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PyPI version

Botometer X Python API

A Python API for Botometer X by OSoMe. Previously known as botornot-python.

Behind the scenes, this uses the Botometer's HTTP endpoint, available via RapidAPI.

RapidAPI usage/account related questions should be posted on RapidAPI discussion.

[Change Note/Announcement]

June, 2024

We are releasing a new API endpoint for Botometer X.

Unlike the original Botometer that fetched data from Twitter and calculated bot scores on the fly, Botometer X is in archival mode and relies on pre-calculated scores based on historical data collected before June 2023. The API endpoint allows users to fetch scores in bulk using a list of user ids or screen names, without the need of a Twitter/X's developer account.

For details of Botometer X, please refer to the FQA.

Help

You probably want to have a look at Troubleshooting & FAQ in the wiki. Please feel free to suggest and/or contribute improvements to that page.

Prior to Utilizing Botometer

To begin using Botometer X, you must follow the steps below before running any code:

  1. Create a free RapidAPI account.
  2. Subscribe to Botometer Pro on RapidApi by selecting a plan.

    There is a completely free version (which does not require any credit card information) for testing purposes.

  3. Ensure Botometer Pro's dependencies are already installed.

    See the Dependencies section for details.

Quickstart

From your command shell, run

pip install botometer

Botometer X

To access the Botometer X endpoint, enter something like this in a Python shell or script:

import botometer

rapidapi_key = "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

bomx = botometer.BotometerX(rapidapi_key=rapidapi_key)
# Check accounts by usernames, note that @ is optional
bomx.get_botscores_in_batch(usernames=['@OSoMe_IU', 'botometer'])

# Check accounts by ids
bomx.get_botscores_in_batch(user_ids=[2451308594, 187521608])

# Check accounts by both usernames and ids
bomx.get_botscores_in_batch(usernames=['@OSoMe_IU'], user_ids=[2451308594])

The queries will return results like those below:

[
    {
        "bot_score": 0.09,
        "timestamp": "Sat, 27 May 2023 23:57:16 GMT",
        "user_id": "2451308594",
        "username": "Botometer"
    },
    {
        "bot_score": 0.21,
        "timestamp": "Thu, 25 May 2023 22:54:53 GMT",
        "user_id": "187521608",
        "username": "OSoMe_IU"
    }
]

The response will be a list of JSON objects. Meanings of the elements in the object:

  • bot_score: The bot score, a float number between 0 and 1 (note that we rescale the score to 1 to 5 on the Botometer X website)
  • timestamp: The time when the bot score was calculated
  • user_id: ID of the account
  • username: Username of the account

For more information on the API, consult the API Overview on RapidAPI.

Installation instructions

This package is on PyPI so you can install it with pip:

$ pip install botometer

Dependencies

Python dependencies

The dependency should be installed automatically with pip.

References

  • Botometer X: The scores are calculated using the BotometerLite model.

  • Botometer v4: Mohsen Sayyadiharikandeh, Onur Varol, Kai-Cheng Yang, Alessandro Flammini, Filippo Menczer. "Detection of Novel Social Bots by Ensembles of Specialized Classifiers." DOI, ArXiv

  • BotometerLite: Yang, K.; Varol, O.; Hui, P.; and Menczer, F. "Scalable and Generalizable Social Bot Detection through Data Selection." AAAI (2020). DOI, ArXiv

  • Botometer v3: Yang, Kai‐Cheng, Onur Varol, Clayton A. Davis, Emilio Ferrara, Alessandro Flammini, and Filippo Menczer. "Arming the public with artificial intelligence to counter social bots." Human Behavior and Emerging Technologies 1, no. 1 (2019): 48-61. DOI, ArXiv

  • Botometer v2: Varol, Onur, Emilio Ferrara, Clayton A. Davis, Filippo Menczer, and Alessandro Flammini. "Online Human-Bot Interactions: Detection, Estimation, and Characterization." ICWSM (2017). AAAI, ArXiv

  • Botometer v1 aka BotOrNot: Davis, C. A., Varol, O., Ferrara, E., Flammini, A., & Menczer, F. (2016, April). "BotOrNot: A system to evaluate social bots". In Proceedings of the 25th International Conference Companion on World Wide Web (pp. 273-274). International World Wide Web Conferences Steering Committee. DOI, ArXiv

  • Varol O., Davis C., Menczer, F., Flammini, A. "Feature Engineering for Social Bot Detection", Feature Engineering for Machine Learning and Data Analytics Google Books

  • Ferrara, Emilio, Onur Varol, Clayton Davis, Filippo Menczer, and Alessandro Flammini. "The rise of social bots." Communications of the ACM 59, no. 7 (2016): 96-104. DOI, ArXiv