Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

11-2020

Abstract

Community driven social media sites are rich sources of knowledge and entertainment and at the same vulnerable to the flames or toxic content that can be dangerous to various users of these platforms as well as to the society. Therefore, it is crucial to identify and remove such content to have a better and safe online experience. Manually eliminating flames is tedious and hence many research works focus on machine learning or deep learning models for automated methods. In this paper, we primarily focus on detecting the insincere content using neural network-based learning methods. We also integrated the profanity features as profanity is correlated with honesty according to psychology research. We tested our model on the questions datasets from CQA platform to detect the insincere content. Our integrated neural network model enabled us to achieve a high performance of F1-score, 94.01%, compared to the standard machine learning algorithms

Keywords

Social media, insincere content, profanity, neural networks, classification.

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2020 11th IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON): November 4-7, Virtual: Proceedings

First Page

41

Last Page

47

ISBN

9781728184166

Identifier

10.1109/IEMCON51383.2020.9284844

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

1-24-2021

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/IEMCON51383.2020.9284844

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