# Knowledge Sharing in Security-Sensitive Communities
## 4 Method  
The primary method for examining the influence of secrecy culture on knowledge sharing is a comparative analysis of communication graphs using the metrics described earlier: clustering coefficient, transitivity, diameter, radius, and density. The comparison is conducted between security-sensitive and non-sensitive organizations taking part in communities discussing a common topic, COVID-19. _The dataset_--The struggle with COVID-19 supplied a unique opportunity to study the formation of task forces that emerged quickly and began fighting the pandemic. Task forces were formed in parallel by the military intelligence and by the civilian Ministry of Health. Both communities used WhatsApp instant messaging for real-time communication and updates regarding the pandemic. This situation was anomalous for the military community, which usually deals with classified materials. Since a large part of the information was not security-related, knowledge sharing was expected to follow the practices of unclassified communities. The discourse revealed within both intelligence and civil communities provides us with a unique opportunity to use network metrics to analyze knowledge sharing. To gain access to the classified information, we sent an explanation to intelligence community members including a link to a tool that removes all the content. The tool is our own development in the Node.js programming language. Members who agreed to cooperate and received permission cleaned the content and sent us text files without the content. Most of the groups we gained access to were completely clean. This means we received a.txt file with a list of classified names of message senders and dates according to the sending order. For some groups, we received a textual description of the purpose of establishing the WhatsApp group. Regarding the type of participants in each group, intelligence personnel, industry, or academia, we received reliable information that was checked and verified by three intelligence members. Figure 1: Research model. The research involved only unidentifiable/de-identified private information. In such cases, a IRB review is not required. Groups--Table 2 describes each of the 18 analyzed Whatsapp groups engaged in COVID-19 knowledge sharing. The groups are organized as follows: security-sensitive groups are first, followed by non-security-sensitive groups. The inner order is defined by the number of organizations members relate to. Acronyms in Table 2: military intelligence is \" | Int.Army\", the civilian intelligence body \"Home Front Command\" (HFC) is \"Int.Civil\", industry is \"Ind\", and academia is \"Acd\". The \"Homogeneous\" column refers to the nature of the analyzed group. If the group includes several security-sensitive organizations, the group is _homogeneous_ regarding its sensitivity to information security (denoted as 1 in Table 2). If the group included mixed groups of security-sensitive organizations and civilians, we define the group as _heterogeneous_ and indicate 0 in the cell. All groups were exported simultaneously and reflected conversations from the same period-about one year. The nodes in Table 2 represent participants (N = 593). Arcs represent knowledge sharing between participants. If there is one interaction between a pair of participants, the weight of the arc is 1. Repeated communication increases the weight of the arc proportionately to the number of interactions. Table 2 suggests that when group participants are from one or two organizations, the group is likely to be homogeneous. When participants are from more than two organizations, the group becomes heterogeneous. Procedure:  
_1. Data preparation:_ we received 18 WhatsApp conversations as clean txt files, after activating a custom script we developed in Node.js to remove the content, resulting in 18 txt files containing only information about the sender and timestamp, while excluding the message content. These files served as the raw data for the study. _2.