  Social bots have emerged over the last decade, initially creating a nuisance
while more recently used to intimidate journalists, sway electoral events, and
aggravate existing social fissures. This social threat has spawned a bot
detection algorithms race in which detection algorithms evolve in an attempt to
keep up with increasingly sophisticated bot accounts. This cat and mouse cycle
has illuminated the limitations of supervised machine learning algorithms,
where researchers attempt to use yesterday's data to predict tomorrow's bots.
This gap means that researchers, journalists, and analysts daily identify
malicious bot accounts that are undetected by state of the art supervised bot
detection algorithms. These analysts often desire to find similar bot accounts
without labeling/training a new model, where similarity can be defined by
content, network position, or both. A similarity based algorithm could
complement existing supervised and unsupervised methods and fill this gap. To
this end, we present the Bot-Match methodology in which we evaluate social
media embeddings that enable a semi-supervised recursive nearest neighbors
search to map an emerging social cybersecurity threat given one or more seed
accounts.
