Tumblr is one of the largest and most popular microblogging website on the Internet. Studies shows that due to high reachability among viewers, low publication barriers and social networking connectivity, microblogging websites are being misused as a platform to post hateful speech and recruiting new members by existing extremist groups. Manual identification of such posts and communities is overwhelmingly impractical due to large amount of posts and blogs being published every day. We propose a topic based web crawler primarily consisting of multiple phases: training a text classifier model consisting examples of only hate promoting users, extracting posts of an unknown tumblr micro-blogger, classifying hate promoting bloggers based on their activity feeds, crawling through the external links to other bloggers and performing a social network analysis on connected extremist bloggers. To investigate the effectiveness of our approach, we conduct experiments on large real world dataset. Experimental results reveals that the proposed approach is an effective method and has an F-score of 0.80. We apply social network analysis based techniques and identify influential and core bloggers in a community.
翻译:Tumblr是互联网上最大和最受欢迎的微博客网站之一。研究显示,由于观众的可及性很高,出版障碍和社交网络连通性低,微博客网站被滥用为发表仇恨言论和由现有极端主义团体招募新成员的平台。由于每天发布大量文章和博客,人工识别这些职位和社区是极其不切实际的。我们提议了一个基于主题的网络爬行者,主要包括多个阶段:培训一个文本分类模型,仅包括煽动仇恨的用户的例子,提取一个未知的暴云微博客的海报,根据他们的活动素材对煽动仇恨的博客进行分类,通过外部链接与其他博客进行爬行,并对关联的极端主义博客进行社会网络分析。为了调查我们的方法的有效性,我们在大型真实世界数据集上进行实验。实验结果表明,拟议的方法是一种有效的方法,有0.80的F群集。我们应用了基于社会网络的分析技术,并在一个社区中识别有影响力和核心的博客。