Finding a Way Through the Social Media Labyrinth: Guiding Design Through User Expectations

TL;DR

This study addresses labyrinthine SNS UIs by analyzing user expectations through a card-sorting exercise on Facebook-derived features (N=21). Participants rated the importance and frequency of 58 UI features, enabling hierarchical clustering into six feature groups and revealing insights into how best to structure SNS interfaces. The findings highlight areas where high-importance features should be readily accessible and where deep nesting or deferred placement could reduce navigational complexity, with implications for improving user agency and data control. Overall, the work provides design guidelines for reorganizing SNS UIs to enhance discoverability and mitigate labyrinthine navigation without relying on deceptive patterns.

Abstract

Social networking services (SNS) have become integral to modern life to create and maintain meaningful relationships. Nevertheless, their historic growth of features has led to labyrinthine user interfaces (UIs) that often result in frustration among users - for instance, when trying to control privacy-related settings. This paper aims to mitigate labyrinthine UIs by studying users' expectations (N=21) through an online card sorting exercise based on 58 common SNS UI features, teaching us about their expectations regarding the importance of specific UI features and the frequency with which they use them. Our findings offer a valuable understanding of the relationship between the importance and frequency of UI features and provide design considerations for six identified UI feature groups. Through these findings, we inform the design and development of user-centred alternatives to current SNS interfaces that enable users to successfully navigate SNS and feel in control over their data by meeting their expectations.

Figures (5)

• Figure 1: This figure displays one participant's card sorting results featuring 5 groups of cards with the labels security, privacy, support, basic functionality and control over personal data and usage preference. The cards are both colour-coded and include importance ratings according to the study design.
• Figure 2: This figure shows the similarity matrix of all 58 Facebook features based on the 21 card sorting groups.
• Figure 3: This hierarchical clustering dendrogram illustrates the optimal number of groups of the card sorting study, highlighted by the dotted red line at 40%.
• Figure 4: This figure shows a scatter plot of all 58 Facebook features based on the 21 card sorting groups placed along the two dimensions: importance and frequency. The scatter plot is divided into four quadrants through the overall mean importance and frequency ratings. The four quadrants can be characterised by containing features with low importance and low frequency ratings (lower left quadrant), low importance rating and high frequency rating (lower right quadrant), high importance and low frequency rating (upper left quadrant), and high importance and high frequency rating (upper right quadrant).
• Figure 5: This figure shows each feature and their convex hull for each of the six groups as introduced in Figure \ref{['fig:scatter_plot']}. Each sub-figure visualises the distribution of the contained group across the two dimensions of importance and frequency.