This paper investigates the research question if senders of large amounts of irrelevant or unsolicited information - commonly called "spammers" - distort the network structure of social networks. Two large social networks are analyzed, the first extracted from the Twitter discourse about a big telecommunication company, and the second obtained from three years of email communication of 200 managers working for a large multinational company. This work compares network robustness and the stability of centrality and interaction metrics, as well as the use of language, after removing spammers and the most and least connected nodes. The results show that spammers do not significantly alter the structure of the information-carrying network, for most of the social indicators. The authors additionally investigate the correlation between e-mail subject line and content by tracking language sentiment, emotionality, and complexity, addressing the cases where collecting email bodies is not permitted for privacy reasons. The findings extend the research about robustness and stability of social networks metrics, after the application of graph simplification strategies. The results have practical implication for network analysts and for those company managers who rely on network analytics (applied to company emails and social media data) to support their decision-making processes.
翻译:本文调查了研究问题,即大量不相关或未经索取的信息的发送者-通常称为“垃圾邮件”-是否扭曲了社交网络的网络结构。分析了两个大型社交网络。分析了两个大型社交网络,第一个是从推特上关于一家大型电信公司的谈话中摘录的,第二个是从为一家大型多国公司工作的200名管理人员的三年电子邮件通信中获取的。这项工作比较了网络的稳健性以及核心和互动指标的稳定性,以及语言的使用,在去除垃圾邮件和联系最少的节点之后。结果显示,垃圾邮件对于大多数社会指标来说,并没有显著改变信息传递网络的结构。作者还调查了电子邮件主题线与内容之间的关系,通过跟踪语言情绪、情感和复杂性,处理不允许收集电子邮件机构的案件,因为隐私原因不允许收集。在应用了图表简化战略之后,有关社会网络衡量标准的稳健性和稳定性的研究扩展了对网络分析员和那些依赖网络分析器的公司管理人员的实际影响(应用公司电子邮件和社会媒体数据)以支持其决策过程。