a way to optimize the result is to feed users with content they like, and don’t show anything outside their comfort zone. Although it’s true that this causes their , it’s not that the algorithms are trying to show you the stuff you like: they’re trying to turn you into a predictable clicker, taking you to a and making it easier for companies to perform any action (e.g. sell you something). interests to become narrower “predictable point” Companies have figured out that they can do this using your own data, by gradually modifying or emphasizing your preferences with targeted material. That’s basically, , where you become a more predictable clicker and so they can monetize you more effectively. This is advanced applied behavioral science, or as (the founder of ) said: if you think of a spectrum of preferences, it’s to one side or the other because they want to drive you to an extreme Jeff Hammerbacher Cloudera “The best minds of my generation are thinking about how to make people click ads. That sucks.” The reasons behind this are mainly economic. A found that Google manipulated search algorithms to prioritize large businesses over smaller ones, guiding search users to more prominent businesses over lesser-known ones. According to the investigation, . After Google told the company it had made a change that lowered the ranking of several high traffic pages, eBay considered pulling its roughly $30 million quarterly advertising spend from Google, but ultimately decided to start a pressure campaign on executives. Google eventually agreed to boost the ranking of several eBay pages that were demoted while eBay took on a costly revision of its web pages to make them more relevant. Wall Street Journal investigation eBay saw its traffic from Google nosedive during 2014, contributing to a $200 million decrease in its revenue guidance for the year Mechanized intervention is an ideal way to keep content flowing towards more lucrative topics, avoiding material that doesn’t generate engagement or profits. For tech companies to succeed, algorithms must focus on monetizable activities, and this is exactly what they are doing with our data. Interested in these topics? Follow me on or Linkedin Twitter Also published at https://medium.com/swlh/algorithms-of-social-manipulation-1915b5cd339a

Amazon

Cloudera

eBay

Google

Netflix

Twitter

Uber

Wall Street Journal

Evidence That AI Will Soon Pass the Turing Test (or maybe it already has)

Read My Stories

Too Long; Didn't Read

Make resilience your competitive advantage

Don't Let Them Fool You: Manipulative Strategies Used By Big Tech Companies To Sell You Stuff

Don't Let Them Fool You: Manipulative Strategies Used By Big Tech Companies To Sell You Stuff

About Author

Comments

TOPICS

THIS ARTICLE WAS FEATURED IN

Related Stories

Untitled Story

Evidence That AI Will Soon Pass the Turing Test (or maybe it already has)

10 Things Everyone Should Know About Machine Learning

10 Repositories that Will Transform the Way You Approach Technical Interviews

10 (Free) Data Structure and Algorithm Courses Junior Developers Should Explore

10 Data Structure & Algorithms Books Every Programmer Should Read

Evidence That AI Will Soon Pass the Turing Test (or maybe it already has)

10 Things Everyone Should Know About Machine Learning

10 Repositories that Will Transform the Way You Approach Technical Interviews

10 (Free) Data Structure and Algorithm Courses Junior Developers Should Explore

10 Data Structure & Algorithms Books Every Programmer Should Read

Light-Mode

Classic

Newspaper

Dark-Mode

Neon Noir

Minty

HN StartUps