We’ve recently seen research about so-called “bots” and misinformation on Twitter and wanted to share our perspective on why findings that might seem remarkable at first are likely inaccurate. We’re working on a more detailed explanation, but some comments for now.

We continue to be excited by the research opportunities that Twitter data provides. Our service is the largest source of real-time social media data, and we make this data available to the public for free through our public API. No other major service does this.
Many researchers, academics, and journalists use our public API — a set of tools for programmatically accessing information on Twitter. We make all public Twitter content available via our APIs. You can learn more about them here: https://t.co/QJQ0USRvI2
The basic issue with much of the research based on our public APIs is simple: The APIs don't provide insight into our defensive actions to protect Twitter from manipulation, including bots.
Because of this, API-based research can't distinguish between accounts we've already identified as bad (and hidden or removed) and real, authentic ones.
This means that our primary actions here — challenging, filtering, and removing bad actors before they have a chance to disrupt people's experience on Twitter — are not reflected.
Why not include this data? Because doing so would make it easier for bad actors to get around our defenses. https://t.co/Q5yweOXc1x
Let’s take a step back and look at the issue of “bots” in general. Even among researchers, there’s little agreement about what “bot” means. It’s a term used to refer to everything from accounts that post automatically to spammers to real people that Tweet something controversial.
The lack of understanding of what a “bot” is and is not contributes to fear, uncertainty, and distrust — in short, unhealthy conversations.
The same way we sometimes see people dismissing facts as "fake news," we also see real people labeling each other as bots rather than engaging with each other — to the detriment of the public conversation.
We've also seen bot detectors and dashboards created by commercial entities, which claim conversations are full of bots, seemingly in an effort to boost their own business models.
When we talk about bots, we mean accounts engaged in platform manipulation and spam. Even then, identifying bots using only public data is very difficult.
Since nobody other than Twitter can see non-public, internal account data, third parties using Twitter data to identify bots are doing so based on probability, not certainty.
One of the most common signals used to predict if someone is a bot is how often they Tweet, or how many times they Retweet. The obvious problem there is, people who are passionate about politics, or sports, or music also Tweet a lot.
Some people only Retweet. There are lots of different ways to use Twitter, and labeling certain uses “bot-like” is unhelpful. Other signals, like political views, the presence of a profile photo, frequency of Retweets, or number of followers seem obvious, but are not clearcut.
These behaviors differ globally, across age groups, language usage, and people’s individual choices about their own privacy and self-expression online.
Many of the common “bot detectors” or “troll hunters” use machine learning techniques to return a “bot score.” What does this actually mean? The answer is very little.
In order to train a machine learning model, you have to start with a training set of users you “know” are bots, so the model can predict whether other users are similar to or different from them.
These tools and approaches are deeply flawed. In our experience, most people aren’t very good at identifying bots from public information alone.
The end result is a staggering margin of error, and one that builds in preconceptions and biases about Tweet volume, political beliefs, and user behavior. These issues rarely make it into media reports, but are often the reasons why some numbers are surprisingly large.
Much of what is being presented as categorical findings is in fact an extrapolated guess and not even close to being accurate. There isn’t really a bot behind every flag. This concern was articulated by one leading researcher in this Buzzfeed piece: https://t.co/WqydQjiYIE
We continue to be committed to enabling academic research, at scale, using Twitter data. Our policies are written to support this work — including when the results are unflattering to Twitter.
However, we believe that to protect our efforts promoting healthy public conversations, there’s a need to speak up here — a lot of this “bot research” is not peer reviewed and not reflective of the facts on any level.
These types of studies, that are covered widely in the media, do not stand up to scrutiny and undermine healthy public conversation, our singular mission as a company.
Oh, and if you see a suspicious account, use our new reporting feature and let us know. It helps our work to make this place better for everyone. Thanks for reading. https://t.co/kypOkCyWk9

More from Tech

These past few days I've been experimenting with something new that I want to use by myself.

Interestingly, this thread below has been written by that.

Let me show you how it looks like. 👇🏻


When you see localhost up there, you should know that it's truly an experiment! 😀


It's a dead-simple thread writer that will post a series of tweets a.k.a tweetstorm. ⚡️

I've been personally wanting it myself since few months ago, but neglected it intentionally to make sure it's something that I genuinely need.

So why is that important for me? 🙂

I've been a believer of a story. I tell stories all the time, whether it's in the real world or online like this. Our society has moved by that.

If you're interested by stories that move us, read Sapiens!

One of the stories that I've told was from the launch of Poster.

It's been launched multiple times this year, and Twitter has been my go-to place to tell the world about that.

Here comes my frustration.. 😤
On Wednesday, The New York Times published a blockbuster report on the failures of Facebook’s management team during the past three years. It's.... not flattering, to say the least. Here are six follow-up questions that merit more investigation. 1/

1) During the past year, most of the anger at Facebook has been directed at Mark Zuckerberg. The question now is whether Sheryl Sandberg, the executive charged with solving Facebook’s hardest problems, has caused a few too many of her own. 2/
https://t.co/DTsc3g0hQf


2) One of the juiciest sentences in @nytimes’ piece involves a research group called Definers Public Affairs, which Facebook hired to look into the funding of the company’s opposition. What other tech company was paying Definers to smear Apple? 3/ https://t.co/DTsc3g0hQf


3) The leadership of the Democratic Party has, generally, supported Facebook over the years. But as public opinion turns against the company, prominent Democrats have started to turn, too. What will that relationship look like now? 4/

4) According to the @nytimes, Facebook worked to paint its critics as anti-Semitic, while simultaneously working to spread the idea that George Soros was supporting its critics—a classic tactic of anti-Semitic conspiracy theorists. What exactly were they trying to do there? 5/
Ok, I’ve told this story a few times, but maybe never here. Here we go. 🧵👇


I was about 6. I was in the car with my mother. We were driving a few hours from home to go to Orlando. My parents were letting me audition for a tv show. It would end up being my first job. I was very excited. But, in the meantime we drove and listened to Rush’s show.

There was some sort of trivia question they posed to the audience. I don’t remember what the riddle was, but I remember I knew the answer right away. It was phrased in this way that was somehow just simpler to see from a kid’s perspective. The answer was CAROUSEL. I was elated.

My mother was THRILLED. She insisted that we call Into the show using her “for emergencies only” giant cell phone. It was this phone:


I called in. The phone rang for a while, but someone answered. It was an impatient-sounding dude. The screener. I said I had the trivia answer. He wasn’t charmed, I could hear him rolling his eyes. He asked me what it was. I told him. “Please hold.”

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I'm going to do two history threads on Ethiopia, one on its ancient history, one on its modern story (1800 to today). 🇪🇹

I'll begin with the ancient history ... and it goes way back. Because modern humans - and before that, the ancestors of humans - almost certainly originated in Ethiopia. 🇪🇹 (sub-thread):


The first likely historical reference to Ethiopia is ancient Egyptian records of trade expeditions to the "Land of Punt" in search of gold, ebony, ivory, incense, and wild animals, starting in c 2500 BC 🇪🇹


Ethiopians themselves believe that the Queen of Sheba, who visited Israel's King Solomon in the Bible (c 950 BC), came from Ethiopia (not Yemen, as others believe). Here she is meeting Solomon in a stain-glassed window in Addis Ababa's Holy Trinity Church. 🇪🇹


References to the Queen of Sheba are everywhere in Ethiopia. The national airline's frequent flier miles are even called "ShebaMiles". 🇪🇹