## 1 Introduction  
Robots played an important role during the COVID-19 pandemic in the context of preventing infection, such as disinfection [1, 2], delivery [3, 4], and therapy [5, 6]. As interaction opportunities continue to grow between robots and people, the importance of understanding the social impact of human-robot interactions is also increasing. For example, a previous study reported that social robots are a medium to provide information to users in public environments [7]. Such information-providing roles would be one essential application for social robots. Recent studies argued that multiple robots influence behavior changes in humans [8, 9]. Another study reported that observing conversations between two robots attracts people's attention more than explanations from just one robot [10]. Another perspective argues that social praise from multiple agents more effectively improves motor skills than a single agent [11]. Based on these contexts, using multiple robots as a form of media, e.g., conversations and discussions among robots, to provide information is a promising application method during COVID-19 in the context of preventing infections, similar to remote-meeting applications.  
For this purpose, investigating influences on people's thinking from robot conversations is important to design the contents in advance. Previous studies reported that the sequence of information greatly influences people, a finding known as the primacy/recency effect [12; 13]. The primacy effect suggests that people are more highly influenced by the information presented at the start than information presented at the middle or end of the contents [12]. The recency effect is the opposite phenomenon; people are more influenced by the information presented at the conclusion of an interaction than information presented at its beginning [13]. These effects have been broadly investigated [14; 15; 16; 17; 18; 19; 20; 21; 22], and researchers in human-computer interaction also recently investigated these effects in cases where information providers are not humans [23; 24; 25; 26]. The results suggest that primacy/recency effects exist in human-computer interaction contexts, and sometimes the recency effects are larger than the primacy effects [25; 26].  
In addition to the recency effects, we believe that another factor influences people's thinking in a discussion context: opinion changes and reaffirmations. Suppose a speaker changes her opinion during a discussion with another speaker who has a different opinion or speculates aloud with a different view. In that case, observers might have been more influenced than by just the reaffirmation of identical opinions. Moreover, even though the amount of information is the same, showing opinion changes may elicit more trust than just reaffirming identical opinions. When robots provide information during discussions, clarifying the effects of opinion changes and reaffirming them from the viewpoint of impression changes as well as trust represent important information for using robots as a medium and designing their content.  
In this study, we investigated how exposure to opinion changes and reaffirmations in robot conversations influenced observer impressions. We conducted a web survey using video stimuli (Figure 1), where two robots discussed Japan's first state of emergency during the pandemic. We compared participant attitudes toward these topics before/after watching video stimuli and their perceived media trust [27] in the videos. We prepared two patterns of opinion changes to a different side (positive-negative and negative-positive) and two patterns of opinion reaffirmation (positive-positive and negative-negative) with identical information contents, and only modified their order.  
The paper's organization is as follows: Section 2 describes the materials and methods. Section 3 describes the experiment results and provides discussions based on the results. Section 5 describes the conclusions.