Search behaviour before and after search success

Why do users continue searching after reviewing all relevant documents with which they could have completed a work task? If we knew the answer, then a search system may be able to help users learn about their current search processes, which in turn may enable them to make the whole search process more efficient, leading to greater effectiveness and user satisfaction. This paper is a first step towards solving this problem. Using a previously collected data set, we identified the point of success and hence task completion, and investigated the search behaviour before and after users had accessed all relevant documents for answering assigned tasks. We used a set of search behaviour actions derived from Marchionini's (1995) Information Seeking Process model, and modeled the distribution of these actions throughout the entire search process, comparing actions before and after success could have been attained. Our results suggest that six defined actions, namely user-submitted query, system-suggested query, forward to items, evaluate relevant items, reflect, and answer appeared to change according to the stage of the entire search process. Also, users have notably distinct patterns before and after search success was obtained, but not realised by the user. Not all action were affected; user-submitted query and system-suggested query appeared to be unaffected by time in post-success case and presuccess case, respectively

Understanding engagement through search behaviour

Evaluating user engagement with search is a critical aspect of understanding how to assess and improve information retrieval systems. While standard techniques for measuring user engagement use questionnaires, these are obtrusive to user interaction, and can only be collected at acceptable intervals. The problem we address is whether there is a less obtrusive and more automatic way to assess how users perceive the search process and outcome. Log files collect behavioural signals (e.g., clicks, queries) from users on a large scale. In this paper, we investigate the potential to predict how users perceive engagement with search by modelling behavioural signals from log files using supervised learning methods.We focus on different engagement dimensions (Perceived Usability, Felt Involvement, Endurability and Novelty) and examine how 37 behavioural features can inform these dimensions. Our results, obtained from 377 in-lab participants undergoing goal-based search tasks, support the connection between perceived engagement and search behaviour. More specifically, we show that time-and query-related features are best suited for predicting user perceived engagement, and suggest that different behavioural features better reflect specific dimensions. We demonstrate the possibility of predicting user-perceived engagement using search behavioural features

The relationship between user perception and user behaviour in interactive information retrieval evaluation

Measures of user behaviour and user perception have been used to evaluate interactive information retrieval systems. However, there have been few efforts taken to understand the relationship between these two. In this paper, we investigated both using user actions from log files, and the results of the User Engagement Scale, both of which came from a study of people interacting with a novel interface to an image collection, but with a non-purposeful task. Our results suggest that selected behavioural actions are associated with selected user perceptions (i.e., focused attention, felt involvement, and novelty), while typical search and browse actions have no association with aesthetics and perceived usability. This is a novel finding that can lead toward a more systematic usercentered evaluation

How do user opinions influence their interaction with web search results?

Understanding the influence of users' opinions on their search behavior together with their inherent biases in web search has garnered widespread interest in recent times. This is largely due to the implications of promoting critical thinking, explaining phenomena such as political polarization, or the manifestation of echo chambers. It is important to understand how personal opinions can bias users' interaction with search results. Moreover, there is a lack of understanding of the impact of user search intents, namely non-purposeful browsing versus searching with a pre-defined goal, on users' interactions with search results. We take a step towards bridging this knowledge gap through an empirical study in this paper. To do so, we select two controversial topics in abortion and gun control, and invite users to learn about them through ĝ€ Purposeless' and ĝ€ Purposeful' web searching. Our findings suggest that users with strong personal opinions exhibit biased interactions with the search results. However, the effect of users' opinions on their interactions with search results can differ depending on whether users search purposelessly or with a purpose. Our findings advance the current understanding of the effect of users' opinions in web search sessions, and show that users' search intents shape their interaction with search results. This work has broad design implications on dealing with bias in interactive information retrieval systems.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Web Information System

High Yield Preparation Method of Thermally Stable Cellulose Nanofibers

The preparation of nanocellulose fibers (NFs) is achieved through pretreating cellulose in a NaOH/urea/thiourea solution, and then defibrillating the fibers through ultrasonication, resulting in a high yield of 85.4%. Extensive work has been done to optimize the preparation parameters. The obtained NFs are about 30 nm in diameter with cellulose II crystal structure. They possess high thermal stability with an onset of thermal degradation at 270 °C and a maximum degradation temperature of 370 °C. Such NFs have potential applications in transistors and batteries with high thermal stability. NFs-H were obtained by homogenizing undefibrillated fibers separated from the preparation of NFs. NFs-H were also in cellulose II crystal form but with lower thermal stability due to low crystallinity. They can be applied to make highly transparent paper

Cyanogel-Based Preparation of Amorphous NiFe Nanoaggregates with Enhanced Activity and Stability for OER

The development of cost-efficient electrocatalysts for oxygen evolution reaction (OER) with high efficiency is crucial to widespread applications of water splitting for hydrogen production. In this work, porous three-dimensional (3D) amorphous NiFe nanoaggregates composed of interconnected nanograins were synthesized by a cyanogel-based wet chemical reduction method using the NiCl2/Na4Fe(CN)6 cyanogel as the precursor and NaBH4 as the reducing agent. The influence of the incorporated Fe amount was carefully studied by slightly changing the feeding molar ratios of the Ni/Fe atoms in the precursors. The intrinsic 3D backbone structure of the cyanogel resulted in crystal nuclei tending to generate along with the backbones, which is key to the formation of NiFe nanoaggregates with a porous 3D interconnected structure. The synthesized NiFe nanoaggregates with a 3D interconnected structure and high porosity, as well as the incorporation of Fe, are in favor of high surface area, more active sites, and abundant oxygen vacancies, leading to superior activity and stability of OER in alkaline electrolytes with a low overpotential of 0.35 V at 10 mA cm−2, a high current density of 24.8 mA cm−2 at 1.65 V, a small Tafel slope of 76.9 mV dec−1, and attractive durability in 1 M KOH solution