Towards an understanding of information credibility on online social networks

Loading...
Thumbnail Image
Authors
Sikdar, Sujoy Kumar
Issue Date
2015-05
Type
Electronic thesis
Thesis
Language
ENG
Keywords
Computer science
Research Projects
Organizational Units
Journal Issue
Alternative Title
Abstract
A related task is that of identifying what pieces of information published on the social network are true. One approach to solve this problem treats humans on the social network as sensors with unknown reliability who sense the state of the world and report their observations as claims by publishing messages. Fact finding algorithms use an unsupervised estimation theoretic approach to jointly estimate the truthfulness of claims and the reliability of the human sensors that make the claims given some prior beliefs. However, due to the sparseness of information available in Twitter streaming data, these algorithms have very little information to update the prior beliefs for claims corroborated by very few sources. We find that using simple heuristics in developing fusion methods to use the credibility predictions yields improvements in performance over the estimates reached by the fact finder alone.
Description
May 2015
School of Science
Full Citation
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Terms of Use
Journal
Volume
Issue
PubMed ID
DOI
ISSN
EISSN