# A Review of Digital Era Governance Research in the First Two Decades: A Bibliometric Study
## 4 Results
### Dynamic Changes in Terms' Importance  
In the final series of experiments, the dynamic change in a term's occurrence in the four sub-periods was analyzed using machine learning methods for predictive modelling. In particular, random forests [49] was used to train a model that predicts the publication year of the article based on the frequency of words in the article titles and abstracts. In turn, the trained model was inspected to extract the words that have the biggest impact on the prediction of the publication year. It is expected that these words can explain the variation in the research themes between the publication periods observed. The data set for training the predictive model consists of 3917 examples, each corresponding to one of the articles included in our study. To obtain the values of the predictive attributes, we follow a standard methodology for transforming text documents into attribute vectors from [PERSON] et al. [95]. We first extract the words from the titles and abstracts of the words. Then, we use lemmatization to transform the different, inflected variants of the words into their common dictionary variant (lemma). We then remove the common stop words, i.e., frequent words that do not convey meaning, such as \"the\" or \"and\", as well as scientific stop words commonly used to state scientific conjectures or study goals, such as \"analyze\", \"formulate\", \"approach\" or \"method\". We also remove words that appear in less than 1% of the articles. Finally, we employ a standard term-frequency-inversed-document-frequency model [96] to calculate the weights of the terms corresponding to individual words and bigrams (sequences of two consecutive words) in the articles' abstracts and titles. Figure 12 shows the results of the analysis. The graph on the left-hand side depicts the importance of 20 terms from the abstracts and titles of the articles with the highest impact on predicting the article's publication year. The blue and the red bars correspond to the terms with increasing and decreasing frequency, respectively. The colour shade (intensity) denotes the intensity of the increasing or decreasing trend: darker and lighter colour shades indicate more and less intense trends, respectively. The two graphs on the right-hand side of Figure 12 present the dynamic change in the frequency of the terms _e-government_ and _smart_. Each vertical bar depicts the average  
Figure 12: Dynamic changes in a term’s importance. term frequency in articles published in a particular year. The horizontal bars correspond to the relative changes in the average frequency of the term in a particular 5-year period with respect to the average term frequency in the whole observation period (2001-2020). Blue and red bars correspond to over-expressed and under-expressed terms that appear in the 5-year period more and less frequently, respectively. Take the term _e-government_ as an example. The light-red colour of the bar indicates that the use of the term e-government has declined over the years, and the declining trend is slight. Indeed, the top graph on the right-hand side shows the change in the frequency of the term e-government. The peak of its use was in 2008 and the sub-period of 2006-2010 (40% above the average for the whole observation period). This might be expected because the term was coined in the early 2000s, with its use intensifying and peaking between 2006 and 2010. There are two reasons for the decline in its use after 2010. First, authors in the settings dedicated to e-government refrained from using it in the abstracts and titles since all of the articles in those settings were on e-government, and the term stopped playing the role of distinguishing one's work from others. Second, in recent years e-government, especially its adjectival part \"electronic\", has been replaced by emerging alternative terms such as _digital_ (used in both digital government or digital transformation), _smart_ and _open_.