Aggressive Social Media Use Detection Based on Deep Learning

Aggressive Social Media Use Detection Based on Deep Learning

Amutha S., Umapriya T., Puspita Dash
Copyright: © 2023 |Pages: 13
DOI: 10.4018/978-1-6684-7679-6.ch002
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Abstract

The prevalence of cyber aggressive comments on social networks, particularly among adolescents, has been increasing steadily as users spend more time connecting with others, sharing information, and pursuing common interests. Recent research has explored various deep learning models for detecting cyber aggressive comments, leading to efficient identification mechanisms compared to standard methods. This chapter proposes a deep belief network model specifically designed for cyber aggressive detection in social media comments. The data pre-processing stage involves text cleaning, tokenization, stemming, lemmatization, and the removal of stop words. The cleaned textual data is then fed into a deep belief network model for prediction. The experimental results demonstrate the high accuracy achieved by the deep belief network model.
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Domain Study

Deep Learning

Deep learning is a branch of artificial intelligence (AI) and machine learning that involves statistical techniques and predictive modeling. It plays a crucial role in simulating the way humans acquire knowledge in specific domains. One of the key advantages of deep learning is its ability to tackle complex problems, even when dealing with unstructured datasets. It proves highly beneficial in tasks such as data collection, analysis, and interpretation, significantly accelerating and simplifying these processes. In contrast to linear machine learning algorithms, deep learning offers a means to automate predictive analytics. By applying nonlinear transformations to hierarchical inputs, deep learning constructs statistical models as outputs. The utilization of deep learning algorithms contributes to achieving higher levels of accuracy in various applications.

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