Unsupervised Deep Learning for Fake Content Detection in Social Media

dc.contributor.author Tao, Jie
dc.contributor.author Fang, Xing
dc.contributor.author Zhou, Lina
dc.date.accessioned 2020-12-24T19:01:11Z
dc.date.available 2020-12-24T19:01:11Z
dc.date.issued 2021-01-05
dc.description.abstract Fake content is ever increasing in the online environment, driven by various motivations such as gain-ing commercial and political advantages. The interactive and collaborative nature of social media further fuels the growth of fake content by exerting fast and widespread influence. Despite growing and interdisciplinary efforts in detecting fake content in social media, some common research challenges remain to be addressed such as humans’ cognitive bias and scarcity of labeled data for training supervised machine learning models. This study aims to tackle both challenges by developing unsupervised deep learning models for the detection of fake content in social media. In view that traditional linguistic features fail to capture context information, our proposed method learns feature representations from the context in social media content. The empirical evaluation results with fake comments from YouTube demonstrate that our proposed methods not only outperform baseline models with traditional unsupervised machine learning techniques, but also achieve comparable performance to the state-of-the-art supervised models. The proposed analytical pipeline provides an end-to-end solution to detecting fake social media contents, which largely reduce the human labor required in collaborative data science teams (i.e., particularly the data labeling). The findings of this study can be used to facilitate collaboration in data science by reducing humans’ cognitive bias and improve the collaboration efficiency.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2021.032
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70643
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Collaboration for Data Science
dc.subject deep learning
dc.subject natural language processing
dc.subject social media analytics
dc.subject unsupervised learning
dc.title Unsupervised Deep Learning for Fake Content Detection in Social Media
prism.startingpage 274
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
0028.pdf
Size:
1.28 MB
Format:
Adobe Portable Document Format
Description: