The developments of AI provide new opportunities for government service delivery. During disasters, government agencies are obligated to act as caregivers and provide support to citizens and residents in danger. However, limited studies are grounded in how chatbots and human governmental agents should apply supportive communication strategies. To fill this gap, this study conducted a 2 (Government Agent type: AI chatbot agent vs. Human agent) x 2 (Verbal Person-centeredness: High vs. Low) x 3 (Emoji Person-centeredness: High vs Low vs No emoji) design to explore their effects on emotional distress and evaluations of government service quality (N = 363). This study finds that higher verbal person-centeredness can reduce citizens’ emotional distress and evaluations of government service quality. Additionally, when lower levels of verbal and emoji person-centeredness were used, citizens evaluated service quality more positively when a chatbot agent sent the message than when a human agent did. Both theoretical and practical implications were discussed.

Previous studies have indicated that problematic social media use (PSMU) is positively related to depressive symptoms. However, the potential mechanisms underlying this relationship are not well understood. For example, prior research found that depressive symptoms are related to one’s social network structure, such as network size and density. Therefore, we examined whether one’s social network size and density mediate the relationship between PSMU and depressive symptoms. We conducted an in-person survey to collect measures of PSMU, social network size and density, and depressive symptoms. Our analysis showed that there was a positive relationship between PSMU and depressive symptoms, which was mediated by social network density but not size. Specifically, the greater one’s PSMU, the less dense their social network, and the greater their depressive symptoms. Our research suggests that PSMU may affect how individuals maintain their social connections with others, which may affect mental health. Implications were discussed.

Algorithmic recommendation systems (ARM) on dating apps serve users with a personalised feed of profiles from other users based on the inferred preferences of the user being served. Despite concerns linking ARM to problematic dating app use and negative social outcomes, it has been suggested that critical awareness of ARM's limitations, such as that ARM restrict user choice (i.e. a ‘confining’ perception of ARM, or CP-ARM), can mitigate problematic usage and reduce negative social outcomes. This study tested such a prediction with semi-structured interviews (N = 20) and a subsequent survey (N = 349), which yielded surprising results – while CP-ARM can indirectly decrease compulsive use of dating apps by lowering the perceived usefulness of dating apps, it can directly increase compulsive use, which can be attributed to a sense of helplessness in controlling digital media use. Consequently, compulsive use can decrease the intention to commit in Internet-initiated romantic relationships. The finding suggests that researchers should not assume that critical awareness of algorithms leads to less problematic usage and better social outcomes but situate the inquiries in a broader socio-cultural context where everyday life is increasingly mediatised by various social platforms and individuals find it difficult to opt out.

Prior research has produced contradictory findings regarding online daters’ potential to navigate the algorithmic systems to find compatible matches. Drawing upon a structuration algorithm media effects model, we examine whether online daters with a higher level of algorithm awareness experience less online mate-searching difficulty and report more optimism and hope after using online dating services. Analysing data from a national representative sample of American online daters (N = 871), we found that, in general, algorithm awareness was negatively related to mate-searching difficulty, which was negatively related to optimism but not hope. In addition, the relationship between algorithm awareness and mate-searching difficulty was stronger among female users than male users in our sample. The findings suggest a potentially positive role of algorithm awareness in promoting immediate online mate-searching experience on current dating platforms used by American online daters. We further discuss the implications on the role of algorithm awareness and positive immediate mate-searching experience in relation to more long-term outcomes, which calls for a dialectic view of algorithm awareness and immediate online success.

Previous studies have suggested that dating app use can foster acceptance of the controversial idea of algorithm matchmaking. However, an integrated framework to explain this relationship is lacking. To address this gap, this study proposes a familiarity-breeds-trust hypothesis, which suggests that dating app use can increase trust in dating algorithms by raising algorithm awareness and enhancing perceived agency. To test this hypothesis, the current work employed an exploratory sequential mixed-methods design. Study 1 interviewed 19 dating app users and identified four types of critical algorithm perceptions that reflected perceived threats to user agency. Study 2 surveyed 371 users of Tantan—a mainstream Chinese dating app that resembles Tinder—and found a positive relationship between Tantan use and trust in dating algorithms. Additionally, Tantan use was positively related to algorithm awareness, which was negatively related to critical algorithm perceptions. Furthermore, critical algorithm perceptions negatively predicted trust in dating algorithms. The findings provide support for the familiarity-breeds-trust hypothesis. Theoretical implications for future human-algorithm interaction studies and practical suggestions are also discussed.