A joint text mining-rank size investigation of the rhetoric structures of the US Presidents’ speeches

© 2019 Elsevier Ltd This work presents a text mining context and its use for a deep analysis of the messages delivered by politicians. Specifically, we deal with an expert systems-based exploration of the rhetoric dynamics of a large collection of US Presidents’ speeches, ranging from Washington to Trump. In particular, speeches are viewed as complex expert systems whose structures can be effectively analyzed through rank-size laws. The methodological contribution of the paper is twofold. First, we develop a text mining-based procedure for the construction of the dataset by using a web scraping routine on the Miller Center website – the repository site collecting the speeches. Second, we explore the implicit structure of the discourse data by implementing a rank-size procedure over the individual speeches, being the words of each speech ranked in terms of their frequencies. The scientific significance of the proposed combination of text-mining and rank-size approaches can be found in its flexibility and generality, which let it be reproducible to a wide set of expert systems and text mining contexts. The usefulness of the proposed method and of the speeches analysis is demonstrated by the findings themselves. Indeed, in terms of impact, it is worth noting that interesting conclusions of social, political and linguistic nature on how 45 United States Presidents, from April 30, 1789 till February 28, 2017 delivered political messages can be carried out. Indeed, the proposed analysis shows some remarkable regularities, not only inside a given speech, but also among different speeches. Moreover, under a purely methodological perspective, the presented contribution suggests possible ways of generating a linguistic decision-making algorithm

A rank-size approach to the analysis of socio-economics data

Questa tesi \ue8 volta ad investigare due importanti fenomeni, uno naturale ed uno umano. Il primo riguarda i terremoti, mentre il secondo \ue8 legato al contenuto dei discorsi ufficiali dei presidenti americani. Per il primo caso, il nostro obiettivo \ue8 quello di definire un indicatore dei danni economici causati dai terremoti, proponendo un indice calibrato su una lunga serie di magnitudo rilevate in lunghi periodi di tempo. Mentre per il caso dei discorsi presidenziali, vogliamo quantificare il loro impatto sul mercato finanziario, in particolare studiamo l\u2019effetto che essi hanno sull\u2019indice \u201cStandard and Poor\u2019s 500\u201d. Il nostro obiettivo principale \ue8 quello di contribuire nell\u2019ambito delle scelte di politica economica prendendo in considerazione tali fenomeni ed analizzandoli con un approccio diverso ed innovativo. L\u2019analisi esposta in questa tesi \ue8 sviluppata per mezzo di strumenti econofisici strettamente collegati all\u2019ambito dell\u2019analisi \u201crank-size\u201d. Tale analisi consiste nell\u2019uso di una serie di funzioni particolarmente utili per l\u2019esplorazione delle propriet\ue0 di grandi dataset, anche quando essi sono distribuiti nel tempo e hanno bande di errore non perfettamente definite per via di particolari condizioni di campionamento. Nei capitoli che riguardano i terremoti cos\uec come in quelli dedicati all\u2019analisi dei discorsi dei presidenti americani sono mostrati e commentati i risultati di regressioni non lineari impiegate per stimare i coefficienti di varie leggi \u201crank-size\u201d. Tali stime sono state manipolate in modo tale da poter giungere a conclusioni dal rilievo economico. I risultati pi\uf9 robusti sono stati raggiunti grazie alla straordinaria capacit\ue0 di interpretare i dati da parte delle leggi \u201crank-size\u201d. Nell\u2019ambito della valutazione dell\u2019impatto economico dei discorsi presidenziali, un\u2019analisi aggiuntiva \ue8 stata svolta valutando diverse distanze tra serie storiche. In particolare considerando la serie storica delle parole semanticamente legate all\u2019economica e pronunciate dai presidenti americani nel corso della storia e le serie storiche del volume, dei prezzi e dei rendimenti dell\u2019indice \u201cStandard & Poor's 500\u201d. Per questa analisi abbiamo impiegato un approccio probabilistico ed anche uno meramente topologico. Infatti abbiamo misurato l\u2019entropia delle serie storiche e comparato le conclusioni valutando le differenze fra diverse misure di distanza vettoriale

The interconnectedness of the economic content in the speeches of the US Presidents

The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents’ talks belonging to the network’s core share the usage of generic (non-technical) economic locutions like “interest” or “trade”.While the use of more technical and less frequent terms characterizes the periphery (e.g. “yield”). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents’ speeches. This is a post-peer-review, pre-copyedit version of an article published in Annals of Operations Research. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10479-019-03372-

Fake news and propaganda: Trump’s democratic America and Hitler’s national socialist (Nazi) Germany

This paper features an analysis of President Trump’s two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot’s power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama’s last State of the Union address and Hitler’s 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler’s relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler’s speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different

The interconnectedness of the economic content in the speeches of the US Presidents

The speeches stated by influential politicians can have a decisive impact on the future of a country. In particular, the economic content of such speeches affects the economy of countries and their financial markets. For this reason, we examine a novel dataset containing the economic content of 951 speeches stated by 45 US Presidents from George Washington (April 1789) to Donald Trump (February 2017). In doing so, we use an economic glossary carried out by means of text mining techniques. The goal of our study is to examine the structure of significant interconnections within a network obtained from the economic content of presidential speeches. In such a network, nodes are represented by talks and links by values of cosine similarity, the latter computed using the occurrences of the economic terms in the speeches. The resulting network displays a peculiar structure made up of a core (i.e. a set of highly central and densely connected nodes) and a periphery (i.e. a set of non-central and sparsely connected nodes). The presence of different economic dictionaries employed by the Presidents characterize the core-periphery structure. The Presidents' talks belonging to the network's core share the usage of generic (non-technical) economic locutions like "interest" or "trade". While the use of more technical and less frequent terms characterizes the periphery (e.g. "yield" ). Furthermore, the speeches close in time share a common economic dictionary. These results together with the economics glossary usages during the US periods of boom and crisis provide unique insights on the economic content relationships among Presidents' speeches

Fake news and propaganda: Trump's democratic America and Hitler's national socialist (Nazi) Germany

This paper features an analysis of President Trump's two State of the Union addresses, which are analysed by means of various data mining techniques, including sentiment analysis. The intention is to explore the contents and sentiments of the messages contained, the degree to which they differ, and their potential implications for the national mood and state of the economy. We also apply Zipf and Mandelbrot's power law to assess the degree to which they differ from common language patterns. To provide a contrast and some parallel context, analyses are also undertaken of President Obama's last State of the Union address and Hitler's 1933 Berlin Proclamation. The structure of these four political addresses is remarkably similar. The three US Presidential speeches are more positive emotionally than is Hitler's relatively shorter address, which is characterised by a prevalence of negative emotions. Hitler's speech deviates the most from common speech, but all three appear to target their audiences by use of non-complex speech. However, it should be said that the economic circumstances in contemporary America and Germany in the 1930s are vastly different

Slava Ukraini: a psychobiographical case study of Volodymyr Zelenskyy’s public diplomacy discourse

Volodymyr Zelenskyy\u27s public diplomacy during the Russo-Ukrainian conflict was examined in this dissertation. Zelenskyy’s discourse emphasized his action-oriented traits, Ukrainian identity, and nationalism. The study employed LTA, and LIWC-22, for natural language processing analyses of Zelenskyy\u27s public speeches and diplomatic discourse. Zelenskyy demonstrated agency, adaptability, collaboration, and positive language patterns, suggesting confidence and optimism, according to the data. In addition, the research emphasizes how domestic and international factors influence state behavior, as well as how political demands, cultural, historical, and political factors influence Zelenskyy\u27s decision-making. This dissertation sheds light on a global leader\u27s psychobiographical characteristics, beliefs, and motivations during a crisis, thereby advancing leadership and conflict resolution. By incorporating transformational leadership theory into LTA, researchers can gain a better understanding of effective leadership and how it develops strong connections with followers. LTA, LIWC-22, and qualitative coding were used to identify themes and trends in Zelenskyy\u27s speeches. The findings show Zelenskyy\u27s linguistic and leadership traits in public diplomacy, emphasizing the importance of understanding leaders\u27 traits in foreign policy decision-making. Psychobiographical profiles aid scholars in understanding a leader\u27s political views on conflict, their ability to influence events, and how they accomplish their objectives. As a result, perceptions of the state as an actor, as well as foreign policy decisions, must consider the effect of individual leaders. Conclusions include the Brittain-Hale Foreign Policy Analysis Model, based on a heuristic qualitative coding framework; HISTORICAL

Topics of the nationwide phone-ins with Vladimir Putin and their role for public support and Russian economy

Acord transformatiu CRUE-CSICHere we consider several macroeconomic indicators taken from the Federal State Statistics Service (2019). First, there is the inflation rate calculated for each month as the sum of the inflation coefficients for the previous 12 months. Second, the unemployment rate is defined as the proportion of the unemployed in the economically active population. Further, the real wage index is calculated by dividing the nominal wage index by the consumer price index of the same period. Finally, budget expenditures are the funds of the federal and consolidated regional budgets directed to financial support of the tasks and functions of the federal and regional governments.Altres ajuts: This research has been supported by the Russian Science Foundation, project No. 19-18-00262 "Modelling a balanced technological and socio-economic development of the Russian regions".The addresses of national leaders can affect their public support and spur changes in the country's economy. To date, very few studies exist establishing these relationships, and no research has been done on the addresses from Vladimir Putin. In this paper we fill this knowledge gap by analysing the nationwide phone-ins of Putin, a special annual format where he addresses the public, and using structural topic modelling studying their topics over time. Furthermore, we relate these topics to public approval of the president and the government as well as to some Russian macroeconomic indicators such as inflation and budget expenditures. Based on our data containing 1938 responses and almost 250 thousand words, we identify 16 main topics covering areas from healthcare and education through economics to elections and legislation. We find that the topic of foreign affairs has gained in popularity over time the most (from around 4.5% at the beginning to more than 10% starting from 2014). Another topic, consistently gaining weight in the president's statements, is related to solving particular problems of the general public (from 8% to 12.5%) and is significantly correlated with subsequent decrease in the country's unemployment (Pearson's correlation coefficient -0.502). We also find that when the government's support is decreasing, Putin tends to discuss more socially significant topics (e.g., inflation, healthcare, Pearson's coef. around -0.5), while when the support is rising, he speaks more about foreign affairs (Pearson's coef. 0.773). Our study provides first evidence that Vladimir Putin may adapt the content of his phone-in meetings to gather public support and influence the country's economy