Emojis and avatars are widely used in online communications, but their emotional conveyance lacks research. This study aims to contribute to the field of emotional expression in computer-mediated communication (CMC) by exploring the effectiveness of emotion recognition, the intensity of perceived emotions, and the perceived preferences for emojis and avatars as emotional expression tools. The following were used as stimuli: 12 photographs from the Yonsei-Face database, 12 Memojis that reflected the photographs, and 6 iOS emojis. The results of this study indicate that emojis outperformed other forms of emotional expression in terms of conveying emotions, intensity, and preference. Indeed, the study findings confirm that emojis remain the dominant form of emotional signals in CMC. In contrast, the study revealed that Memojis were inadequate as an expressive emotional cue. Participants did not perceive Memojis to effectively convey emotions compared with other forms of expression, such as emojis or real human faces. This suggests room for improvement in the design and implementation of Memojis to enhance their effectiveness in accurately conveying intended emotions. Addressing the limitations of Memojis and exploring ways to optimize their emotional expressiveness necessitate further research and development in avatar design.

This study investigates the contexts in which emojis occur through co-occurrence, cluster, and association analysis of Airbnb tweets. Findings reveal these positive emojis tend to co-exist with several types of words representing conversation, emotion, activity, and marketing. This study contributes to the textual paralanguage literature in marketing.