Please use this identifier to cite or link to this item:

Collective Classification for Social Media Credibility Estimation

File Size Format  
0222.pdf 2 MB Adobe PDF View/Open

Item Summary

Title:Collective Classification for Social Media Credibility Estimation
Authors:O'Brien, Kyle
Simek, Olga
Waugh, Frederick
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
Digital and Social Media
collective classification, credibility, heterogeneous graphs, social media
Date Issued:08 Jan 2019
Abstract:We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media.
Pages/Duration:9 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons