Algorithms of Social Manipulation
As we all continuously interact with each other and our favorite businesses through apps and websites, the level at which we are being tracked and monitored is significant. While the technologies behind these capabilities provide us value, the tech companies can also influence our decisions on where to click, spend our money, and much more.
By Diego LopezYse, Data Scientist.
Photo by Tony Liao on Unsplash.
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 influencing your behavior, so you become more predictable. By conditioning your actions, companies can predict outcomes in a better way and know better what to sell you.
Every breath you take
Systems and apps make use of the massive amount of data, like users’ locations, 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 True People Search probed the privacy policies of 11 of the biggest tech companies in the world to find out exactly what they know about us, and the result is scary. It’s not the information you are used to sharing (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.
What information are giant tech companies collecting from you? Source: TruePeopleSearch.
Uber stores massive amounts of data from its users, including their location, gender, spending history, contacts, phone battery level, whether they are on the way home from a one-night stand, and even if they are drunk or not. Uber has also experimented with its drivers to determine the most effective strategies for keeping them on the road as long as possible.
In order to place the right content in front of the right people, Netflix 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 tens of thousands of micro-genres and then paired with a user’s viewing history. Everything you see on Netflix is a recommendation: the rows, the titles in those rows, and the order of those titles within the rows are all deeply considered.
But Amazon is a data powerhouse taken to a whole new level. They capture absolutely everything, 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.
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 eyesight: they can now access 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. Algorithms are a step by step method for solving a problem or reaching a goal, based on taking input and conducting a sequence of specified actions to reach a result. Since the explosion of modern technologies, they have expanded, become more sophisticated, and replicated everywhere, having a central role in places like social media platforms.
The goal of several social media and content selection algorithms is to maximize clicks. 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.
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 interests to become narrower, 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 “predictable point” and making it easier for companies to perform any action (e.g., sell you something).
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 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, where you become a more predictable clicker, and so they can monetize you more effectively. This is advanced applied behavioral science, or as Jeff Hammerbacher (the founder of Cloudera) said:
“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 Wall Street Journal investigation 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, eBay saw its traffic from Google nosedive during 2014, contributing to a $200 million decrease in its revenue guidance for the year. 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.
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.
Original. Reposted with permission.
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