#### Group Activity

The group activity metric calculates how many of the total messages were posted in groups and how many public and private groups were contributed to. It is concerned with the focus of a particular individual and its community contributions. The metric shows where the user posts his content and if the user is more active in public or private conversations. It is suggested by Hacker et al. (2015) and Viol et al. (2016) and is of ego-centric scope.

For the calculation of the metrics public message count $m_{pub}$ and private message count $m_{priv}$ the following queries are needed:

m_pub(uid) := SELECT COUNT (source) FROM relationships
WHERE source = uid AND privacy = "Public";
m_priv(uid) := SELECT COUNT (source) FROM relationships
WHERE source = uid AND privacy = "Private";


(Remark: in some data sets, groups are set to private or public. If a group is set public, the metric's value for private messages will always be zero, and the other way round.)

Users contributing highly to groups are bound to a subset of the Enterprise Social Network. On the one hand they contribute to the specific group of the network, but on the other hand lack activity in the rest of the network. Such users only focus on their topics of interest according to Hacker et al. (2015).

Viol et al. (2016) describe this attribute as focus. Users with focus make valuable contributions to the network, but only a part of the network can benefit from the content. They are engaging in discussions with other peers in their groups, but lack relationships and interactions with other people outside of their group.

Due to the cohesion of the group and shared norms and values, group activity can be associated with high Bonding Social Capital (Coleman et al., 1990) within the groups. It can indicate effective collaboration and knowledge sharing in groups between the actors (Riemer et al., 2005) based on shared norms and common grounds (Nahapiet et al., 1998). A negative effect could be the establishment of subcultures, that do not communicate with the rest of the network. These subcultures can possibly stand orthogonal towards the network and hinder effective collaboration (Hatch et al., 2012).

If you take the sum of these two metrics, it will be the total amount of messages the user has created. The number would be equal to the metric messages created.