10,558 research outputs found
Being Omnipresent To Be Almighty: The Importance of The Global Web Evidence for Organizational Expert Finding
Modern expert nding algorithms are developed under the
assumption that all possible expertise evidence for a person
is concentrated in a company that currently employs the
person. The evidence that can be acquired outside of an
enterprise is traditionally unnoticed. At the same time, the
Web is full of personal information which is sufficiently detailed to judge about a person's skills and knowledge. In this work, we review various sources of expertise evidence out-side of an organization and experiment with rankings built on the data acquired from six dierent sources, accessible through APIs of two major web search engines. We show that these rankings and their combinations are often more realistic and of higher quality than rankings built on organizational data only
Understanding Mobile Search Task Relevance and User Behaviour in Context
Improvements in mobile technologies have led to a dramatic change in how and
when people access and use information, and is having a profound impact on how
users address their daily information needs. Smart phones are rapidly becoming
our main method of accessing information and are frequently used to perform
`on-the-go' search tasks. As research into information retrieval continues to
evolve, evaluating search behaviour in context is relatively new. Previous
research has studied the effects of context through either self-reported diary
studies or quantitative log analysis; however, neither approach is able to
accurately capture context of use at the time of searching. In this study, we
aim to gain a better understanding of task relevance and search behaviour via a
task-based user study (n=31) employing a bespoke Android app. The app allowed
us to accurately capture the user's context when completing tasks at different
times of the day over the period of a week. Through analysis of the collected
data, we gain a better understanding of how using smart phones on the go
impacts search behaviour, search performance and task relevance and whether or
not the actual context is an important factor.Comment: To appear in CHIIR 2019 in Glasgow, U
Events and Controversies: Influences of a Shocking News Event on Information Seeking
It has been suggested that online search and retrieval contributes to the
intellectual isolation of users within their preexisting ideologies, where
people's prior views are strengthened and alternative viewpoints are
infrequently encountered. This so-called "filter bubble" phenomenon has been
called out as especially detrimental when it comes to dialog among people on
controversial, emotionally charged topics, such as the labeling of genetically
modified food, the right to bear arms, the death penalty, and online privacy.
We seek to identify and study information-seeking behavior and access to
alternative versus reinforcing viewpoints following shocking, emotional, and
large-scale news events. We choose for a case study to analyze search and
browsing on gun control/rights, a strongly polarizing topic for both citizens
and leaders of the United States. We study the period of time preceding and
following a mass shooting to understand how its occurrence, follow-on
discussions, and debate may have been linked to changes in the patterns of
searching and browsing. We employ information-theoretic measures to quantify
the diversity of Web domains of interest to users and understand the browsing
patterns of users. We use these measures to characterize the influence of news
events on these web search and browsing patterns
Concept-based Interactive Query Expansion Support Tool (CIQUEST)
This report describes a three-year project (2000-03) undertaken in the Information Studies
Department at The University of Sheffield and funded by Resource, The Council for
Museums, Archives and Libraries. The overall aim of the research was to provide user
support for query formulation and reformulation in searching large-scale textual resources
including those of the World Wide Web. More specifically the objectives were: to investigate
and evaluate methods for the automatic generation and organisation of concepts derived from
retrieved document sets, based on statistical methods for term weighting; and to conduct
user-based evaluations on the understanding, presentation and retrieval effectiveness of
concept structures in selecting candidate terms for interactive query expansion.
The TREC test collection formed the basis for the seven evaluative experiments conducted in
the course of the project. These formed four distinct phases in the project plan. In the first
phase, a series of experiments was conducted to investigate further techniques for concept
derivation and hierarchical organisation and structure. The second phase was concerned with
user-based validation of the concept structures. Results of phases 1 and 2 informed on the
design of the test system and the user interface was developed in phase 3. The final phase
entailed a user-based summative evaluation of the CiQuest system.
The main findings demonstrate that concept hierarchies can effectively be generated from
sets of retrieved documents and displayed to searchers in a meaningful way. The approach
provides the searcher with an overview of the contents of the retrieved documents, which in
turn facilitates the viewing of documents and selection of the most relevant ones. Concept
hierarchies are a good source of terms for query expansion and can improve precision. The
extraction of descriptive phrases as an alternative source of terms was also effective. With
respect to presentation, cascading menus were easy to browse for selecting terms and for
viewing documents. In conclusion the project dissemination programme and future work are
outlined
Search Bias Quantification: Investigating Political Bias in Social Media and Web Search
Users frequently use search systems on the Web as well as online social media to learn about ongoing events and public opinion on personalities. Prior studies have shown that the top-ranked results returned by these search engines can shape user opinion about the topic (e.g., event or person) being searched. In case of polarizing topics like politics, where multiple competing perspectives exist, the political bias in the top search results can play a significant role in shaping public opinion towards (or away from) certain perspectives. Given the considerable impact that search bias can have on the user, we propose a generalizable search bias quantification framework that not only measures the political bias in ranked list output by the search system but also decouples the bias introduced by the different sources—input data and ranking system. We apply our framework to study the political bias in searches related to 2016 US Presidential primaries in Twitter social media search and find that both input data and ranking system matter in determining the final search output bias seen by the users. And finally, we use the framework to compare the relative bias for two popular search systems—Twitter social media search and Google web search—for queries related to politicians and political events. We end by discussing some potential solutions to signal the bias in the search results to make the users more aware of them.publishe
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