History Repeats: Historical Phase Recognition from Short Texts
Authors: Fabio Celli, Valerio Basile (Gruppo Maggioli, Università di Torino, 2024)
Topic: Sentiment, Emotion, Irony, Hate

Abstract
This paper introduces a new multi-class classification task: the prediction of the Structural-Demographic phase of historical cycles - such as growth, impoverishment and crisis - from text describing historical events. To achieve this, we leveraged data from the Seshat project, annotated it following specific guidelines and then evaluated the consistency between three annotators. The classification experiments, with transformers and Large Language Models, show that 2 of 5 phases can be detected with good accuracy. We believe that this task could have a great impact on comparative history and can be helped by event extraction in NLP.

Introduction
In the last decade, at least since Brexit AUTHOR, many countries in the world experienced a generalized polarization and phenomena of toxic language online have grown AUTHOR. Hate speech AUTHOR, misogyny AUTHOR, conspiracy theories AUTHOR and related phenomena are just visible manifestations of deep structural social crises, ushering in periods of shifting world order AUTHOR. While crises may appear sudden, they are often rooted in underlying factors like demographics, geopolitics, technological advancements, and historical-economic cycles. Using scientific method, mathematical modelling and the Structural Demographic Theory (SDT) AUTHOR it was possible to formalise secular cycles AUTHOR, that typically last between 75 to 100 years AUTHOR, and predict outbreaks of political instability in complex societies based on the rate of past crises AUTHOR. The SDT defines three actors and five phases of the secular cycle. The three key actors are: \centering \includegraphics[width=\linewidth]{cycles.png \item The population, which is the source of the society's resources and manpower, represents approximately 90% of the entire society and is the part that follows instructions to produce goods and wealth, consuming only a small part of it. \item The elites, who typically cover around 8% of the society, are the groups of people in charge of finding potential solutions to the problems of the society and are eligible to become part of the state. Who is considered part of the elite and how someone gains or loses elite status depends on the type of government and the power dynamics within a society. \item The state, formed by roughly 2% of the society, is the government that enforces its will and manages resources from the population. It is composed by one or more elite groups, depending on the social structure, and it crystallizes the culture to keep the society alive. The actors interact in five phases during the secular cycle, progressively increasing social and political instability: \item The growth phase. During this phase a fresh and effective culture creates social cohesion, the economy is growing rapidly and the state is expanding its control over the population. This leads to increased economic prosperity and stability but raises the problem of sustainability. Periods of reconstruction immediately following wars, like post-war Italy in the 1950s, are examples of this phase. \item The population immiseration phase. The population continues to grow in number while the economy slows down. This happens because over the long term the rate of return on capital is typically greater than the growth rate of population salaries AUTHOR, as result the elites gets richer and the population gets poorer. Moreover, demography has a strong impact on the wealth of the population: the more workers of the same type are available, the less likely their wages are to grow. The state's ability to extract resources from the population reaches its limits in this phase. This can lead to increasing inequality, and social unrest begins. United States in the 1890s and 1970s are an example of this phase. \item The elite overproduction phase. The population tries to access the elite ranks but overloads the social lift mechanisms and yields a reduced capability of the elite to solve problems in the society, which raise the probability to have societal instability. USSR in the 1950s and US in the 1990s are examples of this phase. \item The state stress phase. The state's ability to govern the population and foster cooperation between population and elites begins to decline, and the elites become increasingly fragmented. This can lead to widespread violence and civil war. Moreover, the state tends to be in financial distress as a consequence of slowed economy and internal fragmentation, thus any triggering event that the state cannot manage can break into a crisis. Germany in the 1920s is an example. \item The crisis, collapse or recovery phase. The state is either reformed by the elites who find an agreement or overthrown by internal or external forces. At the end of this phase a new social equilibrium is found and a new period of stability begins, restarting the cycle. Examples are France in the 1790s, UK in the 1940s, US in the 1860s under civil war and also in 1930s under New Deal reforms. \centering \includegraphics[width=1\linewidth]{chronosnga.png The dynamics described by the SDT are represented in figure fig:cyc AUTHOR. SDT has been used to explain a wide range of historical events, including the French Revolution, the American Civil War AUTHOR, the fall of the Qing Dynasty AUTHOR, the Russian Revolution and the instability in the US in recent years. In this paper we propose a novel multi-class classification task: given a text describing the historical events of a decade, find the appropriate SDT phase label. To do so we exploited historical data from the Seshat project, produced textual descriptions for decades in the history of human societies and annotated each decade with SDT phases following specific annotation guidelines. We computed inter-annotator agreement between 3 annotators and experimented with LLMs in classification. The paper is structured as follows: in Section data we will describe the data, the guidelines for the annotation (Section annot), the classification experiments in Section experiments, the conclusion and direction for future work in Section conc.

Conclusion
We introduced a new classification task named historical phase recognition. We believe that, once we improve their performance, classification algorithms trained for this task will allow us to automatically annotate many more polities with secular cycles with a potential disruptive improvement in the study of comparative history. We believe that inter-annotator agreement can be further improved by having domain experts annotate the data. Additionally, the automatic extraction of events from short historical texts, or the definition of guidelines for their annotation, can be a valuable tool both in the annotation and classification tasks. By combining these two approaches, we can improve the dataset and make it more reliable. For the future we plan to improve the performance of classification by including the temporal interdependence factors, and to improve the inter annotator agreement, also calculating the agreement between labels generated by models and by humans. In the future it would be interesting to add event structure annotations such as TimeML in Chronos. %, and apply historical phase recognition from present day news headlines of different countries, The poor performance in zero-shot classification using an LLM is likely a function of the sophisticated reasoning and world knowledge required to perform the task. The LLM could benefit from more advanced prompting strategies (e.g. few-shot or chain-of-thoughts) or even supervision in the form of fine-tuning. The Chronos dataset is accessible online in viewer/commenter mode}. Edit and download access is available under request.