Xi Niu

Effects of popularity and quality on the usage of query suggestions during information search

Many search systems provide users with recommended queries during online information seeking. Although usage statistics are often used to recommend queries, this information is usually not displayed to the user. In this study, we investigate how the presentation of this information impacts use of query suggestions. Twenty-three subjects used an experimental search system to find documents about four topics. Eight query suggestions were provided for each topic: four were high quality queries and four were low quality queries. Fake usage information indicating how many other people used the queries was also provided. For half the queries this information was high and for the other half this information was low. Results showed that subjects could distinguish between high and low quality queries and were not influenced by the usage information. Qualitative data revealed that subjects felt favorable about the suggestions, but the usage information was less important for the search task used in this study.

The use of query suggestions during information search

Query suggestion is a common feature of many information search systems. While much research has been conducted about how to generate suggestions, fewer studies have been conducted about how people interact with and use suggestions. The purpose of this paper is to investigate how and when people integrate query suggestions into their searches and the outcome of this usage. The paper further investigates the relationships between search expertise, topic difficulty, and temporal segment of the search and query suggestion usage. A secondary analysis of data was conducted using data collected in a previous controlled laboratory study. In this previous study, 23 undergraduate research participants used an experimental search system with query suggestions to conduct four topic searches. Results showed that participants integrated the suggestions into their searching fairly quickly and that participants with less search expertise used more suggestions and saved more documents. Participants also used more suggestions towards the end of their searches and when searching for more difficult topics. These results show that query suggestion can provide support in situations where people have less search expertise, greater difficulty searching and at specific times during the search.

Study of User Search Activities With Two Discovery Tools at an Academic Library

The goal of this study was to investigate and compare user search activities of 2 discovery tools at an academic library. The implementation of a new discovery tool (Primo by Ex Libris) to replace an existing system (VuFind) provided a unique opportunity to collect transaction logs of both systems and examine user search behavior in an empirical test. Results from a transaction log analysis and a user study of this study have contributed to the understanding of users’ search behavior and their preferences and perceptions of the two systems. We find both commonalities and differences between VuFind and Primo for users’ interactions. The combination use of the transaction log analysis and user study could be applied to other similar search systems assessments.

Library assessment and data analytics in the big data era: practice and policies

Emerging technologies have offered libraries and librarians new ways and methods to collect and analyze data in the era of accountability to justify their value and contributions. For example, Gallagher, Bauer and Dollar ( ) analyzed the paper and online journal usage from all possible data sources and discovered that users at the Yale Medical Library preferred the electronic format of articles to the print version. After this discovery, they were able to take necessary steps to adjust their journal subscriptions. Many library professionals advocate such data‐driven library management to strengthen and specify library budget proposals.

TTPDrill: Automatic and Accurate Extraction of Threat Actions from Unstructured Text of CTI Sources

With the rapid growth of the cyber attacks, sharing of cyber threat intelligence (CTI) becomes essential to identify and respond to cyber attack in timely and cost-effective manner. However, with the lack of standard languages and automated analytics of cyber threat information, analyzing complex and unstructured text of CTI reports is extremely time- and labor-consuming. Without addressing this challenge, CTI sharing will be highly impractical, and attack uncertainty and time-to-defend will continue to increase. Considering the high volume and speed of CTI sharing, our aim in this paper is to develop automated and context-aware analytics of cyber threat intelligence to accurately learn attack pattern (TTPs) from commonly available CTI sources in order to timely implement cyber defense actions. Our paper has three key contributions. First, it presents a novel threat-action ontology that is sufficiently rich to understand the specifications and context of malicious actions. Second, we developed a novel text mining approach that combines enhanced techniques of Natural Language Processing (NLP) and Information retrieval (IR) to extract threat actions based on semantic (rather than syntactic) relationship. Third, our CTI analysis can construct a complete attack pattern by mapping each threat action to the appropriate techniques, tactics and kill chain phases, and translating it any threat sharing standards, such as STIX 2.1. Our CTI analytic techniques were implemented in a tool, called TTPDrill, and evaluated using a randomly selected set of Symantec Threat Reports. Our evaluation tests show that TTPDrill achieves more than 82% of precision and recall in a variety of measures, very reasonable for this problem domain.

Computational Surprise in Information Retrieval

The concept of surprise is central to human learning and development. However, compared to accuracy, surprise has received little attention in the IR community, yet it is an essential component of the information seeking process. This workshop brings together researchers and practitioners of IR to discuss the topic of computational surprise, to set a research agenda, and to examine how to build datasets for research into this fascinating topic. The themes in this workshop include discussion of what can be learned from some well-known surprise models in other fields, such as Bayesian surprise; how to evaluate surprise based on user experience; and how computational surprise is related to the newly emerging areas, such as fake news detection, computational contradiction, clickbait detection, etc.

Implementing and Evaluating Serendipity in Delivering Personalized Health Information

Serendipity has been recognized to have the potential of enhancing unexpected information discovery. This study shows that decomposing the concept of serendipity into unexpectedness and interest is a useful way for implementing this concept. Experts’ domain knowledge helps in providing serendipitous recommendation, which can be further improved by adaptively incorporating users’ real-time feedback. This research also conducts an empirical user-study to analyze the influence of serendipity in a health news delivery context. A personalized filtering system named MedSDFilter was developed, on top of which serendipitous recommendation was implemented using three approaches: random, static-knowledge-based, and adaptive-knowledge-based models. The three different models were compared. The results indicate that the adaptive-knowledge-based method has the highest ability in helping people discover unexpected and interesting contents. The insights of the research will make researchers and practitioners rethink the way in which search engines and recommender systems operate to address the challenges of discovering unexpected and interesting information. The outcome will have implications for empowering ordinary people with more chances of bumping into beneficial information.

Search in One’s Hand: How Users Search a Mobile Library Catalog

With the rapid growth of mobile devices, mobile websites become an important channel of library resources and services. The mobile catalog is often significantly different from its desktop version in interface and features, but few studies of library catalog search behavior have been focused on mobile catalog searches. We present a study on user search behavior with a mobile library catalog based on transaction log analysis. We compared mobile and desktop catalog search behaviors and highlighted the similarities and differences, which could provide important evidence for improving mobile library catalogs’ search performance and usability.

CORPUS: Next-Generation Online Platform for Research Collaborations in Humanities

Two major research resources for humanities scholars are manuscripts and scholarly editions (rigorously reconstituted standard texts of seminal writers and thinkers). However, most of these resources either have not been digitized or are not easy to access online [1]. Consequently, scholars frequently need to spend unnecessary time and effort to find and manage different versions of materials (physical or digital) from different sources. To solve this problem, we propose an online platform called CORPUS – a Collaborative Online Research Platform for Users of Scholarly edition – to support scholarly research online in an efficient manner. CORPUS aims to integrate different types of research materials in the humanities (manuscripts, scholarly editions, online publications, and personal notes) and aggregate different versions of the same texts. In addition, it enhances collaboration among scholars while also providing them with a peer-review-based incentive to share and publish their research work.

Collaborative online research platform for scholars in humanities

Manuscripts and scholarly editions are essential resources for humanities researchers. However, most of those resources do not exist in digital form or are not easy to access online. This forces scholars to spend unnecessary time and effort to conduct research on different versions of materials (physical and digital) from different sources. To solve this problem, we propose CORPUS -- a Collaborative Online Research Platform for Users of Scholarly editions -- to support scholarly research online in an efficient manner. To design CORPUS, we conducted contextual inquiries with 10 scholars in philosophy to collect user requirements and generate design ideas. An interactive prototype was developed based on the user requirements. Finally, we conducted a formal evaluation study with the same 10 scholars to test the usability of CORPUS.