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Detecting health misinformation in online health communities: Incorporating behavioral features into machine learning based approaches

机译：检测在线健康社区中的健康错误信息：将行为特征纳入基于机器学习的方法

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摘要

Curbing the diffusion of health misinformation on social media has long been a public concern since the spread of such misinformation can have adverse effects on public health. Previous studies mainly relied on linguistic features and textual features to detect online health-related misinformation. Based on the Elaboration Likelihood Model (ELM), this study proposed that the features of online health misinformation can be classified into two levels: central-level and peripheral-level. In this study, a novel health misinformation detection model was proposed which incorporated the central-level features (including topic features) and the peripheral-level features (including linguistic features, sentiment features, and user behavioral features). In addition, the following behavioral features were introduced to reflect the interaction characteristics of users: Discussion initiation, Interaction engagement, Influential scope, Relational mediation, and Informational independence. Due to the lack of a labeled dataset, we collected the dataset from a real online health community in order to provide a real scenario for data analysis. Four types of misinformation were identified through the coding analysis. The proposed model and its individual features were validated on the real-world dataset. The model correctly detected about 85% of the health misinformation. The results also suggested that behavioral features were more informative than linguistic features in detecting misinformation. The findings not only demonstrated the efficacy of behavioral features in health misinformation detection but also offered both methodological and theoretical contributions to misinformation detection from the perspective of integrating the features of messages as well as the features of message creators.
机译：遏制健康误导的扩散在社交媒体上长期以来一直是公众关注，因为这种错误信息的传播可能对公共卫生产生不利影响。以前的研究主要依赖于语言特征和文本特征来检测在线健康相关的错误信息。基于阐述似然模型（ELM），本研究提出了在线健康错误信息的特征可以分为两个级别：中央级和外围级。在本研究中，提出了一种新的健康错误信息检测模型，其包含中央级别（包括主题特征）和外围级别特征（包括语言特征，情感特征和用户行为特征）。此外，引入了以下行为特征，以反映用户的互动特征：讨论启动，互动参与，有影响力的范围，关系调解和信息独立性。由于缺少标记的数据集，我们从真正的在线健康界收集数据集，以便提供数据分析的实际方案。通过编码分析确定了四种类型的错误信息。在现实世界数据集上验证了所提出的模型及其个别功能。该模型正确地检测到了大约85％的健康错误信息。结果还表明，行为特征比检测错误信息在语言特征上更具信息量。结果不仅证明了行为特征在健康错误信息检测中的疗效，而且从集成消息的特征的角度来看，还为错误信息检测提供了方法论和理论贡献，以及消息创建者的特征。

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  • 来源
    《Information Processing & Management》 |2021年第1期|102390.1-102390.24|共24页
  • 作者

    Yuehua Zhao; Jingwei Da; Jiaqi Yan;

  • 作者单位

    School of Information Management Nanjing University No. 163 Xianlin Road Qixia District Nanjing China Jiangsu Key Laboratory of Data Engineering and Knowledge Service Nanjing China;

    School of Information Management Nanjing University No. 163 Xianlin Road Qixia District Nanjing China;

    School of Information Management Nanjing University No. 163 Xianlin Road Qixia District Nanjing China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Health misinformation; Misinformation detection; Online health community;

    机译：健康错误信息;错误信息检测;在线健康界;

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