How do Users Perceive Information: Analyzing user feedback while annotating textual units

ABSTRACT We describe an initial study of how participants perceive information when they categorize highlighted textual units within a document marked for a given information need. Our investigation explores how users look at different parts of the document and classify textual units within retrieved documents on 4-levels of relevance and importance. We compare how users classify different textual units within a document, and report mean and variance for different users across different topics. Further, we analyze and categorise the reasons provided by users while rating textual units within retrieved documents. This research shows some interesting observations regarding why some parts of the document are regarded as more relevant than others (e.g. it provides contextual information, contains background information) and which kind of information seems to be effective for satisfying the end users (e.g showing examples, providing facts) in a search task. This work is a part of our ongoing investigation into generation of effective surrogates and document summaries based on search topics and user interactions with information

Information foraging through the analysis of semantic network topology

Information seekers are posed with multiple challenges in gathering an unbiased and comprehensive body of information. The costs of analyzing documents often drive searches toward a small subset of documents. Additionally, modern search tools may reinforce the confirmation bias of users by providing only those documents that closely match their search query. The end result is a decision or hypothesis that is ill-considered and substantiated by potentially biased information. Information seekers need an information foraging tool that can help them explore the document corpus to find relevant topics and text snippets, while finding the hidden information that may be buried in the corpus or may not have been known a priori. An automated information foraging tool can mitigate these challenges by automatically identifying a wide breadth of topics for the user, extracted directly from a document corpus. When documents are decomposed and reconstituted into a semantic network, there is value in the topological structures formed. Leveraging a suite of information retrieval and graph analysis algorithms that analyze the semantic network, a framework is defined for assisting information seekers in both exploring and exploiting relevant information from a corpus to support unbiased decision making