Abstract: Social media are rich information sources during crisis events such as earthquakes and terrorist attacks. Despite myriad challenges, with the right tools, significant insight can be gained to assist emergency responders and related applications. However, most extant approaches are incomparable, using bespoke definitions, models, datasets and even evaluation metrics. Furthermore, it's rare that code, trained models, or exhaustive parametrisation details are openly available. Thus, even confirming self-reported performance is problematic; authoritatively determining state of the art (SOTA) is essentially impossible. Consequently, to begin addressing such endemic ambiguity, this paper makes 3 contributions: 1) replication and results confirmation of a leading technique; 2) testing straightforward modifications likely to improve performance; and 3) extension to a novel complimentary type of crisis-relevant information to demonstrate it's generalisability.