  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.
