Measuring the impact of spammers on e-mail and Twitter networks

• Published in: International journal of information management 2019-10, Vol.48, p.254-262
• Main Authors: Fronzetti Colladon, Andrea, Gloor, Peter A.
• Format: Article
• Language: English
• Subjects: Data preprocessing; Decision making; Digital media; Electronic mail systems; Email; Multinational corporations; Robustness; Social network; Social networks; Spammers; Spamming; Stability; Twitter
• ISSN: 0268-4012; 1873-4707
• DOI: 10.1016/j.ijinfomgt.2018.09.009

•We analyze two large social networks extracted from business emails and Twitter.•We show the impact of several node removal strategies, focusing on spammers.•We test network robustness and the stability of several actor-level metrics.•We further test the stability of semantic variables, such as language sentiment.•We draw helpful conclusions for graph simplification purposes. 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.