Everyday Search Research Articles

Associating cognitive abilities with naturalistic search behavior

AbstractDifferences in cognitive abilities affect search behaviors, but this has mostly been observed in laboratory experiments. There is limited research on how users search for information in real‐world, naturalistic settings and how real‐world search behaviors relate to cognitive abilities. In this study, we investigated a wide range of behavioral data captured from real‐life search tasks, their association with users' cognitive abilities, and the potential for automatically inferring cognitive abilities from these data. Furthermore, we aimed to determine the data quantity and monitoring duration needed to effectively estimate cognitive abilities from naturalistic behavior. Twenty individuals with βvarying cognitive abilities participated in the experiments in which their everyday search behavior was continuously recorded for 14 days. Their cognitive ability was evaluated through standard tests conducted individually. Data consisted of over 800 h of monitoring, including 2022 queries extracted from 1,442,447 screen frames and associated operating system logs. Using these data, naturalistic search behaviors were associated with cognitive abilities, and predictive models were trained. The results showed that lower selective attention was found to be associated with longer dwelling on selected search results. Faster psychomotor speed and higher fluid intelligence were found to be associated with a greater amount of text read on selected pages. Predictive models exhibited small error rates in predicting cognitive abilities.

Searching for creativity: How people search to generate new ideas

AbstractCreativity is one of the critical skills for people in a variety of academic, work, and everyday life contexts. Searching for information is essential to the creative process. Despite the recent increased attention in research on information searching for creative tasks, there is still little understanding of how people search for information to generate novel and useful ideas. This study aims to address three key research questions: (1) What search processes do people engage in while completing creative tasks, (2) what creative thinking strategies are employed when searching to generate ideas, and (3) what challenges do people encounter while searching for creative tasks. The data were collected at a university in the United States using multiple methods, combining pre‐task interviews, search sessions that involved the generation of new ideas, and post‐task interviews. Drawing from the data analysis from 31 interviews and search sessions, we present a conceptual framework for information searching for creative tasks across academic and everyday search contexts. Our findings highlight exploration as a critical search activity when searching to generate ideas. The results of this study enhance our understanding of the relationship between search activities and creative thinking strategies.

Data Mining Technics and Cyber Hygiene Behaviors in Social Media

The authors present in this work information about social media and data mining usage for that. It is represented information about social networking sites, where Facebook dominates the industry by boasting an account of 85% of the internet user’s worldwide. Applying data mining techniques to large social media data sets has the potential to continue to improve search results for everyday search engines, realize specialized target marketing for businesses, help psychologist study behavior, provide new insights into social structure for sociologists, personalize web services for consumers, and even help detect and prevent spam for all of us. The most common data mining applications related to social networking sites is represented. Authors have also gave information about different data mining techniques and list of these techniques. It is important to protect personal privacy when working with social network data. Recent publications highlight the need to protect privacy as it has been shown that even anonymizing this type of data can still reveal personal information when advanced data analysis techniques are used. A whole range of different threat of social networks is represented. Authors explain cyber hygiene behaviors in social networks, such as backing up data, identity theft and online behavior.

Towards Interactive Search: Investigating Visual Search in a Novel Real-World Paradigm.

An overwhelming majority of studies on visual search and selective attention were conducted using computer screens. There are arguably shortcomings in transferring knowledge from computer-based studies to real-world search behavior as findings are based on viewing static pictures on computer screens. This does not go well with the dynamic and interactive nature of vision in the real world. It is crucial to take visual search research to the real world in order to study everyday visual search processes. The aim of the present study was to develop an interactive search paradigm that can serve as a “bridge” between classical computerized search and everyday interactive search. We based our search paradigm on simple LEGO® bricks arranged on tabletop trays to ensure comparability with classical computerized visual search studies while providing room for easily increasing the complexity of the search environment. We found that targets were grasped slower when there were more distractors (Experiment 1) and there were sizable differences between various search conditions (Experiment 2), largely in line with classical visual search research and revealing similarities to research in natural scenes. Therefore, our paradigm can be seen as a valuable asset complementing visual search research in an environment between computerized search and everyday search.

Searching PubMed to Retrieve Publications on the COVID-19 Pandemic: Comparative Analysis of Search Strings.

BackgroundSince it was declared a pandemic on March 11, 2020, COVID-19 has dominated headlines around the world and researchers have generated thousands of scientific articles about the disease. The fast speed of publication has challenged researchers and other stakeholders to keep up with the volume of published articles. To search the literature effectively, researchers use databases such as PubMed.ObjectiveThe aim of this study is to evaluate the performance of different searches for COVID-19 records in PubMed and to assess the complexity of searches required.MethodsWe tested PubMed searches for COVID-19 to identify which search string performed best according to standard metrics (sensitivity, precision, and F-score). We evaluated the performance of 8 different searches in PubMed during the first 10 weeks of the COVID-19 pandemic to investigate how complex a search string is needed. We also tested omitting hyphens and space characters as well as applying quotation marks.ResultsThe two most comprehensive search strings combining several free-text and indexed search terms performed best in terms of sensitivity (98.4%/98.7%) and F-score (96.5%/95.7%), but the single-term search COVID-19 performed best in terms of precision (95.3%) and well in terms of sensitivity (94.4%) and F-score (94.8%). The term Wuhan virus performed the worst: 7.7% for sensitivity, 78.1% for precision, and 14.0% for F-score. We found that deleting a hyphen or space character could omit a substantial number of records, especially when searching with SARS-CoV-2 as a single term.ConclusionsComprehensive search strings combining free-text and indexed search terms performed better than single-term searches in PubMed, but not by a large margin compared to the single term COVID-19. For everyday searches, certain single-term searches that are entered correctly are probably sufficient, whereas more comprehensive searches should be used for systematic reviews. Still, we suggest additional measures that the US National Library of Medicine could take to support all PubMed users in searching the COVID-19 literature.

Pick up your bricks! Interactive visual search in a familiar real-world environment

Research on visual search is overwhelmingly limited to participants sitting in front of a computer screen pressing buttons. But how well do these effects transfer to real-life situations? In this series of experiments, we gradually investigated the feasibility of using an interactive environment to learn about how people search. We used a real-world search task that almost everybody has been familiar with since childhood: Finding specific LEGO bricks among many distractors. In the first experiments, the bricks were mounted on gray plates to mirror classic computerized visual search tasks. A specific target brick was contained on each plate multiple times. As expected, the results indicate that participants performed worse when they had to search for a feature conjunction and when the target was less discriminable from the distractors. In the next experiments, the to-be-picked-up target bricks (and up to 200 distractors) were poured onto trays. The results show that the size of the target brick was the major indicator for search difficulty. Target shape and color had only limited influence on performance. In the final set of experiments, participants were instructed to assemble four similar objects out of multiple different bricks. This included a search for multiple different target bricks simultaneously. First results indicate that participants first looked for multiple target bricks at once and picked up all they could find. Then, they specifically looked for missing pieces to assembled one object after the other. Overall, this setup proved to be valuable to learn about participants’ every-day search strategies. We are convinced that using the LEGO environment will prove to be an important method in future visual search research as it offers the applicability and generalizability of familiar real-world environments while also providing a standardized search space.

The coping experiences of spouses of persons with dementia.

To explore how spouses who are caregivers to persons with dementia experience everyday life 6-12months after participation in the intervention condition in the Norwegian Cognitive Behavioural Therapy for Early Dementia (N-CORDIAL) study. There is no current medical curative treatment for cognitive impairment and dementia-associated conditions. These conditions will lead to an increased need for daily assistance and care, as well as greater stress for caregivers. The Norwegian CORDIAL Study is a manual-based randomised controlled trial based on the German CORDIAL study (cognitive rehabilitation and cognitive behavioural treatment for early dementia in Alzheimer's disease), consisting of cognitive rehabilitation, cognitive behavioural therapy and life review methods. A descriptive exploratory study with qualitative semi-structured in-depth interviews with six spouses of persons with dementia who had participated in the Norwegian CORDIAL study. The transcribed text was analysed by systematic text condensation. The main theme identified, "An everyday search for meaning," referred to the stress experienced by the caregivers and their coping strategies in everyday life. The caregivers had gained increased awareness of coping strategies in everyday life 6-12months after the intervention. Caregivers who experienced fewer neuropsychiatric symptoms in their spouses with dementia benefitted most from the intervention. The study is relevant to clinical practice as interventions focusing on how to improve coping strategies may reduce caregiver stress and contribute to postponement of nursing home placement.

Collaborative information seeking in student group projects

Purpose The purpose of this paper is to gain a better understanding of the collaborative information seeking (CIS) behaviors of students conducting authentic group work projects, and the features of a collaborative search system that are most useful to these students. Design/methodology/approach An exploratory study was conducted with 41 participants in ten groups working on an in class, for-credit group project assignment utilizing a collaborative search system. Quantitative and qualitative data were gathered on the everyday search practices of students over the course of the group project, along with quality scores for the sources found. Findings Results showed that student behavior during their CIS related to the quality of their search outcomes, as the effective and efficient searchers found better quality sources. Students’ pre-task attitudes and experiences toward group work also relate to the quality of their search outcomes. Student feedback demonstrated the importance of making collaborative search tools convenient, lightweight, and easy to use. Practical implications These findings may be useful to researchers designing and studying the effectiveness of collaborative search tools, and to instructors planning to incorporate group projects into their classes. Originality/value In this paper, the authors document the authentic behaviors and attitudes of students conducting group projects in an classroom setting, and offer specific recommendations for developers of collaborative search systems. These findings provide greater context for CIS research into the collaborative search behaviors of students conducting group work projects.

Attending to multiple objects relies on both feature- and dimension-based control mechanisms: Evidence from human electrophysiology.

Numerous everyday search tasks require humans to attentionally select and temporally store more than one object present in the visual environment. Recently, several enumeration studies sought to isolate the mechanisms underlying multiple object processing by means of electrophysiological measures, which led to a more fine-grained picture as to which processing stages are modulated by object numerosity. One critical limitation that most of these studies share is that they used stimulus designs in which multiple targets were exclusively defined by the same feature value. Accordingly, it remains an open issue whether these findings generalize to search scenarios in which multiple targets are physically not identical. To systematically address this issue, we introduced three target context conditions in which multiple targets were defined randomly by (1) the same feature (sF), (2) different features within the same dimension (dFsD), or (3) different features across dimensions (dD). Our findings revealed that participants' ability to enumerate multiple targets was remarkably influenced by inter-target relationships, with fastest responses for sF trials, slowest responses for dD trials, and responses of intermediate speed for dFsD trials. Our electrophysiological analyses disclosed that one source of this response slowing was feature-based and originated from the stage of attentional selection (as indexed by PCN waves), whereas another source was dimension-based and associated with working memory processes (as indexed by P3b waves). Overall, our results point to a significant role of physical inter-target relationships in multiple object processing-a factor that has been largely neglected in most studies on enumeration.

Introduction

Introdution Wikipedia is one of the most visited knowledge resources in the world. The Alexa traffic rankings put it at number 7, well above the New York Times (104), the BBC (106), the Library of Congress (1,175), and the venerable Encyclopedia Britannica (3711) (Alexa, 2016). As historian Roy Rosenzweig puts it, Wikipedia has become "perhaps the largest work of online historical writing, the most widely read work of digital history, and the most important free historical resource on the World Wide Web" (Rosenzweig 2006, p. 52). Wikipedia has become so ingrained in our everyday search for information that users rarely give thought to the mechanisms and agency underneath its production of knowledge: who produces its content? And what visible and invisible structures govern this production? Indeed, we have come to take its presence for granted to a degree that editorial contributions to many Wikipedia pages are in fact stagnating (Wikipedia, n.p.; Ford, 2011).

Why do people google movement disorders? An infodemiological study of information seeking behaviors.

Millions of people worldwide everyday search Google or Wikipedia to look for health-related information. Aim of this study was to evaluate and interpret web search queries for terms related to movement disorders (MD) in English-speaking countries and their changes over time. We analyzed information regarding the volume of online searches in Google and Wikipedia for the most common MD and their treatments. We determined the highest search volume peaks to identify possible relation with online news headlines. The volume of searches for some queries related to MD entered in Google enormously increased over time. Most queries were related to definition, subtypes, symptoms and treatment (mostly to adverse effects, or alternatively, to possible alternative treatments). The highest peaks of MD search queries were temporally related to news about celebrities suffering from MD, to specific mass-media events or to news concerning pharmaceutic companies or scientific discoveries on MD. An increasing number of people use Google and Wikipedia to look for terms related to MD to obtain information on definitions, causes and symptoms, possibly to aid initial self-diagnosis. MD information demand and the actual prevalence of different MDs do not travel together: web search volume may mirrors patients' fears and worries about some particular disorders perceived as more serious than others, or may be driven by release of news about celebrities suffering from MD, "breaking news" or specific mass-media events regarding MD.

Building event-centric knowledge graphs from news

Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations–e.g. a celebrity’s birth date, occupation and family members–obtained from large repositories such as Freebase or Wikipedia.In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs.We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user’s understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts.

Building Event-Centric Knowledge Graphs from News

Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations–e.g. a celebrity’s birth date, occupation and family members–obtained from large repositories such as Freebase or Wikipedia. In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs. We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user’s understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts.

Comparison of Research Speed and Accuracy Using Westlaw Classic and WestlawNext

In 2010, Thomson Reuters released WestlawNext powered by WestSearch, a novel and proprietary algorithm. WestlawNext represents a new approach to legal research platform design. This article empirically examines the differences in search results using Westlaw Classic and WestlawNext by testing law students and librarians in both systems. Results demonstrate that researchers complete everyday searches faster and more accurately using WestlawNext. However, WestSearch's unique features and Thomson Reuters' failure to explain the function behind the WestlawNext main search bar and the WestSearch algorithm has limited researchers' ability to understand and effectively use WestlawNext. This lack of understanding has significant implications for instruction.