Machine Learning Times
Machine Learning Times
EXCLUSIVE HIGHLIGHTS
AI and ML in Health Care: A Brief Review
 Of the many disciplines that are active users of...
Visualizing Decision Trees with Pybaobabdt
 Originally published in Towards Data Science, Dec 14, 2021....
Correspondence Analysis: From Raw Data to Visualizing Relationships
 Isn’t it satisfying to find a tool that makes...
Podcast: Four Things the Machine Learning Industry Must Learn from Self-Driving Cars
    Welcome to the next episode of The Machine...
SHARE THIS:

1 year ago
Five Points for Anger, One for a ‘Like’: How Facebook’s Formula Fostered Rage and Misinformation

 
Originally published in The Washington Post, Oct 26, 2021.  

Facebook engineers gave extra value to emoji reactions, including ‘angry,’ pushing more emotional and provocative content into users’ news feeds.

Five years ago, Facebook gave its users five new ways to react to a post in their news feed beyond the iconic “like” thumbs-up: “love,” “haha,” “wow,” “sad” and “angry.”

Behind the scenes, Facebook programmed the algorithm that decides what people see in their news feeds to use the reaction emoji as signals to push more emotional and provocative content — including content likely to make them angry. Starting in 2017, Facebook’s ranking algorithm treated emoji reactions as five times more valuable than “likes,” internal documents reveal. The theory was simple: Posts that prompted lots of reaction emoji tended to keep users more engaged, and keeping users engaged was the key to Facebook’s business.

Facebook’s own researchers were quick to suspect a critical flaw. Favoring “controversial” posts — including those that make users angry — could open “the door to more spam/abuse/clickbait inadvertently,” a staffer, whose name was redacted, wrote in one of the internal documents. A colleague responded, “It’s possible.”

The warning proved prescient. The company’s data scientists confirmed in 2019 that posts that sparked angry reaction emoji were disproportionately likely to include misinformation, toxicity and low-quality news.

To continue reading this article, click here.

Leave a Reply