Information Truthfulness Analysis Project
Truthfulness Analysis of Web Information
Project Overview
More and more Web users depend on the Web to acquire information.
Unfortunately, there is significant amount of untruthful information
on the Web. Reasons for having untruthful information include unintended
errors (e.g., typos) and intentionally-spread rumors for various improper
purposes. The fact that the Web is an open forum where everyone can
publish without prior review makes it easy for untruthful information
to enter the Web. Effective methods are needed to distinguish truthful
information from untruthful ones on the Web and to measure the
trustworthiness of information on the Web.
This project aims to develop an effective and systematic method to tackle
the above important issue by solving two related problems: the first is
the statement truthfulness verification problem and the second is the
document/website trustworthiness estimation problem. Given a fact statement
(i.e., it tries to state a fact), the former is to determine whether or not
the statement is truthful, and if it is not, find out the truthful
statement(s) most relevant to the given statement. The latter is to
estimate the trustworthiness of the information in a document or website
based on a number of factors including the (in)consistency/correctness of
data in the document/website.
Part of the project is being jointly carried out with Drs. Luna Dong,
Kenneth Lyons, and Divesh Srivastava from AT&T Research.
Participants
The following people have participated/are participating
in this project:
- Weiyi Meng (BU, Faculty)
- Xian Li (BU, PhD student)
- Liu Yang (BU, MS student)
- Clement Yu (UIC, Faculty)
- Eduard C. Dragut (Purdue University, PostDoc)
Related Publications
- Xian Li, Weiyi Meng, Clement Yu.
T-verifier: Verifying Truthfulness of Fact Statements.
IEEE International Conference on Data Engineering (ICDE), pp.63-74,
Hannover, Germany, April 2011.
- Xian Li, Weiyi Meng, Clement Yu.
Truthfulness Analysis of
Fact Statements Using the Web. IEEE Data Engineering Bulletin,
34(3), pp.3-10, September 2011 (invited).
- Xian Li, Xin Luna Dong, Kenneth B. Lyons, Weiyi Meng, and Divesh
Srivastava. Truth Finding
on the Deep Web: Is the Problem Solved? Proc. of the
VLDB Conference, pp.97-108, Riva del Garda, Trento, Italy, August 2013.
- Xian Li, Xin Luna Dong, Kenneth B. Lyons, Weiyi Meng, and Divesh
Srivastava. Scaling
Up Copy Detection. Proc. of the 31st IEEE International
Conference on Data Engineering (ICDE), pp.89-100, Seoul, Korea, April 2015.
- Xian Li, Weiyi Meng, Clement Yu. Verification of Fact
Statements with Multiple Truthful Alternatives. 12th International
Conference on Web Information Systems and Technologies (WEBIST), Rome,
Italy, April 2016 (to appear).
Dataset
Last change: February 4, 2016 / meng@cs.binghamton.edu