An algorithm that can spot and kill Twitterbots before they start spamming
1–10 of 14 posts
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#2I feel you can get 80% of the way there just by banning the major cloud/dedicated server hosts (ie AWS, Rackspace, Linode)
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#3>falsely identifying accounts as fake only 0.0058 percent of the time (or about 6 out of 10,000)
That may sound impressive, but if there is 554,750,000 twitter users [1], 554750000 * (6/10000) is still 332,850 people who would have a false positive.
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#4I have a particular tweet that must've gotten into a twitter bot database. It's old but it gets favorited on a regular basis by bots which are (I'm assuming) trying to look like real users.
Not really related to the article, but I've found it interesting. Anyone else see this behavior?
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#5The problem with looking at registration to identify bots is that it's easy to counteract. Right now, bots use simple methods for generating names, and fill out the form instantly, because there's no incentive not to. Neither of those factors is difficult to change.
This article also commits the cardinal sin of telling us the false positive rate without also telling us the false negative rate, or the base rate. You can't tell much from "falsely flagged about 6 out of 10,000" alone.
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#6>falsely identifying accounts as fake only 0.0058 percent of the time (or about 6 out of 10,000) That may sound impressive, but if there is 554,750,000 twitter users [1], 554750000 * (6/10000) is still 332,850 people who would have a false positive. [1]: http://www.statisticbrain.com/twitter-statistics/
That is the bane of Machine Learning algorithms. They cannot be 100% accurate. That is why for destructive measures, you need to establish human oversight when using such algorithms.
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#7I think Twitter's own system for doing this is a bit eager. I registered a new account the other day, followed the obligatory number of people they make you follow on signup, put in some basic profile info, then logged out to finish set up the next day. Logged in the next day.. account has been suspended for being suspicious. Ha!
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#8I have a particular tweet that must've gotten into a twitter bot database. It's old but it gets favorited on a regular basis by bots which are (I'm assuming) trying to look like real users. Not really related to the article, but I've found it interesting. Anyone else see this behavior?
Yep. I have a tweet that gets a random favorite every few weeks. It's months old and only bots every hit it.
That tweet and only that tweet over and over.
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#9For full details, please read the paper published at USENIX 2013 [0]. Some points which I found interesting
+ 6% of the fraudulent accounts purchased are resold (Table 2)
+ India is the most popular location to register fraudulent accounts (Table 3)
+ Hotmail is the most popular e-mail service used to confirm fraudulent Twitter accounts (Table 5)
+ The algorithm to "retroactively predict" fraudulent account is not based on any popular machine learning technique. Most of it looks like regex matching patterns.
+ The evaluation of the algorithm was done on 4,800 random sample Twitter accounts (200 each merchant) out of the available 121,027 accounts on which the longitudinal study was performed
[0] https://www.usenix.org/system/files/conference/usenixsecurit...
Re: An algorithm that can spot and kill Twitterbots before they start spamming
#10>falsely identifying accounts as fake only 0.0058 percent of the time (or about 6 out of 10,000) That may sound impressive, but if there is 554,750,000 twitter users [1], 554750000 * (6/10000) is still 332,850 people who would have a false positive. [1]: http://www.statisticbrain.com/twitter-statistics/
That is the bane of Machine Learning algorithms. They cannot be 100% accurate. That is why for destructive measures, you need to establish human oversight when using such algorithms.
However, humans are also machine learning algorithms, and may or may not have a better false positive rate than 0.0058%.