Founded in 2014
Botometer is a web-based program that uses machine learning to classify Twitter accounts as bot or human by looking at features of a profile including friends, social network structure, temporal activity, language and sentiment. Botometer outputs an overall bot score (0-5) along with several other scores that provides a measure of the likelihood that the account is a bot.
Botometer is not affiliated with RAND. It was selected for this database because it fits our researchers' inclusion criteria.
- Tool type
- Bot/spam detection
- Fully operational
- Intended users
- General public
- Tool focus
- This tool is process-focused. It evaluates how information is produced and disseminated.
- Method or technology
- Machine learning and AI
- Is the tool automated?
- Founding organization
- Indiana University
- Founder/primary contact
- Clayton Davis, Onur Varol
How is this tool working to address disinformation?
This tool aims to fight disinformation by identifying bots (frequent sources of disinformation) and their content, ultimately helping users interpret and evaluate information.
Is there a connection with tech platforms?
Who is funding the tool?
Botometer is a collaboration between Indiana University Network Science Institute (IUNI) and the Center for Complex Networks and Systems Research (CNetS).
Are there external evaluations?
A 2017 evaluation by the tool developers focuses on the tool's abilty to detect bots, rather than the value of bot detection in fighting disinformation: arxiv.org