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Internet Usage Patterns and Gender Differences: A Deep Learning Approach
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DOI：10.1109/MCE.2020.2986817.png)
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Abstract
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This article aims to examine Internet usage patterns by gender, highlighting differences that relate to intensity of usage and types of online activities. The particularity of the sociodemographic, cultural, and economic conditions in Montenegro places gender differences in a context that may differ from those explored in previous research. An online survey for data collection involved 1147 respondents from Montenegro, and the data analysis was performed using the deep-learning method of k-means clustering and employing a decision tree. The results show that gender differences exist for both observed criteria. This article contributes to a better understanding of how online behavior relates to gender differences and confirms that using deep-learning methods can efficiently identify these differences.
Keywords:
Twitter
Deep learning
Biological system modeling
Measurement
Task analysis
Cultural differences
Gender issues
Data collection
Data analysis
Decision trees
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