## 7 Use-Case Analysis  
A quantitative assessment of the performance of our network analytics models is not possible, since companies do not disclose their promotion decisions, nor the motivations behind a detected anomaly. Therefore, we dedicate the section to demonstrating the utility of our ESAD for analyzing a number of issues which are known to be relevant in organizations, and which are not supported by state of the art social analytic platforms. As we already mentioned, there are two types of users for the ESAD presented in this paper:  
* _Mangers_: Managers can use the platform to support career advancements, by inspecting the network leadership of selected candidates, or by identifying possible candidates not previously considered (as in Figure 4); they can use the platform to form a project team, by generating topic networks (as in Figure 5); they can analyze anomalies in user's and group social behaviors and decide whether to take appropriate actions. * _Social Analysts_: Large companies today often include or engage specialized teams for social analytics: this is the case, for example, in Reply. Social analysts can be supported by our platform in deriving general statistics on the behavior of coworkers. Useful questions that can be answered through our platform are: What have been the main topics of discussion in a given timespan (as in Figure 6)? Are there significant differences in the social behavior of coworkers, according to time, role, and gender? Does the social network encourage the sharing of knowledge (e.g., does the dimension of topic networks--on selected topics considered central--increases or decreases? Is there a tendency to create closed circles? ), etc. Examples of the latter two types of analytics are provided in the section. We experimented with our platform and algorithms on a 3-year dump (2012-2014) of anonymized and partly amended (i.e., without attachments) TamTamy threads, mostly in English, Italian, and German threads. Overall, the TamTamy dataset includes over 50,000 threads from around 2000 different users. To the best of our knowledge, this is the largest and lengthiest enterprise dataset considered in literature: for example, the dataset used in Reference [9] spans over only 6 months, while the popular Enron, used in many studies, includes mails from only 158 users, mostly senior management of Enron. Other studies are based on survey data of some dozens of employees. Table 2 presents summary data. The table shows aggregate statistics for the three years, and additional data by gender and role. We show the percentages only for executives and for females, since the percentages for employees and for males can be, respectively, computed by complementing the shown statistics. Table 2 also shows the network leadership measures presented in Section 5, _leadership centrality rank_ (LCR, Section 5.2) and _leadership brokerage rank_ (LBR, Section 5.3), the dashboard also supports the computation of more traditional measures, such as centrality degree. As we already remarked in Section 5 and summarized in Section 5.5, the setting of parameters in our leadership model depends on managerial choices: for example, centrality can be considered more important than brokerage, competence on specific topics might be required, and the mutual influence between collaboration, empowerment, and trust can be adjusted. In our use case, to compute LCR, parameters \(\beta\) and \(\gamma\) in Equations (8) and (9) are all set to 1, while the teleporting factors \(\alpha\) are set to 0.85, which is a commonly used value. Furthermore, as stated in Section 5.2, leadership measures are based only on non-zero threads. The last three lines in Table 2 show the % of females among top LBR, top LCR, and those who are both top LBR and top LCR, i.e., the _Network Leaders_ (NL), according to what we stated in Section 2. Top leaders have been empirically selected as those with an LBR or LCR value \(v\geq avg(v)+2\times SD(v)\) during the analyzed period.