19,161 research outputs found
Why We Read Wikipedia
Wikipedia is one of the most popular sites on the Web, with millions of users
relying on it to satisfy a broad range of information needs every day. Although
it is crucial to understand what exactly these needs are in order to be able to
meet them, little is currently known about why users visit Wikipedia. The goal
of this paper is to fill this gap by combining a survey of Wikipedia readers
with a log-based analysis of user activity. Based on an initial series of user
surveys, we build a taxonomy of Wikipedia use cases along several dimensions,
capturing users' motivations to visit Wikipedia, the depth of knowledge they
are seeking, and their knowledge of the topic of interest prior to visiting
Wikipedia. Then, we quantify the prevalence of these use cases via a
large-scale user survey conducted on live Wikipedia with almost 30,000
responses. Our analyses highlight the variety of factors driving users to
Wikipedia, such as current events, media coverage of a topic, personal
curiosity, work or school assignments, or boredom. Finally, we match survey
responses to the respondents' digital traces in Wikipedia's server logs,
enabling the discovery of behavioral patterns associated with specific use
cases. For instance, we observe long and fast-paced page sequences across
topics for users who are bored or exploring randomly, whereas those using
Wikipedia for work or school spend more time on individual articles focused on
topics such as science. Our findings advance our understanding of reader
motivations and behavior on Wikipedia and can have implications for developers
aiming to improve Wikipedia's user experience, editors striving to cater to
their readers' needs, third-party services (such as search engines) providing
access to Wikipedia content, and researchers aiming to build tools such as
recommendation engines.Comment: Published in WWW'17; v2 fixes caption of Table
Is Stack Overflow Overflowing With Questions and Tags
Programming question and answer (Q & A) websites, such as Quora, Stack
Overflow, and Yahoo! Answer etc. helps us to understand the programming
concepts easily and quickly in a way that has been tested and applied by many
software developers. Stack Overflow is one of the most frequently used
programming Q\&A website where the questions and answers posted are presently
analyzed manually, which requires a huge amount of time and resource. To save
the effort, we present a topic modeling based technique to analyze the words of
the original texts to discover the themes that run through them. We also
propose a method to automate the process of reviewing the quality of questions
on Stack Overflow dataset in order to avoid ballooning the stack overflow with
insignificant questions. The proposed method also recommends the appropriate
tags for the new post, which averts the creation of unnecessary tags on Stack
Overflow.Comment: 11 pages, 7 figures, 3 tables Presented at Third International
Symposium on Women in Computing and Informatics (WCI-2015
Exploratory Analysis of Highly Heterogeneous Document Collections
We present an effective multifaceted system for exploratory analysis of
highly heterogeneous document collections. Our system is based on intelligently
tagging individual documents in a purely automated fashion and exploiting these
tags in a powerful faceted browsing framework. Tagging strategies employed
include both unsupervised and supervised approaches based on machine learning
and natural language processing. As one of our key tagging strategies, we
introduce the KERA algorithm (Keyword Extraction for Reports and Articles).
KERA extracts topic-representative terms from individual documents in a purely
unsupervised fashion and is revealed to be significantly more effective than
state-of-the-art methods. Finally, we evaluate our system in its ability to
help users locate documents pertaining to military critical technologies buried
deep in a large heterogeneous sea of information.Comment: 9 pages; KDD 2013: 19th ACM SIGKDD Conference on Knowledge Discovery
and Data Minin
From Linked Data to Relevant Data -- Time is the Essence
The Semantic Web initiative puts emphasis not primarily on putting data on
the Web, but rather on creating links in a way that both humans and machines
can explore the Web of data. When such users access the Web, they leave a trail
as Web servers maintain a history of requests. Web usage mining approaches have
been studied since the beginning of the Web given the log's huge potential for
purposes such as resource annotation, personalization, forecasting etc.
However, the impact of any such efforts has not really gone beyond generating
statistics detailing who, when, and how Web pages maintained by a Web server
were visited.Comment: 1st International Workshop on Usage Analysis and the Web of Data
(USEWOD2011) in the 20th International World Wide Web Conference (WWW2011),
Hyderabad, India, March 28th, 201
Lucene4IR: Developing information retrieval evaluation resources using Lucene
The workshop and hackathon on developing Information Retrieval Evaluation Resources using Lucene (L4IR) was held on the 8th and 9th of September, 2016 at the University of Strathclyde in Glasgow, UK and funded by the ESF Elias Network. The event featured three main elements: (i) a series of keynote and invited talks on industry, teaching and evaluation; (ii) planning, coding and hacking where a number of groups created modules and infrastructure to use Lucene to undertake TREC based evaluations; and (iii) a number of breakout groups discussing challenges, opportunities and problems in bridging the divide between academia and industry, and how we can use Lucene for teaching and learning Information Retrieval (IR). The event was composed of a mix and blend of academics, experts and students wanting to learn, share and create evaluation resources for the community. The hacking was intense and the discussions lively creating the basis of many useful tools but also raising numerous issues. It was clear that by adopting and contributing to most widely used and supported Open Source IR toolkit, there were many benefits for academics, students, researchers, developers and practitioners - providing a basis for stronger evaluation practices, increased reproducibility, more efficient knowledge transfer, greater collaboration between academia and industry, and shared teaching and training resources
Towards Query Logs for Privacy Studies: On Deriving Search Queries from Questions
Translating verbose information needs into crisp search queries is a
phenomenon that is ubiquitous but hardly understood. Insights into this process
could be valuable in several applications, including synthesizing large
privacy-friendly query logs from public Web sources which are readily available
to the academic research community. In this work, we take a step towards
understanding query formulation by tapping into the rich potential of community
question answering (CQA) forums. Specifically, we sample natural language (NL)
questions spanning diverse themes from the Stack Exchange platform, and conduct
a large-scale conversion experiment where crowdworkers submit search queries
they would use when looking for equivalent information. We provide a careful
analysis of this data, accounting for possible sources of bias during
conversion, along with insights into user-specific linguistic patterns and
search behaviors. We release a dataset of 7,000 question-query pairs from this
study to facilitate further research on query understanding.Comment: ECIR 2020 Short Pape
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