miscellaneous
Twitter Bot Detection
3260 papers • 126 benchmarks • 313 datasets
Academic studies estimate that up to 15% of Twitter users are automated bot accounts [1]. The prevalence of Twitter bots coupled with the ability of some bots to give seemingly human responses has enabled these non-human accounts to garner widespread influence. Hence, detecting non-human Twitter users or automated bot accounts using machine learning techniques has become an area of interest to researchers in the last few years. [1] https://aaai.org/ocs/index.php/ICWSM/ICWSM17/paper/view/15587
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Most implemented papers
Evading classifiers in discrete domains with provable optimality guarantees
Bogdan Kulynych, C. Troncoso, Jamie Hayes, N. Samarin•Wed Oct 24 2018
A graphical framework that generalizes existing attacks in discrete domains, can accommodate complex cost functions beyond $p-norms, including financial cost incurred when attacking a classifier, and efficiently produces valid adversarial examples with guarantees of minimal adversarial cost is introduced.
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