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TIMESTAMPS
  • 00:05
    - Bots.
  • 00:06
    They're all around us, and they can serve a number of purposes, both good and bad.
  • 00:10
    They're in our homes, they're on our phones, and they're common on social media platforms like Twitter.
  • 00:15
    But just how common are they?
  • 00:17
    Pew Research Center estimates that as much as two-thirds of tweeted links to popular websites are generated by bots, not humans.
  • 00:24
    Now, that's only tweeted links to popular websites.
  • 00:27
    But how did we determine that bots are responsible for sharing so much content on Twitter, and what exactly is a bot?
  • 00:33
    Broadly speaking, a bot is a software application that can complete automated tasks, often replacing the need for humans to do them.
  • 00:41
    Ever ask the voice assistant on your phone to play that song you've been super into lately?
  • 00:45
    Yup, that's a bot.
  • 00:47
    Bots appear in all sorts of digital spaces, and people interact with them all the time, sometimes without even realizing it.
  • 00:53
    It's no secret that there are bots on Twitter.
  • 00:55
    There are Twitter bots that can send alerts after an earthquake or post what's new on Netflix.
  • 00:60
    And then, there may be bots that act with malicious intent, trying to spread misinformation or sow confusion.
  • 01:07
    Our study of Twitter bots focused on automated accounts that tweet or retweet links to content around the web without human intervention.
  • 01:14
    This is part of a larger research agenda at Pew Research Center to better understand the flow of information online and the impact it has on society.
  • 01:21
    sample set details To measure how pervasive bots are in sharing links to external sites on Twitter, 1.2 million English-language tweets containing links using Twitter's streaming API over a 47-day period in the summer of 2017.
  • 01:33
    This gave us a random sample of public tweets, up to 1% of all public posts each day.
  • 01:38
    We wanted to know both how many of these links were shared by bot accounts and what kind of content bots were sharing.
  • 01:43
    Was it mostly news?
  • 01:44
    Was it sports?
  • 01:45
    Or was it something else entirely?
  • 01:47
    To figure this out, we wrote a computer program that followed each tweeted link to its destination and then saved the location of that page in a database.
  • 01:55
    We then isolated nearly 3,000 websites that were shared most frequently.
  • 01:59
    After we had a list of tweets with links to these most popular sites, we counted how many came from bots.
  • 02:04
    Sounds easy, right?
  • 02:05
    Well in practice, it's fairly complicated.
  • 02:07
    First, classifying a million tweets by hand takes a long time. classification problem in supervised machine learning.
  • 02:11
    Second, relatively few automated Twitter accounts identify themselves as bots, and it's not easy to know if an account is a bot or not.
  • 02:19
    So, in order to determine which of the accounts in our sample were, we turned to a tool called Botometer.
  • 02:25
    Now, if you're wondering what Botometer is, it's a machine-learning algorithm developed by researchers at Indiana University and the University of Southern California.
  • 02:33
    It uses over 1,000 pieces of information about a Twitter account to determine if that account is likely a bot.
  • 02:38
    This information includes the age of the account, who they follow, the content of their tweets, among other things.
  • 02:44
    Also, rather than simply declaring a Twitter account to be a bot or a human, Botometer actually gives each account a score between 0 and 1.
  • 02:51
    But researchers like ourselves are the ones to decide where to draw the line between human and bot by choosing a threshold score.
  • 02:58
    So to ensure Botometer was a reliable tool for our study, we tested it using data from human coders who classified over 300 accounts as automated or not.
  • 03:06
    We then had Botometer classify the same accounts at various thresholds to determine the most accurate score to use for our full sample of tweets.
  • 03:14
    By the way, you can see all of these tests in the methodology section of our report -- http://www.pewinternet.org/2018/04/09/bots-in-the-twittersphere/.
  • 03:18
    In the end, we estimated that automated accounts are responsible for sharing about two-thirds of tweeted links in our sample.
  • 03:25
    Comparatively, two-thirds of tweeted links to popular news and current events sites were also shared by bots.
  • 03:31
    And overall, bots were most likely to share links to sites that focused on sports or adult content.content bots shared
  • 03:36
    So, now you know how we identified bots on Twitter.
  • 03:39
    But what we can't say from this study is whether the links shared by bot accounts contain truthful information or not, or the extent to which human users interacted with content posted by suspected bots.
  • 03:51
    It is important to remember that not all bots are nefarious, and some of them may even play a valuable role in the social media ecosystem.
  • 03:57
    But we'll let you decide that for yourself.
  • 03:59
    To learn more about our methods and about what types of content bots are sharing on Twitter, check out our report "Bots in the Twittersphere" at pewresearch.org/twitterbots.

How did Pew Research Center identify Twitter bots?

In the context of ongoing debates over the role and nature of bots, Pew Research Center set out to better understand how many of the links being shared on Twitter are being promoted by bots rather than humans. Our analysis found that an estimated two-thirds of tweeted links to popular websites are posted by automated accounts – not human beings.






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