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.


The following people have participated/are participating in this project:

Related Publications

  1. 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.
  2. 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).
  3. 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.
  4. 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.
  5. 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).


Last change: February 4, 2016 /