Technology Policy Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea.
Management Information Systems, Chungbuk National University, Cheongju, South Korea.
PLoS One. 2023 Feb 10;18(2):e0281291. doi: 10.1371/journal.pone.0281291. eCollection 2023.
Recently, the digital divide problem among elderly individuals has been intensifying. A larger problem is that the level of use of digital technology varies from person to person. Therefore, a digital divide may even exist among elderly individuals. Considering the recent accelerating digital transformation in our society, it is highly likely that elderly individuals are experiencing many difficulties in their daily life. Therefore, it is necessary to quickly address and manage these difficulties.
This study aims to predict the digital divide in the elderly population and provide essential insights into managing it. To this end, predictive analysis is performed using public data and machine learning techniques.
This study used data from the '2020 Report on Digital Information Divide Survey' published by the Korea National Information Society Agency. In establishing the prediction model, various independent variables were used. Ten variables with high importance for predicting the digital divide were identified and used as critical, independent variables to increase the convenience of analyzing the model. The data were divided into 70% for training and 30% for testing. The model was trained on the training set, and the model's predictive accuracy was analyzed on the test set. The prediction accuracy was analyzed using logistic regression (LR), support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and eXtreme gradient boosting (XGBoost). A convolutional neural network (CNN) was used to further improve the accuracy. In addition, the importance of variables was analyzed using data from 2019 before the COVID-19 outbreak, and the results were compared with the results from 2020.
The study results showed that the variables with high importance in the 2020 data predicting the digital divide of elderly individuals were the demographic perspective, internet usage perspective, self-efficacy perspective, and social connectedness perspective. These variables, as well as the social support perspective, were highly important in 2019. The highest prediction accuracy was achieved using the CNN-based model (accuracy: 80.4%), followed by the XGBoost model (accuracy: 79%) and LR model (accuracy: 78.3%). The lowest accuracy (accuracy: 72.6%) was obtained using the DT model.
The results of this analysis suggest that support that can strengthen the practical connection of elderly individuals through digital devices is becoming more critical than ever in a situation where digital transformation is accelerating in various fields. In addition, it is necessary to comprehensively use classification algorithms from various academic fields when constructing a classification model to obtain higher prediction accuracy.
The academic significance of this study is that the CNN, which is often employed in image and video processing, was extended and applied to a social science field using structured data to improve the accuracy of the prediction model. The practical significance of this study is that the prediction models and the analytical methodologies proposed in this article can be applied to classify elderly people affected by the digital divide, and the trained models can be used to predict the people of younger generations who may be affected by the digital divide. Another practical significance of this study is that, as a method for managing individuals who are affected by a digital divide, the self-efficacy perspective about acquiring and using ICTs and the socially connected perspective are suggested in addition to the demographic perspective and the internet usage perspective.
最近，老年人之间的数字鸿沟问题日益加剧。一个更大的问题是，每个人使用数字技术的水平各不相同。因此，老年人之间甚至可能存在数字鸿沟。考虑到我们社会最近加速的数字化转型，老年人在日常生活中很可能遇到许多困难。因此，有必要迅速解决和管理这些困难。
本研究旨在预测老年人中的数字鸿沟，并提供管理数字鸿沟的重要见解。为此，使用公共数据和机器学习技术进行预测分析。
本研究使用了韩国国家信息社会局发布的“2020 年数字信息鸿沟调查报告”中的数据。在建立预测模型时，使用了各种独立变量。确定了 10 个对预测数字鸿沟具有重要意义的变量，并将其作为关键的独立变量，以增加分析模型的便利性。数据分为 70%用于训练和 30%用于测试。在训练集上训练模型，并在测试集上分析模型的预测准确性。使用逻辑回归（LR）、支持向量机（SVM）、K-最近邻（KNN）、决策树（DT）和极端梯度提升（XGBoost）分析预测准确性。还使用卷积神经网络（CNN）进一步提高准确性。此外，还分析了 2019 年（新冠疫情爆发前）数据中变量的重要性，并将结果与 2020 年的结果进行了比较。
研究结果表明，2020 年预测老年人数字鸿沟的高重要性变量包括人口统计学视角、互联网使用视角、自我效能视角和社会联系视角。这些变量以及社会支持视角在 2019 年也非常重要。基于 CNN 的模型的预测准确性最高（准确率：80.4%），其次是 XGBoost 模型（准确率：79%）和 LR 模型（准确率：78.3%）。DT 模型的准确性最低（准确率：72.6%）。
本分析结果表明，在各个领域的数字化转型加速的情况下，支持通过数字设备加强老年人的实际联系比以往任何时候都更加重要。此外，在构建分类模型时，需要综合使用来自各个学术领域的分类算法，以获得更高的预测准确性。
本研究的学术意义在于，将常用于图像处理和视频处理的 CNN 扩展并应用于使用结构化数据的社会科学领域，以提高预测模型的准确性。本研究的实际意义在于，本文提出的预测模型和分析方法可以用于对受数字鸿沟影响的老年人进行分类，并可以使用训练好的模型预测可能受到数字鸿沟影响的年轻一代人群。本研究的另一个实际意义在于，作为管理受数字鸿沟影响的个体的一种方法，除了人口统计学视角和互联网使用视角外，还提出了关于获取和使用信息通信技术的自我效能视角和社会联系视角。
