An Explanatory Study on User Behavior in Discovering Aggregated Multimedia Web Content

Abdur Rehman Khan, Umer Rashid, Naveed Ahmed
2022 IEEE Access  
The recent advancements in the web allow users to generate multimedia content, resulting in multimedia information proliferation. Existing search engines provide access to multimedia content via a disjoint assembly of media-specific results called verticals. However, this decentralized assembly of media contents requires manual aggregation and synthesizing efforts at the user's end, hindering the information exploration process and subsequently may cause cognitive overload, hence, demanding
more » ... vative tools to discover multimedia content. The researchers have devised numerous state-of-the-art approaches; however, analysis to confirm the efficacy has little emphasis. This study investigates users' complex multimedia information-seeking behavior over state-of-the-art web search systems to unveil the user's informationseeking issues. Our research employs between-subjects study and post hoc analysis strategies to analyze participants' information-seeking characteristics. The study design adopted statistical hypothesis testing to consolidate previous user behavioral studies, confirm existing strategies, and present recommended practices for future general-purpose web search engines. The participants were assigned Google and an advanced discovery search system using the same multimedia dataset to ensure the obtained results' credibility. The primary behavioral parameters include search efforts, multimedia content exploration, search user interface (SUI), information management and presentation, and user cognition. This study uncovers several inadequacies of the search engines in meeting users' complex discovery needs, including 29.6% less user engagement, 43% system and searching dissatisfaction, and 32% less knowledge acquisition with 63.9% increased clicking effort on traditional search engines. The results confirmed previous user studies and suggest novel research recommendations statistically significant in multimedia information explorationrelated endeavors.
doi:10.1109/access.2022.3177597 fatcat:y5nm4okkyrhpnka4uge2oxxi3m