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Parag Agrawal
@paraga
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San Francisco, CAJoined April 2008

Parag Agrawal’s Tweets

Context on Tweets — by the people, for the people — is coming to everyone in the US. It’s open, transparent, and we’ve consistently seen notes be found helpful and informative even by people who often disagree.
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Here’s a short video about Birdwatch! A video description is available here: twitter.github.io/birdwatch/vide
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Today we launched a new custom Timeline experiment - just one of the many things we’re working on over at 🚀 There is a lot of potential for the developer community to build features like this in the future, and we are just getting started. Congrats to the team!🌹
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The Twitter Board is committed to closing the transaction on the price and terms agreed upon with Mr. Musk and plans to pursue legal action to enforce the merger agreement. We are confident we will prevail in the Delaware Court of Chancery.
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Hey — thanks for sharing your inspiring journey with the world on Twitter, and with our team. Love to see it #LondonToLagos
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Since 19th April, @lionheart1759 has been riding from #LondontoLagos on a campaign to end Polio.He’s shared daily updates of his journey on Twitter. Myself, @OluwamayowaTJ & @nnekachile of #TwitterGhana office had the privilege to meet him on his arrival in GH. #TwitterForGood
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There are LOTS of details that are very important underneath this high-level description. We shared an overview of the estimation process with Elon a week ago and look forward to continuing the conversation with him, and all of you.
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Unfortunately, we don’t believe that this specific estimation can be performed externally, given the critical need to use both public and private information (which we can’t share). Externally, it’s not even possible to know which accounts are counted as mDAUs on any given day.
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Our actual internal estimates for the last four quarters were all well under 5% – based on the methodology outlined above. The error margins on our estimates give us confidence in our public statements each quarter.
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The use of private data is particularly important to avoid misclassifying users who are actually real. FirstnameBunchOfNumbers with no profile pic and odd tweets might seem like a bot or spam to you, but behind the scenes we often see multiple indicators that it’s a real person.
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Each human review is based on Twitter rules that define spam and platform manipulation, and uses both public and private data (eg, IP address, phone number, geolocation, client/browser signatures, what the account does when it’s active…) to make a determination on each account.
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Our estimate is based on multiple human reviews (in replicate) of thousands of accounts, that are sampled at random, consistently over time, from *accounts we count as mDAUs*. We do this every quarter, and we have been doing this for many years.
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Now, we know we aren’t perfect at catching spam. And so this is why, after all the spam removal I talked about above, we know some still slips through. We measure this internally. And every quarter, we have estimated that <5% of reported mDAU for the quarter are spam accounts.
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Our team updates our systems and rules constantly to remove as much spam as possible, without inadvertently suspending real people or adding unnecessary friction for real people when they use Twitter: none of us want to solve a captcha every time we use Twitter.
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The hard challenge is that many accounts which look fake superficially – are actually real people. And some of the spam accounts which are actually the most dangerous – and cause the most harm to our users – can look totally legitimate on the surface.
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We suspend over half a million spam accounts every day, usually before any of you even see them on Twitter. We also lock millions of accounts each week that we suspect may be spam – if they can’t pass human verification challenges (captchas, phone verification, etc).
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Some final context: fighting spam is incredibly *dynamic*. The adversaries, their goals, and tactics evolve constantly – often in response to our work! You can’t build a set of rules to detect spam today, and hope they will still work tomorrow. They will not.
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Next, spam isn’t just ‘binary’ (human / not human). The most advanced spam campaigns use combinations of coordinated humans + automation. They also compromise real accounts, and then use them to advance their campaign. So – they are sophisticated and hard to catch.
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First, let me state the obvious: spam harms the experience for real people on Twitter, and therefore can harm our business. As such, we are strongly incentivized to detect and remove as much spam as we possibly can, every single day. Anyone who suggests otherwise is just wrong.
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Finally – so much gratitude for our whole Twitter team. They have stood strong and focused, sharp and agile. They've been doing the work, as they always have. Onward.
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I will also try to bring more transparency to the work that we do. You won’t see tweets from me on the ‘topic of the day’ or the loudest sound bite, but rather on the ongoing, continuous, and challenging work our teams are doing to improve the public conversation on Twitter.
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So what can you expect from me going forward? I’m still focused on doing the job, and that includes making hard decisions as needed. I will continue to embrace the deep complexities of our service and our business. And you can expect more change for the better.
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People have also asked: why manage costs now vs after close? Our industry is in a very challenging macro environment – right now. I won’t use the deal as an excuse to avoid making important decisions for the health of the company, nor will any leader at Twitter.
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