Know thyself through data: Improving WhatsApp interaction awareness with data-driven visualizations
Year: 2025
Type: article
Abstract: Digital behaviors are often quantified through screen time or frequency metrics, providing insights into activity patterns and their alignment with subjective perceptions. However, these metrics alone fall short of capturing texting, a behavior involving complex interpersonal dynamics. Research on messaging self-perception is scarce — a critical gap since biased perceptions can strain relationships and affect well-being. In one of the first empirical studies on this topic, we conducted an online study with 68 participants, who donated meta-data (message lengths and times) from over 300 WhatsApp chats. Participants answered questions about their messaging behavior, such as whether they write more or respond faster than their contacts. They then received personalized visualizations based on donated data and reassessed their perceptions, allowing us to examine whether data-driven insights improved self-report accuracy. The results show a significant improvement in the alignment between self-reports and objective metrics for absolute response speed and participants’ contribution within a chat. In contrast, assessments of peak activity times and response speed relative to contacts were already fairly accurate and did not change. Finally, although self-report scores changed for two subjective estimates – the even distribution of messages across chats and texting throughout the day – their alignment with objective metrics did not improve significantly. To account for the potential emotional impact of challenging participants’ existing self-perceptions, we assessed their mood and did not find significant changes in positive or negative affect. To conclude, personalized visualizations can enhance the alignment between self-perceptions and objective behavior, potentially guiding individuals toward more intentional texting behavior. • We used WhatsApp data to quantify texting behavior, incl. balance and reply speed. • Compared self-reports with objective metrics before and after data-driven feedback. • Feedback improved alignment between subjective scores and some objective metrics. • Personalized insights did not negatively affect the mood of participants. • Data-driven visuals can sharpen socially relevant aspects of texting self-perception.
Source: Computers in Human Behavior
Authors: Olya Hakobyan, Hanna Drimalla
Institution: Bielefeld University
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,780
Award: G1675984264
OpenAlex ID: W7106350299