If you use Twitter in the US, you may have noticed small annotations next to trending or notable messages. These observations come from the business’s Birdwatch initiative, a cutting-edge method of fact-checking that gives tweets more context and is entirely carried out by users.
Knowing that this system is peer-reviewed, the first thing that comes to mind is who decides (and how) that a context note is appropriate and should be appended to the tweet. Such a method wouldn’t it merely amplify the divisiveness frequently seen online? Social media sites, after all, are rife with false information and thrive on content that is radical, controversial, and contentious. The emphasis has been placed on the fact that this is how Facebook and many other websites build engagement.
Birdwatch has a unique approach. It employs a technique known as Bridging-based ranking. In general, this approach emphasizes the value of beneficial connections across various audiences. People from various backgrounds and with various viewpoints can all find the context annotations particular to tweets that are attached to them to be beneficial.
“Divisive material may be more likely to go viral in many online environments, especially those that use engagement-based ranking. This “bias toward division” is intended to be countered through bridging-based ranking systems. Aviv Ovadya from the Belfer Center at Harvard Kennedy School wrote on his Twitter blog that Birdwatch’s use of bridging to elevate context found helpful by individuals who tend to differ is an exciting step toward a better internet — one that promotes those building common ground.
This strategy has been thoroughly examined by Ovadya in a report. It is crucial to understand that this is about rewarding and giving less polarizing information a higher rating rather than simply presenting different viewpoints, which could make the polarization problem worse.
Many of the articles in Birdwatch have been regarded as educational regardless of political party.
Anyone in the US can now participate in birdwatch, and users can volunteer to do so. The social media business has undertaken a number of surveys and data analysis while the initiative has been running as a trial for more than a year to determine its effectiveness. Once the background comment was included, there was, on average, a 20 to 40% decrease in agreement with false tweets. Additionally, users are often between 15 and 35 percent less likely to choose to Like or Retweet a Tweet after a message has been added than users who view the Tweet by itself.
For Birdwatch, Twitter appears to be going all-in on openness. This method’s underlying algorithm is openly accessible on GitHub.
