Before the internet, we relied on newspapers and broadcasters to filter much of our information, choosing curators based on their styles, reputations and biases – did you want a Wall Street Journal or New York Times view of the world? Fox News or NPR? The rise of powerful search engines made it possible to filter information based on our own interests – if you’re interested in sumo wrestling, you can learn whatever Google will show you, even if professional curators don’t see the sport as a priority.
Social media has presented a new problem for filters. The theory behind social media is that we want to pay attention to what our friends and family think is important. In practice, paying attention to everything 500 or 1500 friends are interested in is overwhelming – Robin Dunbar theorizes that people have a hard limit to how many relationships we can cognitively maintain. Twitter solves this problem with a social hack: it’s okay to miss posts on your feed because so many are flowing by… though Twitter now tries to catch you up on important posts if you had the temerity to step away from the service for a few hours.
Facebook and other social media platforms solve the problem a different way: the algorithm. Facebook’s news feed usually differs sharply from a list of the most recent items posted by your friends and pages you follow – instead, it’s been personalized using thousands of factors, meaning you’ll see posts Facebook thinks you’ll want to see from hours or days ago, while you’ll miss some recent posts the algorithm thinks won’t interest you. Research from the labs of Christian Sandvig and Karrie Karahalios suggests that even heavy Facebook users aren’t aware that algorithms shape their use of the service, and that many have experienced anxiety about not receiving responses to posts the algorithm suppressed.
Many of the anxieties about Facebook and other social platforms are really anxieties about filtering. The filter bubble, posited by Eli Pariser, is the idea that our natural tendencies towards homophily get amplified by filters designed to give us what we want, not ideas that challenge us, leading to ideological isolation and polarization. Fake news designed to mislead audiences and garner ad views relies on the fact that Facebook’s algorithms have a difficult time determining whether information is true or not, but can easily see whether information is new and popular, sharing information that’s received strong reactions from previous audiences. When Congress demands action on fake news and Kremlin propaganda, they’re requesting another form of filtering, based on who’s creating content and on whether it’s factually accurate. (...)
Algorithmic filters optimize platforms for user retention and engagement, keeping our eyes firmly on the site so that our attention can be sold to advertisers. We thought it was time that we all had a tool that let us filter social media the ways we choose. What if we could choose to challenge ourselves one day, encountering perspectives from outside our normal orbits, and relax another day, filtering for what’s funniest and most viral. So we built Gobo.
What’s Gobo?
Gobo is a social media aggregator with filters you control. You can use Gobo to control what’s edited out of your feed, or configure it to include news and points of view from outside your usual orbit. Gobo aims to be completely transparent, showing you why each post was included in your feed and inviting you to explore what was filtered out by your current filter settings. (...)
How does it work?
Gobo retrieves posts from people you follow on Twitter and Facebook and analyzes them using simple machine learning-based filters. You can set those filters – seriousness, rudeness, virality, gender and brands – to eliminate some posts from your feed. The “politics” slider works differently, “filtering in”, instead of “filtering out” – if you set the slider towards “lots of perspectives”, our “news echo” algorithm will start adding in posts from media outlets that you likely don’t read every day.
Social media has presented a new problem for filters. The theory behind social media is that we want to pay attention to what our friends and family think is important. In practice, paying attention to everything 500 or 1500 friends are interested in is overwhelming – Robin Dunbar theorizes that people have a hard limit to how many relationships we can cognitively maintain. Twitter solves this problem with a social hack: it’s okay to miss posts on your feed because so many are flowing by… though Twitter now tries to catch you up on important posts if you had the temerity to step away from the service for a few hours.
Facebook and other social media platforms solve the problem a different way: the algorithm. Facebook’s news feed usually differs sharply from a list of the most recent items posted by your friends and pages you follow – instead, it’s been personalized using thousands of factors, meaning you’ll see posts Facebook thinks you’ll want to see from hours or days ago, while you’ll miss some recent posts the algorithm thinks won’t interest you. Research from the labs of Christian Sandvig and Karrie Karahalios suggests that even heavy Facebook users aren’t aware that algorithms shape their use of the service, and that many have experienced anxiety about not receiving responses to posts the algorithm suppressed.Many of the anxieties about Facebook and other social platforms are really anxieties about filtering. The filter bubble, posited by Eli Pariser, is the idea that our natural tendencies towards homophily get amplified by filters designed to give us what we want, not ideas that challenge us, leading to ideological isolation and polarization. Fake news designed to mislead audiences and garner ad views relies on the fact that Facebook’s algorithms have a difficult time determining whether information is true or not, but can easily see whether information is new and popular, sharing information that’s received strong reactions from previous audiences. When Congress demands action on fake news and Kremlin propaganda, they’re requesting another form of filtering, based on who’s creating content and on whether it’s factually accurate. (...)
Algorithmic filters optimize platforms for user retention and engagement, keeping our eyes firmly on the site so that our attention can be sold to advertisers. We thought it was time that we all had a tool that let us filter social media the ways we choose. What if we could choose to challenge ourselves one day, encountering perspectives from outside our normal orbits, and relax another day, filtering for what’s funniest and most viral. So we built Gobo.
What’s Gobo?
Gobo is a social media aggregator with filters you control. You can use Gobo to control what’s edited out of your feed, or configure it to include news and points of view from outside your usual orbit. Gobo aims to be completely transparent, showing you why each post was included in your feed and inviting you to explore what was filtered out by your current filter settings. (...)
How does it work?
Gobo retrieves posts from people you follow on Twitter and Facebook and analyzes them using simple machine learning-based filters. You can set those filters – seriousness, rudeness, virality, gender and brands – to eliminate some posts from your feed. The “politics” slider works differently, “filtering in”, instead of “filtering out” – if you set the slider towards “lots of perspectives”, our “news echo” algorithm will start adding in posts from media outlets that you likely don’t read every day.
by Ethan Zukerman, My Heart's in Accra | Read more:
Image: Gobo
