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Credibility: Features Exploration and Credibility Prediction
| Venue: | in Advances in Information Retrieval |
| Citations: | 5 - 2 self |
BibTeX
@INPROCEEDINGS{Olteanu_credibility:features,
author = {Ra Olteanu and Stanislav Peshterliev and Xin Liu and Karl Aberer},
title = {Credibility: Features Exploration and Credibility Prediction},
booktitle = {in Advances in Information Retrieval},
year = {},
pages = {557--568}
}
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Abstract
Abstract. The open nature of the World Wide Web makes evaluating webpage credibility challenging for users. In this paper, we aim to auto-matically assess web credibility by investigating various characteristics of webpages. Specifically, we first identify features from textual content, link structure, webpages design, as well as their social popularity learned from popular social media sites (e.g., Facebook, Twitter). A set of sta-tistical analyses methods are applied to select the most informative fea-tures, which are then used to infer webpages credibility by employing supervised learning algorithms. Real dataset-based experiments under two application settings show that we attain an accuracy of 75 % for classification, and an improvement of 53 % for the mean absolute error (MAE), with respect to the random baseline approach, for regression.
Keyphrases
credibility prediction feature exploration webpage credibility various characteristic application setting random baseline approach informative fea-tures world wide web link structure social popularity textual content ass web credibility sta-tistical analysis method open nature popular social medium site real dataset-based experiment mean absolute error
