Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/64046

Using Data Analytics to Filter Insincere Posts from Online Social Networks A Case Study: Quora Insincere Questions

File Size Format  
0245.pdf 446.53 kB Adobe PDF View/Open

Item Summary

Title:Using Data Analytics to Filter Insincere Posts from Online Social Networks A Case Study: Quora Insincere Questions
Authors:Al-Ramahi, Mohammad
Alsmadi, Izzat
Keywords:Data Analytics, Data Mining and Machine Learning for Social Media
data analytics
online social network
quora insincere questions
Date Issued:07 Jan 2020
Abstract:The internet in general and Online Social Networks (OSNs) in particular continue to play a significant role in our life where information is massively uploaded and exchanged. With such high importance and attention, abuses of such media of communication for different purposes are common. Driven by goals such as marketing and financial gains, some users use OSNs to post their misleading or insincere content. In this context, we utilized a real-world dataset posted by Quora in Kaggle.com to evaluate different mechanisms and algorithms to filter insincere and spam contents. We evaluated different preprocessing and analysis models. Moreover, we analyzed the cognitive efforts users made in writing their posts and whether that can improve the prediction accuracy. We reported the best models in terms of insincerity prediction accuracy.
Pages/Duration:9 pages
URI:http://hdl.handle.net/10125/64046
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.304
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Data Analytics, Data Mining and Machine Learning for Social Media


Please email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons