  Recently, an increasing number of safety organizations in the U.S. have
incorporated text-based risk reporting systems to respond to safety incident
reports from their community members. To gain a better understanding of the
interaction between community members and dispatchers using text-based risk
reporting systems, this study conducts a system log analysis of LiveSafe, a
community safety reporting system, to provide empirical evidence of the
conversational patterns between users and dispatchers using both quantitative
and qualitative methods. We created an ontology to capture information (e.g.,
location, attacker, target, weapon, start-time, and end-time, etc.) that
dispatchers often collected from users regarding their incident tips. Applying
the proposed ontology, we found that dispatchers often asked users for
different information across varied event types (e.g., Attacker for Abuse and
Attack events, Target for Harassment events). Additionally, using emotion
detection and regression analysis, we found an inconsistency in dispatchers'
emotional support and responsiveness to users' messages between different
organizations and between incident categories. The results also showed that
users had a higher response rate and responded quicker when dispatchers
provided emotional support. These novel findings brought significant insights
to both practitioners and system designers, e.g., AI-based solutions to augment
human agents' skills for improved service quality.
