Diverging interests in the workplace may lead to undesirable employee behavior such as taking undue credit, underperforming, shirking responsibilities, and undermining colleagues. This kind of conduct, also referred to as weasel behavior, can have significant negative implications for both individuals and the organization as a whole. Therefore, its identification is of crucial importance. Recent work in process science has defined thirteen weasel behavior patterns and proposed the use of process mining related techniques to uncover them from the traces recorded by information systems. However, these definitions have not yet been tested on any event log. This paper aims at closing this gap by providing the design specifications and algorithms necessary to extract weasel behavior from event logs. We evaluate our implementation on the real-world logs provided by the IEEE Task Force on Process Mining and report the extent of weasel behavior present in each dataset. Our results have relevant implications on the application and development of resource-centered process analysis techniques and contribute to better understanding the information present in the widely-used BPI logs.