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

Ryosuke Nagura

dblp:63/2028 · DBLP profile ↗
← Back
1ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

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

Databases, data management, data science and information retrieval · 1 · 1 first-author

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
Web and social media mining · 56% Information retrieval · 44%

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

TopicWeightPapersLastEvidence papers
Information retrieval › web search › web information retrieval › web content quality
credibility assessment
0.112006
A method of rating the credibility of news documents on the web · SIGIR 2006
Web and social media mining › news analysis
news article analysis
0.112006
A method of rating the credibility of news documents on the web · SIGIR 2006
Web and social media mining › web mining
web document analysis
0.112006
A method of rating the credibility of news documents on the web · SIGIR 2006
Information retrieval › document retrieval › domain-specific retrieval
news retrieval
0.012006
A method of rating the credibility of news documents on the web · SIGIR 2006
Information retrieval
web search
0.012006
A method of rating the credibility of news documents on the web · SIGIR 2006

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

subjectivity detection · 0.1content agreement analysis · 0.1
YearPublicationVenuePosition
2006 A method of rating the credibility of news documents on the web
abstract
We propose a method to rate the of news articles using three clues: (1) commonality of the contents of articles among different news publishers; (2) numerical agreement versus contradiction of numerical values reported in the articles; and (3) objectivity based on subjective speculative phrases and news sources. We tested this method on news stories taken from seven different news sites on the Web. The average agreement between the system-produced credibility and the manual judgments of three human assessors on the 52 sample articles was 69.1%. The limitations of the current approach and future directions are discussed.
Ryosuke Nagura, Yohei Seki, Noriko Kando, Masaki Aono
SIGIR1