## 2 Materials and Methods
### Text Analysis  
For keyword/key-phrase extraction we used the TF-IDF weighting factor [26]. This measure attempts to identify the uniqueness of a specific word or phrase by reducing the weighting of words that appear commonly across all documents in a collection. Mathematically, given our corpus of IDTs, \(C\), the document frequency of \(df(t)\) is simply the number of documents \(d\) in \(C\) that contain the term \(t\), in formulas \(df(t)=|\{d\in C|t\in d\}|\). Based on this, the inverse document frequency is defined as:  
\[idf(t)=\log(\frac{|C|}{df(t)}). \tag{1}\]  
Note that the log function is used for sublinear scaling to avoid rare words getting an extremely high score. Each term is given a TF-IDF weighting, which provides high values for terms appearing frequently in the selected document \(D\) but rarely in others:  
\[tfidf(t,D)=tf(t,D)\cdot idf(t). \tag{2}\]  
A combination of word clouds, heatmaps, and network visualisations was used to outline the results. In word clouds, rather than changing word size according to their frequencies, we used TF-IDF scores. For example, Figure 2 illustrates the benefits of the TF-IDF in drawing out the pertinent themes which would be lost if we were merely highlighting the most common words. In this case, in 2021 the TF-IDF correctly highlights major themes (tracked also by previous works) like the COVID-19 pandemic (see [9; 44]), the increasing risk for cyber-attacks and online manipulation (see [45; 46; 47]) and growing concern around inflation (see [48]). In addition to computing TF-IDF scores, we also built cognitive networks. As common practice in computer science, we used co-occurrences between the cleaned, lemmatised, non-stopword word-forms to infer syntactic relationships between words in sentences [25; 39]. This approach might miss syntactic dependencies between words far apart in sentences (see also [15]) but it has the advantage of being a computationally inexpensive method [39] that requires no prior training data. We also enriched this structure by building relationships between any two words being synonyms according to WordNet [49]. The resulting network structure is cognitive because it reflects, to a limited extent, how concepts were syntactically and semantically associated within the narratives of different transcripts. As in prior works, we aggregated links coming from different sentences in the same document. We reconstructed the semantic frame of concept X as the network neighbourhood of that concept, i.e., the collection of nodes adjacent to X. We use semantic frames to quantitatively compare how concepts were reported in the transcripts across different periods. The evolution of words associated with a common word was also visualised through the lens of cognitive networks--this provides a perspective of how the meaning and context of words evolve over time and could provide insight into the changing risk landscape. For sentiment analysis, we used the Loughran and McDonald dictionary [28] implemented via the _pysent3_ library. For any word \(w\), the lexicon provides positive (\(P_{w}\)) and negative (\(N_{w}\)) word scores, as well as polarity \(p_{w}\) and subjectivity \(s_{w}\) based on the following formula [28]:  
\[p_{w}=\frac{P_{w}-N_{w}}{P_{w}+N_{w}}\qquad\qquad s_{w}=\frac{P_{w}+N_{w}}{ \sum_{w}(P_{w}+N_{w})}. \tag{3}\]  
In addition, Pearson's correlation coefficient is used to measure the strength and direction of relationship between the polarity and share price movements, defined as \(\rho(X,Y)=(cov(X,Y))/(\sigma_{X}\sigma_{Y})\), where X is the share price and Y is the corresponding polarity score. For topic modelling with LDA [42] we use the generative version of LDA implemented in _gensim_. Each topic becomes an unstructured list/distribution of words that co-occur together in specific documents more than random expectation [4].