Pontic Greek cuisine: the most common foods, ingredients, and dishes presented in cookbooks and folklore literature

Introduction The Pontic Greeks, besides their long and distinguished history, have a special and important culture and identity, elements of which are still preserved and active by their descendants a century after their settlement in Greece. One element of their identity and culture is their basic yet diverse cuisine, which is an important and recognized local cuisine in contemporary Greece. This study aimed to identify the most common foods, ingredients, and dishes found in Greek Pontic Cuisine. Methods Six cookbooks, two cooking magazines, four folklore books, and four folklore magazines were reviewed in this study. A considerable amount of data was collected and processed using a text analysis tool. Results and discussion The study provides the most frequently encountered dishes, foods, and ingredients that feature in the publications. The most common dishes are soups, including tanomenon sorva (soup with coarse grains, salty strained yogurt, and mint). Among other dishes, siron (a pre-baked filo-based pastry dish), chavitz (a thick corn dish resembling porridge), and foustoron (an omelet with fresh cow butter) are quite common. Common staples are anchovies and greens. In cookbooks and cookery magazines, ingredients include butter, wheat, eggs, tomatoes, milk, bulgur, corn-flour, and cheese. Meanwhile, the study publications are an excellent way of passing down traditional food knowledge intergenerational, as they are largely descended from Pontic Greek progenitors. Conclusion After analyzing all the publications, it was declared that dairy products, grains, and vegetables were commonly used in Pontic cuisine. It was concluded that cookbooks are crucial for the preservation of the Greek Pontic culinary tradition.

Exploring Ancient Networks

A small archive of texts from ancient Iraq is used to demonstrate an approach to network analysis in which traditional close reading and computational text analysis go hand-in-hand. The computational methods produce tables and graphs that link back to online editions of the primary material, enabling the user to check the results.

Parental Mentalizing During a Pandemic: Use of Mental-State Language on Parenting Social Media Before and During the COVID-19 Pandemic

The COVID-19 pandemic has been a demanding caregiving context for parents, particularly during lockdowns. In this study, we examined parental mentalization, parents’ proclivity to consider their own and their child’s mental states, during the pandemic, as manifested in mental-state language (MSL) on parenting social media. Parenting-related posts on Reddit from two time periods in the pandemic in 2020, March to April (lockdown) and July to August (postlockdown), were compared with time-matched control periods in 2019. MSL and self–other references were measured using text-analysis methods. Parental mentalization content decreased during the pandemic: Posts referred less to mental activities and to other people during the COVID-19 pandemic and showed decreased affective MSL, cognitive MSL, and self-references specifically during lockdown. Father-specific subreddits exhibited strongest declines in mentalization content, whereas mother-specific subreddits exhibited smaller changes. Implications on understanding associations between caregiving contexts and parental mentalization, gender differences, and the value of using social-media data to study parenting and mentalizing are discussed.

My Home is my Castle? The Role of Living Arrangements on Experiencing the COVID-19 Pandemic: Evidence From Germany

During the early months of the COVID-19 pandemic in Germany, social restrictions and social distancing policies forced large parts of social life to take place within the household. However, comparatively little is known about how private living situations shaped individuals experiences of this crisis. To investigate this issue, we analyze how experiences and concerns vary across living arrangements along two dimensions that may be associated with social disadvantage: loneliness and care. In doing so, we employ quantitative text analysis on open-ended questions from survey data on a sample of 1,073 individuals living in Germany. We focus our analyses on four different household structures: living alone, shared living without children, living with a partner and children, and single parents. We find that single parents (who are primarily single mothers) are at high risk of experiencing care-related worries, particularly regarding their financial situation, while individuals living alone are most likely to report feelings of loneliness. Those individuals living in shared houses, with or without children, had the lowest risk of experiencing both loneliness and care-related worries. These findings illustrate that the living situation at home substantially impacts how individuals experienced and coped with the pandemic situation during the first wave of the pandemic.

Gender and Ethnicity Bias in Medicine: A Text Analysis of 1.8 Million Critical Care Records

Gender and ethnicity biases are pervasive across many societal domains including politics, employment, and medicine. Such biases will facilitate inequalities until they are revealed and mitigated at scale. To this end, over 1.8 million records from a large US hospital were evaluated with natural language processing techniques in search of gender and ethnicity bias indicators. Consistent with non-linguistic evidence of bias in medicine, physicians often focused on the emotions of female compared to male patients and focused more on the scientific diagnoses of male compared to female patients. Physicians reported on fewer emotions for Black patients versus White patients and physicians demonstrated the greatest need to work through diagnoses for Black women compared to other patients. This work provides evidence of gender and ethnicity biases in medicine as communicated by physicians in the field and requires the critical examination of institutions that perpetuate bias in social systems.

Complex Contact Network of Patients at the Beginning of an Epidemic Outbreak: An Analysis Based on 1218 COVID-19 Cases in China

The spread of viruses essentially occurs through the interaction and contact between people, which is closely related to the network of interpersonal relationships. Based on the epidemiological investigations of 1218 COVID-19 cases in eight areas of China, we use text analysis, social network analysis and visualization methods to construct a dynamic contact network of the epidemic. We analyze the corresponding demographic characteristics, network indicators, and structural characteristics of this network. We found that more than 65% of cases are likely to be infected by a strong relationship, and nearly 40% of cases have family members infected at the same time. The overall connectivity of the contact network is low, but there are still some clustered infections. In terms of the degree distribution, most cases’ degrees are concentrated between 0 and 2, which is relatively low, and only a few ones have a higher degree value. The degree distribution also conforms to the power law distribution, indicating the network is a scale-free network. There are 17 cases with a degree greater than 10, and these cluster infections are usually caused by local transmission. The first implication of this research is we find that the COVID-19 spread is closely related to social structures by applying computational sociological methods for infectious disease studies; the second implication is to confirm that text analysis can quickly visualize the spread trajectory at the beginning of an epidemic.

Machiavelli’s Political Thought and Its Inspiration—Text Analysis Based on the Prince

Machiavelli is one of the founders of modern bourgeois political theory. The birth of his masterpiece The Prince creates a new pattern of western political thought, which marks the first time that political science has escaped from the bondage of religion and ethics. At the same time, Machiavelli is also named “Machiavelliism”. The so-called “no means to achieve the purpose” has become the greatest misunderstanding of Machiavelli. Based on the prince analysis of Machiavelli’s political thought, around his national unity of Italy launched the national regime, military, monarchy and other aspects of thinking.

The Influence of Cognitive Biases and Financial Factors on Forecast Accuracy of Analysts

The objective of this study was to jointly analyze the importance of cognitive and financial factors in the accuracy of profit forecasting by analysts. Data from publicly traded Brazilian companies in 2019 were obtained. We used text analysis to assess the cognitive biases from the qualitative reports of analysts. Further, we analyzed the data using statistical regression learning methods and statistical classification learning methods, such as Multiple Linear Regression (MRL), k-dependence Bayesian (k-DB), and Random Forest (RF). The Bayesian inference and classification methods allow an expansion of the research line, especially in the area of machine learning, which can benefit from the examples of factors addressed in this research. The results indicated that, among cognitive biases, optimism had a negative relationship with forecasting accuracy while anchoring bias had a positive relationship. Commonality, to a lesser extent, also had a positive relationship with the analyst’s accuracy. Among financial factors, the most important aspects in the accuracy of analysts were volatility, indebtedness, and profitability. Age of the company, fair value, American Depositary Receipts (ADRs), performance, and loss were still important but on a smaller scale. The results of the RF models showed a greater explanatory power. This research sheds light on the cognitive as well as financial aspects that influence the analyst’s accuracy, jointly using text analysis and machine learning methods, capable of improving the explanatory power of predictive models, together with the use of training models followed by testing.