Government chatbots have become increasingly popular as artificial-intelligence-based tools to improve communication between the government and its citizens. This study explores the interaction mode design of a trustworthy government chatbot, which involves multiple social characteristics from the user-centric perspective. A discrete choice experiment was conducted in the context of Chinese government chatbots to examine the effects of various social characteristics on citizen preferences. Participants utilized a crowdsourcing survey platform to report their preferences for interaction processes designed with distinct sets of social characteristics. Valid data were obtained from 371 participants and analyzed using a multinomial logit model. The results indicate that (in order from highest to lowest impact) emotional intelligence, proactivity, identity consistency, and conscientiousness significantly influence the citizens' preferences. Identity consistency has a negative effect, whereas the other factors all have positive impacts. It was also determined that some of these correlations are influenced by the participants' individual characteristics, such as age, gender, and prior experience with chatbots. This work provides empirical evidence for the relative importance of social characteristics and their impacts on user perception, expands the service dimension scope of information provision/communication (one of five categories of digital interaction), and facilitates the identification and operationalization of the social characteristics. We provide a theoretical framework to understand the interaction model design of a trustworthy government chatbot and also offer practical recommendations for government chatbot designers and policy implications. •We identified five key social characteristics for government chatbots.•A discrete choice experiment was conducted to examine the relative importance of these social characteristics.•Emotional intelligence, proactivity, identity consistency, and conscientiousness significantly impact citizen preferences.•These significant effects are reduced gradually.•Some effects were strengthened or weakened by individual factors such as age, gender, and prior chatbot usage experience.