Al-Ramahi, MohammadAlsmadi, Izzat2020-01-042020-01-042020-01-07978-0-9981331-3-3http://hdl.handle.net/10125/64046The 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.9 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalData Analytics, Data Mining and Machine Learning for Social Mediadata analyticsonline social networkquora insincere questionsConference Paper10.24251/HICSS.2020.304