## 4 Use Case Description: The TamTamy Enterprise Social Network  
The research described in this paper is part of a financed research project which leads to the implementation of a Social Analytic layer, named _Fiordaliso_. We apply our methodology to the use case of a proprietary ESN, TamTamy ([[http://www.reply.eu/tamtamy-reply/en/](http://www.reply.eu/tamtamy-reply/en/)]([http://www.reply.eu/tamtamy-reply/en/](http://www.reply.eu/tamtamy-reply/en/)), accessed on 1 September 2021).  
TamTamy has been designed by Reply ([[http://www.reply.com/en/](http://www.reply.com/en/)]([http://www.reply.com/en/](http://www.reply.com/en/)), accessed on 1 September 2021), a large international network of specialized companies in the field of digital services, based in the United Kingdom, Italy, Germany, France, Belgium, Poland, Holland, and now expanding worldwide (USA, Brazil). Given its cluster-based organization, Reply openly declares to be focused on online sharing tools, such as TamTamy, since \"_communities are the key to create engagement, brand awareness and knowledge sharing_\". Furthermore, interviewed managers reported that being active online is acknowledged to be a push for future promotions. Therefore, TamTamy seems to be an ideal environment for the objectives of the present study.  
Though the type of data that can be derived from a social network obviously depends on the specific platform, many of the features of TamTamy are indeed common to other popular enterprise social networks, such as, e.g., live ([[https://www.jivesoftware.com/products-solutions/jive-n/](https://www.jivesoftware.com/products-solutions/jive-n/)]([https://www.jivesoftware.com/products-solutions/jive-n/](https://www.jivesoftware.com/products-solutions/jive-n/)), accessed on 1 September 2021). Basically, these environments provide a collaboration space to share knowledge and documents, to communicate with co-workers, and to search and find relevant content and people within a company.  
Though the TamTamy platform provides a number of services, we are focusing here mainly with social networking and communication. Messages are started by a thread initiator (the _author_). The other users (_commenters_) are then free to contribute and to rate messages with likes and dislikes, akin to Facebook ([[https://www.facebook.com/](https://www.facebook.com/)]([https://www.facebook.com/](https://www.facebook.com/)), accessed on 1 September 2021) style. The basic format of each thread is the following:  
thread_id, thread_title, thread_author, timestamp, thread_tags, author_title, author_gender(0=male,1=female), thread_content, {comment_id, commeter, commeter_title, gender, timestamp, comment_content,}\"  
The _thread tags_ are free, although there exist a number of predefined tags. The field _Title_ indicates the author/commenter role in the company (_manager, consultant, senior consultant, partner, external, content publisher_). Partners and managers are _executive_ roles. A thread excerpt (anonymized for privacy reasons) is shown in Figure 2. Note that only rarely is the recipient explicitly indicated in the message; otherwise, answers are meant to be directed to the author of the thread, as on Facebook. In our dataset, threads are in English, German, and Italian, and, often, the text includes a mix-up of languages since many technical terms are in English, even when the conversation is in another language. Moreover, the use of mixed terms (e.g., _pagamento online_) is quite common. The phenomenon of \"mixed linguality\" (i.e., terminological expressions including words in multiple languages) was one of the main difficulties when analyzing message content (see Section 5.4), since available natural language processing (NLP) tools, such as part of speech tagging, cannot be readily used.  
In our study, we used a three-year anonymized dataset of employees' messages, which has been made available for analysis in the context of the Fiordaliso project.