Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Lauren L. Saling

dblp:65/757 · DBLP profile ↗
← Back
2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0003-0722-6232ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 100%
Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval
information seeking
0.612022
Where Do Queries Come From? · SIGIR 2022
Information retrieval
query formulation
0.612022
Where Do Queries Come From? · SIGIR 2022
Information retrieval › query understanding
query variation
0.612022
Where Do Queries Come From? · SIGIR 2022
Computational social science and digital humanities
online behavior
0.012000
Potential determinants of heavier internet usage · Int. J. Hum. Comput. Stud. 2000

Methods — techniques the papers use, named apart from their topics

small experiments · 0.6literature review · 0.6
YearPublicationVenuePosition
2022 Where Do Queries Come From?
abstract
Where do queries -- the words searchers type into a search box -- come from? The Information Retrieval community understands the performance of queries and search engines extensively, and has recently begun to examine the impact of query variation, showing that different queries for the same information need produce different results. In an information environment where bad actors try to nudge searchers toward misinformation, this is worrisome. The source of query variation -- searcher characteristics, contextual or linguistic prompts, cognitive biases, or even the influence of external parties -- while studied in a piecemeal fashion by other research communities has not been studied by ours. In this paper we draw on a variety of literatures (including information seeking, psychology, and misinformation), and report some small experiments to describe what is known about where queries come from, and demonstrate a clear literature gap around the source of query variations in IR. We chart a way forward for IR to research, document and understand this important question, with a view to creating search engines that provide more consistent, accurate and relevant search results regardless of the searcher's framing of the query.
Marwah Alaofi, Luke Gallagher, Dana McKay, Lauren L. Saling, Mark Sanderson, Falk Scholer, Damiano Spina, Ryen W. White
SIGIR4
2000 Potential determinants of heavier internet usage
Lynette Armstrong, James G. Phillips, Lauren L. Saling
Int. J. Hum. Comput. Stud.3