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

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dc.contributor.authorAl-Ramahi, Mohammad
dc.contributor.authorAlsmadi, Izzat
dc.date.accessioned2020-01-04T07:39:59Z
dc.date.available2020-01-04T07:39:59Z
dc.date.issued2020-01-07
dc.description.abstractThe 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.
dc.format.extent9 pages
dc.identifier.doi10.24251/HICSS.2020.304
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/64046
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectData Analytics, Data Mining and Machine Learning for Social Media
dc.subjectdata analytics
dc.subjectonline social network
dc.subjectquora insincere questions
dc.titleUsing Data Analytics to Filter Insincere Posts from Online Social Networks A Case Study: Quora Insincere Questions
dc.typeConference Paper
dc.type.dcmiText

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