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

arXiv: https://arxiv.org/abs/2105.10256 · HTML: https://mlsanity.com/paper/2105.10256/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 73 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 7 of 7)

- [The impact of virtual mirroring on customer satisfaction](https://mlsanity.com/paper/2105.09571/) (2021)
- [Forecasting managerial turnover through e-mail based social network analysis](https://mlsanity.com/paper/2105.09208/) (2021)
- [Using four different online media sources to forecast the crude oil price](https://mlsanity.com/paper/2105.09154/) (2021)
- [Measuring Team Creativity Through Longitudinal Social Signals](https://mlsanity.com/paper/1407.0440/) (2014)
- [The power of reciprocal knowledge sharing relationships for startup success](https://mlsanity.com/paper/2105.11538/) (2021)
- [Sequential Defense Against Random and Intentional Attacks in Complex Networks](https://mlsanity.com/paper/1502.00065/) (2015)
- [Robustness and stability of enterprise intranet social networks: The impact of moderators](https://mlsanity.com/paper/2105.09127/) (2021)

## Built on by (most cited first, 5 of 5)

- [An Investigation on Learning, Polluting, and Unlearning the Spam Emails for Lifelong Learning](https://mlsanity.com/paper/2111.14609/) (2021)
- [From words to connections: Word use similarity as an honest signal conducive to employees' digital communication](https://mlsanity.com/paper/2111.06133/) (2021)
- [Classifying spam emails using agglomerative hierarchical clustering and a topic-based approach](https://mlsanity.com/paper/2402.05296/) (2024)
- [Put your money where your mouth is: Using deep learning to identify consumer tribes from word usage](https://mlsanity.com/paper/2105.13036/) (2021)
- [Corporate core values and social responsibility: What really matters to whom](https://mlsanity.com/paper/2106.01644/) (2021)

## Abstract (arXiv)

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.