使用语言与社交互动特征的社交媒体用户身份关联
User Identity Linkage in Social Media Using Linguistic and Social Interaction Features
AI总结：
针对社交媒体用户多账号规避平台治理的问题，本研究提出融合用户语言、社交互动等多维度活动特征的机器学习检测模型，可识别属于同一自然人的多个虚拟身份，在两类不良Twitter内容场景中验证了有效性，助力防范非法不良活动传播。
摘要：
社交媒体用户通常持有多个账号，以扩大自身想法、观点和见解的传播范围。在不良内容这一特定场景中，用户往往会创建多个账号以绕过社交媒体平台实施的打击措施，从而即便部分账号被封禁，仍能保留自身的网络身份。用户身份关联旨在识别可能属于同一自然人的社交媒体账号，以防范辱骂性/非法活动的传播。为此，本研究提出了一种基于机器学习的检测模型，该模型利用用户网络活动的多重属性，以识别两个或更多虚拟身份是否属于同一个真实的自然人。该模型的有效性在涉及辱骂性内容和恐怖主义相关内容的两组Twitter内容案例中得到了验证。
英文摘要：
Social media users often hold several accounts in their effort to multiply the spread of their thoughts, ideas, and viewpoints. In the particular case of objectionable content, users tend to create multiple accounts to bypass the combating measures enforced by social media platforms and thus retain their online identity even if some of their accounts are suspended. User identity linkage aims to reveal social media accounts likely to belong to the same natural person so as to prevent the spread of abusive/illegal activities. To this end, this work proposes a machine learning-based detection model, which uses multiple attributes of users' online activity in order to identify whether two or more virtual identities belong to the same real natural person. The models efficacy is demonstrated on two cases on abusive and terrorism-related Twitter content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
