Farhah Nesren S, Alqarni Ahmed Abdullah, Ebrahim Nadhem, Ahmad Sultan
Department of Health Informatics, College of Health Science, Saudi Electronic University, Riyadh, Saudi Arabia.
King Salman Center for Disability Research, Riyadh, Saudi Arabia.
Front Med (Lausanne). 2025 Aug 20;12:1646249. doi: 10.3389/fmed.2025.1646249. eCollection 2025.
Social media is increasingly used in many contexts within the healthcare sector. The improved prevalence of Internet use via computers or mobile devices presents an opportunity for social media to serve as a tool for the rapid and direct distribution of essential health information. Autism spectrum disorders (ASD) are a comprehensive neurodevelopmental syndrome with enduring effects. Twitter has become a platform for the ASD community, offering substantial assistance to its members by disseminating information on their beliefs and perspectives via language and emotional expression. Adults with ASD have considerable social and emotional challenges, while also demonstrating abilities and interests in screen-based technologies.
The novelty of this research lies in its use in the context of Twitter to analyze and identify ASD. This research used Twitter as the primary data source to examine the behavioral traits and immediate emotional expressions of persons with ASD. We applied Convolutional Neural Networks with Long Short-Term Memory (CNN-LSTM), LSTM, and Double Deep Q-network (DDQN-Inspired) using a standardized dataset including 172 tweets from the ASD class and 158 tweets from the non-ASD class. The dataset was processed to exclude lowercase text and special characters, followed by a tokenization approach to convert the text into integer word sequences. The encoding was used to transform the classes into binary labels. Following preprocessing, the proposed framework was implemented to identify ASD.
The findings of the DDQN-inspired model demonstrate a high precision of 87% compared to the proposed model. This finding demonstrates the potential of the proposed approach for identifying ASD based on social media content.
Ultimately, the proposed system was compared against the existing system that used the same dataset. The proposed approach is based on variations in the text of social media interactions, which can assist physicians and clinicians in performing symptom studies within digital footprint environments.
社交媒体在医疗保健领域的诸多场景中使用得越来越频繁。通过计算机或移动设备使用互联网的普及率提高，为社交媒体成为快速直接传播基本健康信息的工具提供了契机。自闭症谱系障碍（ASD）是一种具有持久影响的综合性神经发育综合征。推特已成为自闭症谱系障碍群体的一个平台，通过语言和情感表达传播他们的信念和观点，为其成员提供了大量帮助。患有自闭症谱系障碍的成年人面临着相当大的社交和情感挑战，同时也在基于屏幕的技术方面展现出能力和兴趣。
本研究的新颖之处在于在推特的背景下进行分析和识别自闭症谱系障碍。本研究将推特用作主要数据源，以检查自闭症谱系障碍患者的行为特征和即时情感表达。我们应用了带有长短期记忆的卷积神经网络（CNN-LSTM）、长短期记忆网络（LSTM）以及受双深度Q网络启发的网络（DDQN-Inspired），使用了一个标准化数据集，其中包括来自自闭症谱系障碍类别的172条推文和来自非自闭症谱系障碍类别的158条推文。对数据集进行处理以排除小写文本和特殊字符，随后采用分词方法将文本转换为整数单词序列。编码用于将类别转换为二进制标签。预处理之后，实施所提出的框架以识别自闭症谱系障碍。
受双深度Q网络启发的模型的研究结果表明，与所提出的模型相比，其精度高达87%。这一发现证明了基于社交媒体内容识别自闭症谱系障碍的所提出方法的潜力。
最终，将所提出的系统与使用相同数据集的现有系统进行了比较。所提出的方法基于社交媒体互动文本中的差异，这可以帮助医生和临床医生在数字足迹环境中进行症状研究。
