A 2011 Gallup poll revealed that if American men between the ages of 18 and 49 could have only one child, 54% would want a boy; “no preference,” at 26%, beat out girls, who rated a measly 19%.

You're a coach, parent, player, gym teacher or even just a fan who likes watching balls fly into nets, send $20. You saved a life. Take the rest of the day off. You have ever had a net in the driveway, front lawn or on your head at McDonald's, send $20. You ever imagined Angelina Jolie in fishnets, $20. So you stay home and eat on the dinette. You'll live. Hey, Dick's Sporting Goods. You have 255 stores. How about you kick in a dime every time you sell a net? Hey, NBA players, hockey stars and tennis pros, how about you donate $20 every time one of your shots hits the net? Maria Sharapova, you don't think this applies to you just because you're Russian? Nyet!

A 2017 study published in the Journal of Advertising utilized social media mining techniques to gauge users’ perception of a variety of common brand names.4 The study specifically looked at Twitter, examining tweets about four different brands in each of five industries: fast-food restaurants, department stores, telecommunication carriers, consumer electronics products, and footwear companies. The researchers used a tool called the Twitter Streaming Application Programming Interface (API). This tool, which is provided by Twitter, allows users to pull tweets off of Twitter according to certain keywords.  In this case, the researchers used the Twitter handles of each company (“@CompanyName”) as keywords to pull about ten million tweets about each of the twenty companies studied over a six-month period in 2015. They then used algorithms to sift through the tweets, compile them, and boil them down to a general topic and sentiment. The results were incredibly specific. For example, the study found that 15.7% of tweets about fast-food restaurants were about promotions the chains were offering5  and that 66.7% of tweets about Comcast contained a negative sentiment.6

Cambridge Analytica mined over fifty million Facebook profiles.

A study published in October of last year sought to determine how to make best use of digital out-of-home (DOOH) advertisements in the London Underground.7 An example of a DOOH ad would be a digital billboard programed to change the advertisement on display after a specific period of time.  To achieve their goal, the researchers used the same Twitter Streaming API described in the previous study; however, this time they utilized Twitter’s geotagging function (a capability that allows Twitter users to “tag” their location when they post a tweet). Each London Underground station was carefully outlined on a map of London.  Then, the researchers randomly sampled geotagged tweets falling within those zones (meaning the tweeter was at a station).  The specific Underground station, the time of the tweet, and the content of the tweet were all extracted. The researchers continued this practice for one year, seemingly unbeknownst to the Twitter-using patrons of the London Underground, collecting over 10.5 million tweets. This data was then compiled and processed to determine what sort of things people were tweeting about in each London Underground station at certain times of the day on weekdays and on weekends.  For example, nearly 35% of tweets from the Holloway Road station were about sports, and almost 40% of tweets posted between 6 PM and midnight on weekends at the North Greenwich station were about music.8 The authors of the study recommended using this data to create targeted DOOH advertising. For instance, a music-related ad on a rotating digital billboard at night on the weekends in North Greenwich station would probably be more successful than an ad for a sports team.

ocial media and big data have combined to create a novel field of study called social media mining, which is similar to data mining, but confined to the world of Twitter, Facebook, Instagram, and the like. Social media mining is “the process of representing, analyzing, and extracting actionable patterns from social media data.

However, Cambridge Analytica is not the only group using social media data to influence large populations. The use of this data has become ubiquitous among researchers, marketers, and the government.

The fear of missing out, a phenomenon first identified in 2000 by marketing strategist Dan Herman and later allegedly coined by Patrick McGinnis, is apparently one of the stronger drivers of social network use. According to a 2013 Fix infographic, 67% of users feared they would "miss something" without their social media fix.

A study from Harvard University showed that self-disclosure online fires up a part of the brain that also lights up when engaging in pleasurable activities. In some studies, frequent social media usage has caused detrimental effects in other aspects of people’s lives, leaving some researchers to view the problem as an addiction.

There are over 2 billion Facebook users worldwide, about 500 million tweets are sent daily on Twitter, 95 million images are uploaded to Instagram daily and on YouTube, over 400 hours of video are uploaded per minute. Those figures alone should tell us something. In fact, all social media is addictive by design.

It could be argued that this psychological experiment is what led to the Cambridge Analytica scandal. In the aftermath of the event, it was revealed that the personal data of 87 million people had been harvested by a researcher and sold to the consultancy,

A few years ago, the company started experimenting with people’s newsfeeds to see if negative news would make people more pessimistic online and if positive posts would make them more kind. The social media giant did not disclose this experiment or ask for permission from its users, but according to a New York Times article, "users consent to this kind of manipulation when they agree to its terms of service."

“This is an inherently cultural thing. It’s at the intersection of technology and psychology, and it’s very personal.”