How chatbot communication styles impact citizen reports to police

OBJECTIVE: We developed and tested a chatbot for reporting information to police. We examined how chatbot communication styles impacted three outcomes: (a) report accuracy, (b) willingness to provide contact information, and (c) user trust in the chatbot system. HYPOTHESES: In police-citizen interactions, people respond more positively when police officers use a combination of power and solidarity in their communication. We expected that this would hold for citizen-reporting chatbot interactions. METHOD: We conducted an online survey experiment with 950 U.S. adults who approximated the population on key demographics. Participants watched a video of a suspicious scenario and reported the incident to a chatbot. We manipulated and programmed the communication style of a generative pre-trained transformer chatbot to include elements of the power-solidarity framework from linguistics to create a 2 (power: low vs. high) × 2 (solidarity: low vs. high) design. We then compared three outcomes across conditions. RESULTS: The high power-high solidarity condition yielded the most positive responses. Relative to high power-high solidarity reports, low power-low solidarity reports were less accurate about the individual involved. Trust in the chatbot and willingness to provide contact information did not vary across conditions. CONCLUSION: Findings contributed to criminological, linguistic, and information technology literatures to show how communication styles impact user responses to and perceptions of a chatbot for reporting to police. (PsycInfo Database Record (c) 2025 APA, all rights reserved)

Chatbot · Internet privacy · MEDLINE · Political science · PsycINFO · Solidarity · World Wide Web · AI in Service Interactions · Applied Psychology · Computer Science · Deception detection and forensic psychology · Ethics and Social Impacts of AI · Psychology · Social Psychology