What is it?
Sybillian is a twitter bot that can receive mentions or be included in conversations to analyze the account(s) to see what their bot score is based off of a BotOrNot score.
BotOrNot checks the activity of a Twitter account and gives it a score based on how likely the account is to be a bot. Higher scores are more bot-like.
BotOrNot accomplishes this goal by the use of Random Forest to do ensemble learning via a multitude of Decision Trees. The model is then trained with 'known bots' and 'known human' accounts to build a heuristic decision tree model that nullifies bias. Sybillian's role is to present BotOrNot accounts to test against this ensemble and provides a score for multiple factors, including:
- content classification
- friend classification
- network classification
- sentiment classification
- temporal classification
- user classification
- language agnostic classification
For further details, you can reference the Indiana University Publications.
What is it's purpose?
I built Sybillian because I wanted to offhand check the "truthiness" of accounts that were pushing a specific topic or idea. Whether it be for marketing or otherwise.
How can it be used?
You can either directly mentions @SybilDetector with a username. ex:
and receive a response back:
@Decad3nce SwiftOnSecurity has a bot score of 0.45. Classification of 'might be a bot'. See https://t.co/upkHRBwQ4S
Or, you can add it as a reply to a twitter "thread" to see the percentage of participants (including retweeters) ex resp:
@Decad3nce the conversation of 76 contributors contains 4 probable bots, percentage of bots in conversation 5.26315789474.
How can I deploy my own?
- Create a Twitter Application.
credentils.pywith your tokens and shared secrets from the application creation.
- Run locally by creating a virtual environment and
pip installthe active directory.
- Run remotely by hosting the application on whatever server of your choosing.
Isn't this just a shell for BotOrNot?
Yes, yes it is. But it also allows the process of 'harvesting' bot data to be further decentralized.
- Move away from local sql db to a generic and pluggable backend for caching that can be hosted remotely. Then allow surrogate bots to provide data to that central cache.