Its all in a name: detecting and labeling bots by their name
- 18 December 2018
- journal article
- research article
- Published by Springer Science and Business Media LLC in Computational and Mathematical Organization Theory
- Vol. 25 (1), 24-35
- https://doi.org/10.1007/s10588-018-09290-1
Abstract
No abstract availableKeywords
Funding Information
- Office of Naval Research Global (N000141812108, N00014-13-1-0835/N00014-16-1-2324)
This publication has 14 references indexed in Scilit:
- Bot Conversations are Different: Leveraging Network Metrics for Bot Detection in TwitterPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Using Random String Classification to Filter and Annotate Automated AccountsPublished by Springer Science and Business Media LLC ,2018
- The DARPA Twitter Bot ChallengeComputer, 2016
- BotOrNotPublished by Association for Computing Machinery (ACM) ,2016
- Reverse Engineering Socialbot Infiltration Strategies in TwitterPublished by Association for Computing Machinery (ACM) ,2015
- Unsupervised, low latency anomaly detection of algorithmically generated domain names by generative probabilistic modelingJournal of Advanced Research, 2014
- Using naive bayes to detect spammy names in social networksPublished by Association for Computing Machinery (ACM) ,2013
- Who is tweeting on TwitterPublished by Association for Computing Machinery (ACM) ,2010
- A few chirps about twitterPublished by Association for Computing Machinery (ACM) ,2008
- A Mathematical Theory of CommunicationBell System Technical Journal, 1948