## 4 Methodology
This annual list collects leading researchers in the sciences and social sciences from around the world. Starting from this set of researchers, a data collection phase is performed from Scopus using the Elsevier developer's API. For each author, the following information is collected:

* Details of each article, such as abstract, keywords, year of publishing;
* Temporal range of activity;
* H-index metric;
* Nationality and affiliation. This primary data collection phase has two main purposes:

* Collect textual data to train the word embedding models, in order to obtain an accurate semantic space of topics;
* Collect information about researchers, which will be used in the Data Mining phase to study the hidden relation between an author's mobility across topics and the success in scientific research. After performing the collection phase, the data are further processed with the aim of collecting comparable information. In fact, in order to have a fair comparison among researchers' successes, it is necessary to consider a common period of work activity. In light of this, only authors who have published for at least six years in the period between 2005 and 2015 are selected. For each author, the H-index metric is recalculated, considering only the scientific contribution in the considered period. The resulting number of selected works is 606,605, with 113 million of _non-unique_ words from abstracts and keywords of the collected papers. ### Data Preprocessing

This phase aims to clean the data previously collected in order to increase the performance of the word embedding models. With this aim, standard Natural Language Processing (NLP) techniques are applied to the textual data. In particular, special characters and capitalization are removed. A stop words filter is applied to remove words which do not convey significant meaning (i.e., articles, prepositions). Only for Word2 Vec, a stemming algorithm is used to reduce inflected words to their word stem, base, or root form. Generally, in an NLP preprocessing phase, it is also important to deal with N-grams, which are words obtained by a composition of \(N\) words, i.e., \"New York\" or \"New York Times\". For example, in the sentence \"I have bought a new car\", the word \"new\" has its meaning (adjective in this case). Conversely, in the sentence \"I have been in New York\" we should consider the word \"New_York\" as a whole. The aim is to preserve the words \"New\" and \"York\" when they appear separately, but also to recognize the name \"New_York\" as a single entity (in the model, all letters are converted into their lowercase form). ### Generation of the Semantic Space

In this phase, Word2 Vec and SBERT are used to extract the semantic coordinates of each topic and creating the respective semantic spaces. A main difference between the two models should be now highlighted. Word2 Vec aims to learn word representations but not sentence embedding directly: to obtain the document vector it is necessary to sum or average all the words vectors that compose a sentence (document). This approach is also used in an extension of Word2 Vec called Doc2 Vec. On the other hand, SBERT requires fewer hyper-parameters selected for its pre-trained nature and it directly obtains sentence embeddings. For this reason, we trained Word2 Vec by also selecting its parameters and we only fine-tuned SBERT with our dataset. The Word2 Vec training parameters ([[https://radimrehurek.com/gensim/models/word2](https://radimrehurek.com/gensim/models/word2) vec.html]([https://radimrehurek.com/gensim/models/word2](https://radimrehurek.com/gensim/models/word2) vec.html), accessed on 8 January 2022) are set according to [4,37]. The most important parameters of the Word2 Vec implementation in Gensim library, that have been changed from the default value, are:

* size = 600;
* min count = 300;
* window size = 132. We experimented systematically with different values for _min count_ and _size_ before selecting the most suitable for our model.