Comparison of information search behavior for different exploratory tasks: Evidence from experiments in online knowledge communities

Users rely on exploratory search to find useful and serendipitous information in online knowledge communities. Although there are multiple types of exploratory tasks, we know little about the differences in search behaviors for distinct exploratory tasks. Consequently, communities cannot provide adaptive support for users performing distinct exploratory tasks. Against this backdrop, a lab experiment was conducted to reveal the behavioral differences among different exploratory tasks through querying, clicking, scrolling and eye-tracking data. By operationalizing search motivation and cognitive complexity, exploratory tasks were categorized into four types: borderline learning, core learning, borderline investigation, and core investigation. 37 participants with good search ability completed the experiment, and the final dataset contains 124 observations from 31 participants. ANOVA tests showed that users performing investigation tasks generated longer queries, more satisfied clicks, less scrolling, more fixations within result areas, more interactions with social tags, and more frequent browsing of reviews than users performing learning tasks. Compared to core tasks, users had more queries when performing borderline tasks. Moreover, machine learning was conducted to validate whether different exploratory tasks can be distinguished through these behaviors. Gradient Boosting Machine allowed the correct classification of four exploratory tasks with 84.75 % accuracy. The three most important indicators were UniQueryNum, MaxScrollDepth, and TagClickNum. By revealing differences in user behaviors for different exploratory tasks, this study advances the understanding of exploratory search behavior in knowledge communities at a finer granularity, and helps develop adaptive communities that support distinct exploratory tasks.