## 5 A Computational Model of Network Leadership
### Topic Leaders  
The previously described leadership measures can be applied to an enterprise network in general; however, it may be of interest to assess leadership with reference to specific topics. A user may be highly credible when he/she discusses about, e.g., _mobile apps_, and be much less confident on _business models_. Consequently, network leadership should be analyzed also in the context of users' competence. To this end, our aim is to extract _topic networks_, i.e., networks of users focused on specific topics. In TamTamy, the content of threads, besides their multilinguality and mixed-linguality, greatly differ also in the type of discussed topics. A few threads are on leisure topics (for example, the organization of a football match), however, the majority is on technical or administrative topics. We perform topic extraction in two steps: first, by learning relevant terminology from threads; and, second, by generating clusters of co-occurring terms. Finally, we also classify topics in more general categories. A common topic learning approach in literature is to use stemmed words as items, and then to cluster items using a latent topic model (such as Latent Dirichlet Allocation (LDA) [53] or one of its many variants). This solution turned out to perform poorly due both to mixed linguality and to the reduced dimension of messages. To extract more meaningful terms (with reference to the company's competences and topics), first, we index only \"content\" tokens, or _concepts_, identified as those words mapping with BabelNet [54], a freely available semantic network covering more than 50 languages and more than 13 million concepts. In this way, the specific language in which a concept is expressed does not matter. Then, we extract concept n-grams that are either consecutive (i.e., compounds) or separatedby prepositions and determiners. For example, in the sentence: _\"This technology has been around for over three years and has been used in Macy's for marketing purposes for months now\"_, only the bold tokens are indexed. Finally, we extract _concept cliques_ using the Bron-Kerbosch clique detection algorithm, as described in Reference [55], with the restriction that each element in the clique should exceed an experimentally defined frequency threshold \(\phi\) (we experimentally set \(\phi=3\)). An example of topic \(\tau\) is the following: \begin{tabular}{l l} investimenti_online & digital_marketing \\ _perspective_engage_ & analisi_delle_performance_ \\ \end{tabular}   
Note in the example the presence of several \"mixed language\" concepts (e.g., _investimenti online, analisi delle performance_ where words are either in English or Italian). This is rather common in work environments where the usage of English technical terms dominates. Topics are extracted within temporal windows of length \(W\) (we experimented with different values of \(W\) ranging from weeks to years). Cosine-similarity is used to cluster topics vertically (within the same \(W\)) and horizontally along the temporal line, in order to generate _topic streams_\(s(\tau)\). An example of two topics assigned to the same stream is listed below: \begin{tabular}{l l} TOPIC\#:169 & TOPIC\#:162 \\ user_experience & mobile_pos \\ news_pay & pay_reply \\ carte_di_credito & user_experience \\ credit_card & soluzione_di_mobile_pos \\ metodo_di_pagamento & sistemi_di_pagamento \\ american_express & circuit_di_pagamento \\ pagamenti_online & gestione_coupon \\ \end{tabular}   
Since this is not particularly relevant for the scope of the paper (alternative topic extraction methods could be used), we do not compare our algorithm with other topic detection algorithms in literature in detail; however, we mention that topics extracted with different methods have been comparatively evaluated by our project partners in Reply (this was the only possible option for evaluation, since many keywords are obscure for external evaluators), who found the solution proposed here to produce significantly more meaningful topics than, e.g., using LDA.