Denigration is a specialized form of cyberbullying which describes a recurrent, sustained and intentional attempt to damage the victim’s reputation or ruin the friendships that he or she has by spreading unfounded gossip or rumors online. It is the most common bullying tactic involving character assassination of public figures like celebrities and politicians. As a comprehensive approach to match to the scale of social media this research put forwards a D-BullyRumbler model for automatic detection and resolution of denigration cyberbullying in online textual content using a hybrid of lexicon-based and machine learning-based techniques. The model processes textual, content-based and user-based features to uncover denigration from two perspectives. Firstly, a direct explicit content analysis is done to look for denigration markers as features for model training and testing. Concurrently, potentially harmful messages, rumors, are identified as candidates and examined for target profile type to reveal the case of denigration. An additional OR operation is done to maintain the holistic framework. Another novelty of the work includes the use of hybrid filter-wrapper method, Chi-square filter and cuckoo search wrapper algorithm to improve the performance of reputation rumor classification module. Experimental results on social media datasets show the superior classification performance. The results validate the effectiveness of the proposed model which facilitates timely intervention by buzzing an alarm to the moderators and further forming a rumble safety strip to inhibit the production and dissemination of inappropriate content to protect the victims.

中文翻译：

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D-BullyRumbler：使用混合过滤器包装方法解决在线诽谤欺凌的安全隆隆声带

诋毁是网络欺凌的一种特殊形式，它描述了一种反复、持续和有意的企图，通过在网上散布毫无根据的八卦或谣言来损害受害者的声誉或破坏他或她的友谊。这是最常见的欺凌策略，涉及对名人和政治家等公众人物进行性格暗杀。作为一种与社交媒体规模相匹配的综合方法，本研究提出了一种 D-BullyRumbler 模型，用于使用基于词典和基于机器学习的技术的混合自动检测和解决在线文本内容中的诋毁网络欺凌。该模型处理文本、基于内容和基于用户的特征，以从两个角度揭示诋毁。首先，进行直接的显式内容分析以寻找诋毁标记作为模型训练和测试的特征。同时，潜在有害的消息、谣言被确定为候选对象，并检查目标配置文件类型以揭示诽谤案例。执行额外的 OR 操作以维护整体框架。该工作的另一个新颖之处包括使用混合过滤器包装器方法、卡方过滤器和布谷鸟搜索包装器算法来提高声誉谣言分类模块的性能。在社交媒体数据集上的实验结果显示出优越的分类性能。