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The purpose of this paper is to model and study the effectiveness of an attack on the anonymity of Internet users by a group of collaborating eavesdroppers.

The paper is based on an analysis of the Internet topology. The study is based on two methods for choosing nodes that contribute the most to the detection of as many communicating Internet users as possible.

The paper illustrates that it is possible to compromise the anonymity of many Internet users when eavesdropping on a relatively small number of nodes, even when the most central ones are protected from eavesdropping.

It is assumed that the Internet users under attack are not using any anonymity enhancing technologies, but nodes can be protected from eavesdropping. It proposes a measure of the success of an attack on Internet users' anonymity, for a given deployment of collaborating eavesdroppers in the Internet.

The paper shows that several, and not necessarily the most prominent, collaborating nodes can compromise the anonymity of a considerable portion of Internet users. This study also emphasizes that when trying to completely compromise the anonymity of Internet users, an eavesdroppers' deployment strategy that considers eavesdroppers' collaboration can result in substantial resource saving compared to choosing a set of the most prominent nodes.

The paper proposes a new measure of anonymity level in the network, based on the linkability of the Internet users. This paper is the first to present results of a non‐trivial Group Betweenness optimization strategy in large complex networks.

In order to further advance the research of social bots, based on the latest research trends and in line with international research frontiers, it is necessary to understand the global research situation in social bots.

Choosing Web of Science™ Core Collections as the data sources for searching social bots research literature, this paper visually analyzes the processed items and explores the overall research progress and trends of social bots from multiple perspectives of the characteristics of publication output, major academic communities and active research topics of social bots by the method of bibliometrics.

The findings offer insights into research trends pertaining to social bots and some of the gaps are also identified. It is recommended to further expand the research objects of social bots in the future, not only focus on Twitter platform and strengthen the research of social bot real-time detection methods and the discussion of the legal and ethical issues of social bots.

Most of the existing reviews are all for the detection methods and techniques of social bots. Unlike the above reviews, this study is a systematic literature review, through the method of quantitative analysis, comprehensively sort out the research output in social bots and shows the latest research trends in this area and suggests some research indirections that need to be focused in the future. The findings will provide references for subsequent scholars to research on social bots.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2021-0336.