The expression of emotions in 20th century books

Medicine, emotience, and reason

Medicine is faced with a number of intractable modern challenges that can be understood in terms of hyper-intellectualization; a compassion crisis, burnout, dehumanization, and lost meaning. These challenges have roots in medical philosophy and indeed general Western philosophy by way of the historic exclusion of human emotion from human reason. The resolution of these medical challenges first requires a novel philosophic schema of human knowledge and reason that incorporates the balanced interaction of human intellect and human emotion. This schema of necessity requires a novel extension of dual-process theory into epistemology in terms of both intellect and emotion each generating a distinct natural kind of knowledge independent of the other as well as how these two forms of mental process together construct human reason. Such a novel philosophic schema is here proposed. This scheme is then applied to the practice of medicine with examples of practical applications with the goal of reformulating medical practice in a more knowledgable, balanced, and healthy way. This schema's expanded epistemology becomes the philosophic foundation for more fully incorporating the humanities in medicine.

Tone in politics is not systematically related to macro trends, ideology, or experience

What explains the variation in tone in politics? Different literatures argue that changes in the tone of politicians reflect changes in the economy, general language, well-being, or ideology. So far, these claims have been empirically tested only in isolation, in single country studies, or with a small subset of indicators. We offer an overarching view by modelling the use of tone in European national parliaments in 7 countries across 30 years. Using a semi-supervised sentiment-topic model to measure polarity and arousal in legislative debates, we show in a preregistered multiverse analysis that the tone in legislative debates is not systematically related to previously claimed factors. We also replicate the absence of such systematic relationships using national leader speeches and parties' election manifestos. There is also no universal trend towards more negativity or emotionality in political language. Overall, our results highlight the importance of multi-lingual and cross-country multiverse analyses for generalizing findings on emotions in politics.

Cognitive fossils: using cultural artifacts to reconstruct psychological changes throughout history

Psychology is crucial for understanding human history. When aggregated, changes in the psychology of individuals - in the intensity of social trust, parental care, or intellectual curiosity - can lead to important changes in institutions, social norms, and cultures. However, studying the role of psychology in shaping human history has been hindered by the difficulty of documenting the psychological traits of people who are no longer alive. Recent developments in psychology suggest that cultural artifacts reflect in part the psychological traits of the individuals who produced or consumed them. Cultural artifacts can thus serve as 'cognitive fossils' - physical imprints of the psychological traits of long-dead people. We review the range of materials available to cognitive and behavioral scientists, and discuss the methods that can be used to recover and quantify changes in psychological traits throughout history.

How Male and Female Literary Authors Write About Affect Across Cultures and Over Historical Periods

A wealth of literature suggests the existence of sex differences in how emotions are experienced, recognized, expressed, and regulated. However, to what extent these differences result from the put in place of stereotypes and social rules is still a matter of debate. Literature is an essential cultural institution, a transposition of the social life of people but also of their intimate affective experiences, which can serve to address questions of psychological relevance. Here, we created a large corpus of literary fiction enriched by authors' metadata to measure the extent to which culture influences how men and women write about emotion. Our results show that even though before the twenty-first century and across 116 countries women more than men have written about affect, starting from 2000, this difference has diminished substantially. Also, in the past, women's narratives were more positively laden and less arousing. While the difference in arousal is ubiquitous and still present nowadays, sex differences in valence vary as a function of culture and have dissolved in recent years. Altogether, these findings suggest that historic evolution is associated with men and women writing similarly about emotions and reveal a sizable impact of culture on the affective characteristics of the lexicon.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-023-00219-9.

Using portraits to quantify the changes of generalized social trust in European history: A replication study

A portrait is an exercise of impression management: the sitter can choose the impression she or he wants to create in the eyes of others': competence, trustworthiness, dominance, etc. Indirectly, this choice informs us about the qualities that were specifically valued at the time the portrait was created. In a previous paper, we have shown that cues of perceived trustworthiness in portraits increased in time during the modern period in Europe, meaning that people probably granted more importance to be seen as a trustworthy person. Moreover, this increase is correlated to economic development. In this study, we aim to replicate this result, using more controlled databases: 1) a newly created database of European head-of-state sovereigns (N = 966, from 1400 to 2020), that is a database of individuals holding the same social position across time and countries, and 2) a database of very high-quality portraits digitized with the same technique, and coming from the same Museum, the Chateau de Versailles database (N = 2,291, from 1483 to 1938). Using mixed effects linear models, we observed in the first dataset that the modeled perceived facial trustworthiness of these sovereigns' faces increased over time (b = 0.182 ± 0.04 s.e.m., t(201) = 4.40, p < 0.001). On the opposite, no effect of time was detected on the portraits of the Château de Versailles (b = - 0.02 ± 0.03 s.e.m., t(759) = - 0.85, p > .250). We conclude by discussing the potential of this new technique to uncover long-term behavioral changes in history, as well as its limitations.

Displaying concerns within telephone triage conversations of callers with chest discomfort in out-of-hours primary care: A conversation analytic study

OBJECTIVES

In primary care out of hours service (OHS-PC), triage nurses ask questions to assign urgency level for medical assessment. A semi-automatic decision tool (the Netherlands Triage Standard, NTS) facilitates triage nurses with key questions, but does not leave much room for paying attention to callers' concerns. We wanted to understand how callers with chest pain formulate their concerns and are helped further during telephone triage.

METHODS

We conducted a conversation analytic study of 68 triage calls from callers with chest discomfort who contacted OHS-PC of which we selected 35 transcripts in which concerns were raised. We analyzed expressions of concerns and the corresponding triage nurse response.

RESULTS

Due to the task-oriented nature of the NTS, callers' concerns were overlooked. For callers, however, discussing concerns was relevant, stressed by the finding that the majority of callers with chest discomfort expressed concerns.

CONCLUSIONS

Interactional difficulties in concern-related discussions arised directly after expressed concerns if not handled adequately, or during the switch to the counseling phase.

PRACTICE IMPLICATIONS

When callers display concerns during telephone triage, we recommend triage nurses to explore them briefly and then return to the sequence of tasks described in the NTS-assisted triage process.

Searching, Navigating, and Recommending Movies through Emotions: A Scoping Review

Movies offer viewers a broad range of emotional experiences, providing entertainment, and meaning. Following the PRISMA-ScR guidelines, we reviewed the literature on digital systems designed to help users search and browse movie libraries and offer recommendations based on emotional content. Our search yielded 83 eligible documents (published between 2000 and 2021). We identified 22 case studies, 34 empirical studies, 26 proof of concept, and one theoretical paper. User transactions (e.g., ratings, tags) were the preferred source of information. The documents examined approached emotions from both a categorical ( $n=35$ ) and dimensional ( $n=18$ ) perspectives, and nine documents offer a combination of both approaches. Although there are several authors mentioned, the references used are frequently dated, and 12 documents do not mention author or model used. We identified 61 words related to emotion or affect. Documents presented on average 1.36 positive terms and 2.64 negative terms. Sentiment analysis ( $n=31$ ) is frequently used for emotion identification, followed by subjective evaluations ( $n=15$ ), movie low-level audio and visual features (n = 11), and face recognition technologies ( $n=8$ ). We discuss limitations and offer a brief review of current emotion models and research.

How to Develop Reliable Instruments to Measure the Cultural Evolution of Preferences and Feelings in History?

While we cannot directly measure the psychological preferences of individuals, and the moral, emotional, and cognitive tendencies of people from the past, we can use cultural artifacts as a window to the zeitgeist of societies in particular historical periods. At present, an increasing number of digitized texts spanning several centuries is available for a computerized analysis. In addition, developments form historical economics have enabled increasingly precise estimations of sociodemographic realities from the past. Crossing these datasets offer a powerful tool to test how the environment changes psychology and vice versa. However, designing the appropriate proxies of relevant psychological constructs is not trivial. The gold standard to measure psychological constructs in modern texts - Linguistic Inquiry and Word Count (LIWC) - has been validated by psychometric experimentation with modern participants. However, as a tool to investigate the psychology of the past, the LIWC is limited in two main aspects: (1) it does not cover the entire range of relevant psychological dimensions and (2) the meaning, spelling, and pragmatic use of certain words depend on the historical period from which the fiction work is sampled. These LIWC limitations make the design of custom tools inevitable. However, without psychometric validation, there is uncertainty regarding what exactly is being measured. To overcome these pitfalls, we suggest several internal and external validation procedures, to be conducted prior to diachronic analyses. First, the semantic adequacy of search terms in bags-of-words approaches should be verified by training semantic vector spaces with the historical text corpus using tools like word2vec. Second, we propose factor analyses to evaluate the internal consistency between distinct bag-of-words proxying the same underlying psychological construct. Third, these proxies can be externally validated using prior knowledge on the differences between genres or other literary dimensions. Finally, while LIWC is limited in the analysis of historical documents, it can be used as a sanity check for external validation of custom measures. This procedure allows a robust estimation of psychological constructs and how they change throughout history. Together with historical economics, it also increases our power in testing the relationship between environmental change and the expression of psychological traits from the past.

Analysis of O2O Teaching Assistant Mode of College English in MOOC Environment

In the past, teaching assistants always focused on the physical environment and actions, resulting in a lot of carbon emissions and material waste. This paper discusses the online and offline integration of college English teaching assistance model, which helps inspire the significance of saving energy and reduce carbon emissions in higher education environment. Our country has been putting education informationization in a very important position. In recent years, the demand of college education informatization is more urgent, which calls for the reform of college classroom teaching mode. "O2O teaching mode" is a teaching mode that integrates online and offline teaching, uses computer information network technology, makes use of network media, and relies on online MOOC platform to carry out online network learning and offline face-to-face classroom learning. "O2O teaching mode" has the characteristics of openness, interactivity, individuality, convenience, and generation, which is conducive to changing students' learning mode and teachers' teaching mode, realizing resource sharing, and improving teaching quality. English is a practical course. In the practice of modern English teaching, it is necessary to maintain and develop the advantages of traditional humanistic teaching, and to make efficient use of the media resources and network resources under the condition of modern educational technology, so as to ensure that English learners have active and sufficient practice opportunities. With the popularity of the Internet and the development of MOOC, more and more English learners begin to learn independently online. English online learning platform has become an indispensable tool for independent learning and daily English teaching. Especially when the Internet is connected with the campus network, the role of the network has changed from teaching auxiliary resources to teaching platform, making online learning become a new way of learning, realizing the interaction of English teaching in the network environment.

Global and Local Trends Affecting the Experience of US and UK Healthcare Professionals during COVID-19: Twitter Text Analysis

Background: Healthcare professionals (HCPs) are on the frontline of fighting the COVID-19 pandemic. Recent reports have indicated that, in addition to facing an increased risk of being infected by the virus, HCPs face an increased risk of suffering from emotional difficulties associated with the pandemic. Therefore, understanding HCPs’ experiences and emotional displays during emergencies is a critical aspect of increasing the surge capacity of communities and nations. Methods: In this study, we analyzed posts published by HCPs on Twitter to infer the content of discourse and emotions of the HCPs in the United States (US) and United Kingdom (UK), before and during the COVID-19 pandemic. The tweets of 25,207 users were analyzed using natural language processing (NLP). Results: Our results indicate that HCPs in the two countries experienced common health, social, and political issues related to the pandemic, reflected in their discussion topics, sentiments, and emotional display. However, the experiences of HCPs in the two countries are also subject to local socio-political trends, as well as cultural norms regarding emotional display. Conclusions: Our results support the potential of utilizing Twitter discourse to monitor and predict public health responses in emergencies.

Evoked and transmitted culture models: Using bayesian methods to infer the evolution of cultural traits in history

A central question in behavioral and social sciences is understanding to what extent cultural traits are inherited from previous generations, transmitted from adjacent populations or produced in response to changes in socioeconomic and ecological conditions. As quantitative diachronic databases recording the evolution of cultural artifacts over many generations are becoming more common, there is a need for appropriate data-driven methods to approach this question. Here we present a new Bayesian method to infer the dynamics of cultural traits in a diachronic dataset. Our method called Evoked-Transmitted Cultural model (ETC) relies on fitting a latent-state model where a cultural trait is a latent variable which guides the production of the cultural artifacts observed in the database. The dynamics of this cultural trait may depend on the value of the cultural traits present in previous generations and in adjacent populations (transmitted culture) and/or on ecological factors (evoked culture). We show how ETC models can be fitted to quantitative diachronic or synchronic datasets, using the Expectation-Maximization algorithm, enabling estimating the relative contribution of vertical transmission, horizontal transmission and evoked component in shaping cultural traits. The method also allows to reconstruct the dynamics of cultural traits in different regions. We tested the performance of the method on synthetic data for two variants of the method (for binary or continuous traits). We found that both variants allow reliable estimates of parameters guiding cultural evolution, and that they outperform purely phylogenetic tools that ignore horizontal transmission and ecological factors. Overall, our method opens new possibilities to reconstruct how culture is shaped from quantitative data, with possible application in cultural history, cultural anthropology, archaeology, historical linguistics and behavioral ecology.

Broad coverage emotion annotation

In this paper we present the emotion annotation of 1.5 billion words Portuguese corpora, publicly available. We motivate the annotation process and detail the decisions made. The resource is evaluated, being applied to different areas: to study Lusophone literature, to obtain paraphrases, and to do genre comparison.

How do you feel? Using natural language processing to automatically rate emotion in psychotherapy

Emotional distress is a common reason for seeking psychotherapy, and sharing emotional material is central to the process of psychotherapy. However, systematic research examining patterns of emotional exchange that occur during psychotherapy sessions is often limited in scale. Traditional methods for identifying emotion in psychotherapy rely on labor-intensive observer ratings, client or therapist ratings obtained before or after sessions, or involve manually extracting ratings of emotion from session transcripts using dictionaries of positive and negative words that do not take the context of a sentence into account. However, recent advances in technology in the area of machine learning algorithms, in particular natural language processing, have made it possible for mental health researchers to identify sentiment, or emotion, in therapist-client interactions on a large scale that would be unattainable with more traditional methods. As an attempt to extend prior findings from Tanana et al. (2016), we compared their previous sentiment model with a common dictionary-based psychotherapy model, LIWC, and a new NLP model, BERT. We used the human ratings from a database of 97,497 utterances from psychotherapy to train the BERT model. Our findings revealed that the unigram sentiment model (kappa = 0.31) outperformed LIWC (kappa = 0.25), and ultimately BERT outperformed both models (kappa = 0.48).

The rise of prosociality in fiction preceded democratic revolutions in Early Modern Europe

The English and French Revolutions represent a turning point in history, marking the beginning of the modern rise of democracy. Recent advances in cultural evolution have put forward the idea that the early modern revolutions may be the product of a long-term psychological shift, from hierarchical and dominance-based interactions to democratic and trust-based relationships. In this study, we tested this hypothesis by analyzing theater plays during the early modern period in England and France. We found an increase in cooperation-related words over time relative to dominance-related words in both countries. Furthermore, we found that the accelerated rise of cooperation-related words preceded both the English Civil War (1642) and the French Revolution (1789). Finally, we found that rising per capita gross domestic product (GDPpc) generally led to an increase in cooperation-related words. These results highlight the likely role of long-term psychological and economic changes in explaining the rise of early modern democracies.

Sentiment analysis as a measure of conservation culture in scientific literature

Culturomics is emerging as an important field within science, as a way to measure attitudes and beliefs and their dynamics across time and space via quantitative analysis of digitized data from literature, news, film, social media, and more. Sentiment analysis is a culturomics tool that, within the last decade, has provided a means to quantify the polarity of attitudes expressed within various media. Conservation science is a crisis discipline; therefore, accurate and effective communication are paramount. We investigated how conservation scientists communicate their findings through scientific journal articles. We analyzed 15,001 abstracts from articles published from 1998 to 2017 in 6 conservation-focused journals selected based on indexing in scientific databases. Articles were categorized by year, focal taxa, and the conservation status of the focal species. We calculated mean sentiment score for each abstract (mean adjusted z score) based on 4 lexicons (Jockers-Rinker, National Research Council, Bing, and AFINN). We found a significant positive annual trend in the sentiment scores of articles. We also observed a significant trend toward increasing negativity along the spectrum of conservation status categories (i.e., from least concern to extinct). There were some clear differences in the sentiments with which research on different taxa was reported, however. For example, abstracts mentioning lobe finned fishes tended to have high sentiment scores, which could be related to the rediscovery of the coelacanth driving a positive narrative. Contrastingly, abstracts mentioning elasmobranchs had low scores, possibly reflecting the negative sentiment score associated with the word shark. Sentiment analysis has applications in science, especially as it pertains to conservation psychology, and we suggest a new science-based lexicon be developed specifically for the field of conservation.

A complex network approach to political analysis: Application to the Brazilian Chamber of Deputies

In this paper, we introduce a network-based methodology to study how political entities evolve over time. We constructed networks of voting data from the Brazilian Chamber of Deputies, where deputies are nodes and edges are represented by voting similarity among deputies. The Brazilian Chamber of deputies is characterized by a multi-party political system. Thus, we would expect a broad spectrum of ideas to be represented. Our results, however, revealed that plurality of ideas is not present at all: the effective number of communities representing ideas based on agreement/disagreement in propositions is about 3 over the entire studied time span. The obtained results also revealed different patterns of coalitions between distinct parties. Finally, we also found signs of early party isolation before presidential impeachment proceedings effectively started. We believe that the proposed framework could be used to complement the study of political dynamics and even applied in similar social networks where individuals are organized in a complex manner.

Cultural evolution of emotional expression in 50 years of song lyrics

Popular music offers a rich source of data that provides insights into long-term cultural evolutionary dynamics. One major trend in popular music, as well as other cultural products such as literary fiction, is an increase over time in negatively valenced emotional content, and a decrease in positively valenced emotional content. Here we use two large datasets containing lyrics from n = 4913 and n = 159,015 pop songs respectively and spanning 1965-2015, to test whether cultural transmission biases derived from the cultural evolution literature can explain this trend towards emotional negativity. We find some evidence of content bias (negative lyrics do better in the charts), prestige bias (best-selling artists are copied) and success bias (best-selling songs are copied) in the proliferation of negative lyrics. However, the effects of prestige and success bias largely disappear when unbiased transmission is included in the models, which assumes that the occurrence of negative lyrics is predicted by their past frequency. We conclude that the proliferation of negative song lyrics may be explained partly by content bias, and partly by undirected, unbiased cultural transmission.

Guideline for improving the reliability of Google Ngram studies: Evidence from religious terms

The Google Books Ngram Viewer (Google Ngram) is a search engine that charts word frequencies from a large corpus of books and thereby allows for the examination of cultural change as it is reflected in books. While the tool's massive corpus of data (about 8 million books or 6% of all books ever published) has been used in various scientific studies, concerns about the accuracy of results have simultaneously emerged. This paper reviews the literature and serves as a guideline for improving Google Ngram studies by suggesting five methodological procedures suited to increase the reliability of results. In particular, we recommend the use of (I) different language corpora, (II) cross-checks on different corpora from the same language, (III) word inflections, (IV) synonyms, and (V) a standardization procedure that accounts for both the influx of data and unequal weights of word frequencies. Further, we outline how to combine these procedures and address the risk of potential biases arising from censorship and propaganda. As an example of the proposed procedures, we examine the cross-cultural expression of religion via religious terms for the years 1900 to 2000. Special emphasis is placed on the situation during World War II. In line with the strand of literature that emphasizes the decline of collectivistic values, our results suggest an overall decrease of religion's importance. However, religion re-gains importance during times of crisis such as World War II. By comparing the results obtained through the different methods, we illustrate that applying and particularly combining our suggested procedures increase the reliability of results and prevents authors from deriving wrong assumptions.

International visibility as determinants of foreign direct investment: An empirical study of Chinese Provinces

While previous studies use economic and institutional variables to explain transnational investment operations, we argue that regionally-specific international visibility can significantly influence the investment decisions of foreign firms with spatial and temporal dynamics. Empirically, we extract the usage frequency of the names of all of the Chinese provinces in millions of English-language books from Google Books N-gram corpus to construct the index of international visibility as a proxy measurement of international prominence. Results from dynamic panel data analysis (1994-2004) using the Generalized Method of Moments demonstrate that the level of international visibility of a province has a positive effect on the inflows of foreign direct investments, controlling for a set of economic and institutional factors. Further analyses show that this visibility effect varies with different state images of China formed in various historical periods and is stronger with regard to inland provinces compared to coastal provinces. Our results are robust across alternative corpora and different model specifications.

Measuring Emotion in Parliamentary Debates with Automated Textual Analysis

An impressive breadth of interdisciplinary research suggests that emotions have an influence on human behavior. Nonetheless, we still know very little about the emotional states of those actors whose daily decisions have a lasting impact on our societies: politicians in parliament. We address this question by making use of methods of natural language processing and a digitized corpus of text data spanning a century of parliamentary debates in the United Kingdom. We use this approach to examine changes in aggregate levels of emotional polarity in the British parliament, and to test a hypothesis about the emotional response of politicians to economic recessions. Our findings suggest that, contrary to popular belief, the mood of politicians has become more positive during the past decades, and that variations in emotional polarity can be predicted by the state of the national economy.

Linguistic positivity in historical texts reflects dynamic environmental and psychological factors

People use more positive words than negative words. Referred to as "linguistic positivity bias" (LPB), this effect has been found across cultures and languages, prompting the conclusion that it is a panhuman tendency. However, although multiple competing explanations of LPB have been proposed, there is still no consensus on what mechanism(s) generate LPB or even on whether it is driven primarily by universal cognitive features or by environmental factors. In this work we propose that LPB has remained unresolved because previous research has neglected an essential dimension of language: time. In four studies conducted with two independent, time-stamped text corpora (Google books Ngrams and the New York Times), we found that LPB in American English has decreased during the last two centuries. We also observed dynamic fluctuations in LPB that were predicted by changes in objective environment, i.e., war and economic hardships, and by changes in national subjective happiness. In addition to providing evidence that LPB is a dynamic phenomenon, these results suggest that cognitive mechanisms alone cannot account for the observed dynamic fluctuations in LPB. At the least, LPB likely arises from multiple interacting mechanisms involving subjective, objective, and societal factors. In addition to having theoretical significance, our results demonstrate the value of newly available data sources in addressing long-standing scientific questions.

Birth of the cool: a two-centuries decline in emotional expression in Anglophone fiction

The presence of emotional words and content in stories has been shown to enhance a story's memorability, and its cultural success. Yet, recent cultural trends run in the opposite direction. Using the Google Books corpus, coupled with two metadata-rich corpora of Anglophone fiction books, we show a decrease in emotionality in English-speaking literature starting plausibly in the nineteenth century. We show that this decrease cannot be explained by changes unrelated to emotionality (such as demographic dynamics concerning age or gender balance, changes in vocabulary richness, or changes in the prevalence of literary genres), and that, in our three corpora, the decrease is driven almost entirely by a decline in the proportion of positive emotion-related words, while the frequency of negative emotion-related words shows little if any decline. Consistently with previous studies, we also find a link between ageing and negative emotionality at the individual level.

Economic performance and public concerns about social class in twentieth-century books

What is the association between macroeconomic conditions and public perceptions of social class? Applying a novel approach based on the Google Books N-gram corpus, this study addresses the relationship between public concerns about social class and economic conditions throughout the twentieth century. The usage of class-related words/phrases, or "literary references to class," in American English-language books is related to US economic performance and income inequality. The findings of this study demonstrate that economic conditions play a significant role in literary references to class throughout the century, whereas income inequality does not. Similar results are obtained from further analyses using alternative measures of class concerns as well as different corpora of English Fiction and the New York Times. We add to the social class literature by showing that the long-term temporal dynamics of an economy can be exhibited by aggregate class concerns. The application of massive culture-wide content analysis using data of unprecedented size also represents a contribution to the literature.

Temporal Relationships Between Individualism–Collectivism and the Economy in Soviet Russia

Collectivism and individualism are commonly used to delineate societies that differ in their cultural values and patterns of social behavior, prioritizing the relative importance of the group and the individual, respectively. Collectivist and individualist expression is likely to be intricately linked with the political and economic history of a society. Scholars have proposed mechanisms for both positive and negative correlations between economic growth and a culture of either individualism or collectivism. Here, we consider these relationships across the dramatic history of 20th- and early 21st-century Russia (1901-2009), spanning the late Russian Empire, the communist state, and the growth of capitalism. We sample Russian speakers to identify common Russian words expressing individualism or collectivism, and examine the changing frequencies of these terms in Russian publications collected in Google’s Ngram corpus. We correlate normalized individualism and collectivism expression against published estimates of economic growth (GDP and net material product [NMP]) available between 1961 and 1995, finding high collectivist expression and economic growth rate followed by the correlated decline of both prior to the end of Soviet system. Temporal trends in the published expression of individualism and collectivism, in addition to their correlations with estimated economic growth rates, are examined in relation to the change in economic and political structures, ideology and public discourse. We also compare our sampled Russian-language terms for individualism and collectivism with Twenge et al.’s equivalent collection from American English speakers.

Cultural Value Shifting in Pronoun Use

By investigating the use of first-person pronouns in nine languages using the Google Ngram Database, we examined the degree to which different cultural values skewed toward individualism or collectivism over a span of 59 years. We found that in eight of nine languages (British English being the exception), first-person singular pronouns (vs. first-person plural pronouns) have become increasingly prevalent, which in turn points to a rising sense of individualism. British English showed a U-shaped curve trend in the use of first-person singular pronouns (vs. first-person plural pronouns). Although they initially decreased, British English’s first-person singular pronouns (vs. first-person plural pronouns) use was higher than most other languages throughout the whole period. Chinese displayed a fluctuating pattern wherein the use of first-person singular pronouns (vs. first-person plural pronouns) increased in recent periods. The dynamics of cultural change and culture diversity were discussed.

Emotional Sentence Annotation Helps Predict Fiction Genre

Fiction, a prime form of entertainment, has evolved into multiple genres which one can broadly attribute to different forms of stories. In this paper, we examine the hypothesis that works of fiction can be characterised by the emotions they portray. To investigate this hypothesis, we use the work of fictions in the Project Gutenberg and we attribute basic emotional content to each individual sentence using Ekman's model. A time-smoothed version of the emotional content for each basic emotion is used to train extremely randomized trees. We show through 10-fold Cross-Validation that the emotional content of each work of fiction can help identify each genre with significantly higher probability than random. We also show that the most important differentiator between genre novels is fear.

Rank diversity of languages: generic behavior in computational linguistics

Statistical studies of languages have focused on the rank-frequency distribution of words. Instead, we introduce here a measure of how word ranks change in time and call this distribution rank diversity. We calculate this diversity for books published in six European languages since 1800, and find that it follows a universal lognormal distribution. Based on the mean and standard deviation associated with the lognormal distribution, we define three different word regimes of languages: "heads" consist of words which almost do not change their rank in time, "bodies" are words of general use, while "tails" are comprised by context-specific words and vary their rank considerably in time. The heads and bodies reflect the size of language cores identified by linguists for basic communication. We propose a Gaussian random walk model which reproduces the rank variation of words in time and thus the diversity. Rank diversity of words can be understood as the result of random variations in rank, where the size of the variation depends on the rank itself. We find that the core size is similar for all languages studied.

Computational psychotherapy research: scaling up the evaluation of patient-provider interactions

In psychotherapy, the patient-provider interaction contains the treatment's active ingredients. However, the technology for analyzing the content of this interaction has not fundamentally changed in decades, limiting both the scale and specificity of psychotherapy research. New methods are required to "scale up" to larger evaluation tasks and "drill down" into the raw linguistic data of patient-therapist interactions. In the current article, we demonstrate the utility of statistical text analysis models called topic models for discovering the underlying linguistic structure in psychotherapy. Topic models identify semantic themes (or topics) in a collection of documents (here, transcripts). We used topic models to summarize and visualize 1,553 psychotherapy and drug therapy (i.e., medication management) transcripts. Results showed that topic models identified clinically relevant content, including affective, relational, and intervention related topics. In addition, topic models learned to identify specific types of therapist statements associated with treatment-related codes (e.g., different treatment approaches, patient-therapist discussions about the therapeutic relationship). Visualizations of semantic similarity across sessions indicate that topic models identify content that discriminates between broad classes of therapy (e.g., cognitive-behavioral therapy vs. psychodynamic therapy). Finally, predictive modeling demonstrated that topic model-derived features can classify therapy type with a high degree of accuracy. Computational psychotherapy research has the potential to scale up the study of psychotherapy to thousands of sessions at a time. We conclude by discussing the implications of computational methods such as topic models for the future of psychotherapy research and practice.

Authors' response. more on maps, terrains, and behaviors

In a recent New York Times column (April 15, 2013), David Brooks discussed how the big-data agenda lacks a coherent framework of social theory – a deficiency that the Bentley, O'Brien, and Brock (henceforth BOB) model was meant to overcome. Or, stated less pretentiously, the model was meant as a first step in that direction – a map that hopefully would serve as a minimal, practical, and accessible framework that behavioral scientists could use to analyze big data. Rather than treating big data as a record of, and also a predictor of, where and when certain behaviors might take place, the BOB model is interested in what big data reveal about how decisions are being made, how collective behavior evolves from daily to decadal time scales, and how this varies across communities.

Books average previous decade of economic misery

For the 20^th century since the Depression, we find a strong correlation between a ‘literary misery index’ derived from English language books and a moving average of the previous decade of the annual U.S. economic misery index, which is the sum of inflation and unemployment rates. We find a peak in the goodness of fit at 11 years for the moving average. The fit between the two misery indices holds when using different techniques to measure the literary misery index, and this fit is significantly better than other possible correlations with different emotion indices. To check the robustness of the results, we also analysed books written in German language and obtained very similar correlations with the German economic misery index. The results suggest that millions of books published every year average the authors' shared economic experiences over the past decade.