Category Archives: facebook

Biggest drop in Facebook organic reach we have ever seen

Facebook Explore Feed is rolling out globally this week. Most people around the world can see it in their bookmarks and they can discover new content here. But in Slovakia, Sri Lanka, Serbia, Bolivia, Guatemala and Cambodia it works differently: all posts by pages are moved from newsfeed to Explore Feed. In main newsfeed are now just friend and sponsored posts. Yes, you log into Facebook and you can see only posts from your friends and ads. You have to click on Explore Feed to see posts from pages you follo

Source: Biggest drop in Facebook organic reach we have ever seen

Publishers might have to start paying Facebook if they want anyone to see their stories – Recode

Facebook may make it harder for people to see publishers’ stories, unless those publishers pay to promote them. As part of a new test in six countries, Facebook is taking content from publishers and businesses out of its main feed. Instead, those posts will exist in a separate, hard-to-find feed that Facebook recently launched for discovering new stuff, called the “Explore Feed.”

Source: Publishers might have to start paying Facebook if they want anyone to see their stories – Recode

Decentralized Social Networks Sound Great. Too Bad They’ll Never Work | WIRED

Designing robust reward mechanisms to curate content that keeps people informed rather than entertained remains a problem. If distributed platforms could solve it, they could theoretically tackle media challenges like echo chambers and filter bubbles, but such dilemmas still present a serious challenge for new systems.

Source: Decentralized Social Networks Sound Great. Too Bad They’ll Never Work | WIRED

Is the Conservative Party deliberately distributing fake news in attack ads on Facebook?

Researchers from London School of Economics report about their ongoing research into political targeting on Facebook in the UK (the research is done in collaboration with WhoTargetsMe, a browser extension to measure political targeting on Facebook):

As we made clear in our first two posts, our analysis here is exploratory. It is, for example, unclear to what extent our dataset is representative of Conservatives’ online advertising throughout this campaign, and the WhoTargetsMe sample of potential voters, from whose Facebook feeds our data is scraped, may be skewed.

Bearing in mind these problems, we can say that, of the 820 exposures to ads paid for by Conservatives that we analysed, 28% (or 232 items) attacked Corbyn using facts that appear to be false or are clearly manipulated to confound the reader – and sometimes both.

Generally, Conservatives used 73% (598) of its 820 ads exposed in our sample to attack Corbyn. They are not, of course, the only ones targeting opponents. As we have shown, both Labour and Lib Dems have done the same. However, while ads by these other parties conveyed simplifying messages, portraying adversaries as weak, immoral or pro-elite, we couldn’t find, at least in our samples, pieces by them using baseless or misleading facts.

Source: Is the Conservative Party deliberately distributing fake news in attack ads on Facebook? | LSE Media Policy Project

Build a Better Monster: Morality, Machine Learning, and Mass Surveillance

By Maciej Ceglowski

The tech industry is in the middle of a massive, uncontrolled social experiment. Having made commercial mass surveillance the economic foundation of our industry, we are now learning how indiscriminate collections of personal data, and the machine learning algorithms they fuel, can be put to effective political use. Unfortunately, these experiments are being run in production. Our centralized technologies could help authoritarians more than they help democracy, and the very power of the tools we’ve built for persuasion makes it difficult for us to undo the damage done. What can concerned people in the tech industry do to seize a dwindling window of opportunity, and create a less monstrous online world?

 

Source: Build a Better Monster: Morality, Machine Learning, and Mass Surveillance