Impact of lexical and sentiment factors on the popularity of scientific papers

We investigate how textual properties of scientific papers relate to the number of citations they receive. Our main finding is that correlations are non-linear and affect differently most-cited and typical papers. For instance, we find that in most journals short titles correlate positively with citations only for the most cited papers, for typical papers the correlation is in most cases negative. Our analysis of 6 different factors, calculated both at the title and abstract level of 4.3 million papers in over 1500 journals, reveals the number of authors, and the length and complexity of the abstract, as having the strongest (positive) influence on the number of citations.Comment: 9 pages, 3 figures, 3 table

Temporal Taylor's scaling of facial electromyography and electrodermal activity in the course of emotional stimulation

High frequency psychophysiological data create a challenge for quantitative modeling based on Big Data tools since they reflect the complexity of processes taking place in human body and its responses to external events. Here we present studies of fluctuations in facial electromyography (fEMG) and electrodermal activity (EDA) massive time series and changes of such signals in the course of emotional stimulation. Zygomaticus major (ZYG, "smiling" muscle) activity, corrugator supercilii (COR, "frowning"bmuscle) activity, and phasic skin conductance (PHSC, sweating) levels of 65 participants were recorded during experiments that involved exposure to emotional stimuli (i.e., IAPS images, reading and writing messages on an artificial online discussion board). Temporal Taylor's fluctuations scaling were found when signals for various participants and during various types of emotional events were compared. Values of scaling exponents were close to 1, suggesting an external origin of system dynamics and/or strong interactions between system's basic elements (e.g., muscle fibres). Our statistical analysis shows that the scaling exponents enable identification of high valence and arousal levels in ZYG and COR signals

Determining crucial factors for the popularity of scientific articles

Using a set of over 70.000 records from PLOS One journal consisting of 37 lexical, sentiment and bibliographic variables we perform analysis backed with machine learning methods to predict the class of popularity of scientific papers defined by the number of times they have been viewed. Our study shows correlations among the features and recovers a threshold for the number of views that results in the best prediction results in terms of Matthew's correlation coefficient. Moreover, by creating a variable importance plot for random forest classifier, we are able to reduce the number of features while keeping similar predictability and determine crucial factors responsible for the popularity.Comment: 13 pages, 6 figure

QUESTIONING IF THE LITERARY NARRATIVE AND REAL-LIFE STORIES OVERLAP WITH TODAY'S REALITIES: THE EXAMPLE OF "ANNE WITH AN E" TV SERIAL

In this study, there are three key topics are explored and discussed. It is first and foremost vital to examine whether the series in question is appropriate for viewers who are older than eight years old. In the second evaluation, it is examined whether contemporary realities and literary narratives have any overlap. This implies that issues related to history, society, psychology, and modernity are approached from a fresh angle. Last but not least, it has to do with the potential social effects of adjusting historical tales to the present. This entails reassessing the past or historical narratives from the viewpoint of the present. However, the key inquiry is: How closely does a film's core meaning align with the meaning that is revealed after seeing it? What about a movie's relevance alters when it's watched again after some time? This study addresses the film-audience connection in two separate ways under the category of "meaning." The relationship between the meaning that viewers derive from the movie's content while watching it is the main topic of discussion. The second is the text's complementary meaning as revealed by its social, psychological, or historical elements.  Article visualizations

Complexity Science in Human Change

This reprint encompasses fourteen contributions that offer avenues towards a better understanding of complex systems in human behavior. The phenomena studied here are generally pattern formation processes that originate in social interaction and psychotherapy. Several accounts are also given of the coordination in body movements and in physiological, neuronal and linguistic processes. A common denominator of such pattern formation is that complexity and entropy of the respective systems become reduced spontaneously, which is the hallmark of self-organization. The various methodological approaches of how to model such processes are presented in some detail. Results from the various methods are systematically compared and discussed. Among these approaches are algorithms for the quantification of synchrony by cross-correlational statistics, surrogate control procedures, recurrence mapping and network models.This volume offers an informative and sophisticated resource for scholars of human change, and as well for students at advanced levels, from graduate to post-doctoral. The reprint is multidisciplinary in nature, binding together the fields of medicine, psychology, physics, and neuroscience

Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties

Human values are crucial to human decision-making. Value pluralism is the view that multiple correct values may be held in tension with one another (e.g., when considering lying to a friend to protect their feelings, how does one balance honesty with friendship?). As statistical learners, AI systems fit to averages by default, washing out these potentially irreducible value conflicts. To improve AI systems to better reflect value pluralism, the first-order challenge is to explore the extent to which AI systems can model pluralistic human values, rights, and duties as well as their interaction. We introduce ValuePrism, a large-scale dataset of 218k values, rights, and duties connected to 31k human-written situations. ValuePrism's contextualized values are generated by GPT-4 and deemed high-quality by human annotators 91% of the time. We conduct a large-scale study with annotators across diverse social and demographic backgrounds to try to understand whose values are represented. With ValuePrism, we build Kaleido, an open, light-weight, and structured language-based multi-task model that generates, explains, and assesses the relevance and valence (i.e., support or oppose) of human values, rights, and duties within a specific context. Humans prefer the sets of values output by our system over the teacher GPT-4, finding them more accurate and with broader coverage. In addition, we demonstrate that Kaleido can help explain variability in human decision-making by outputting contrasting values. Finally, we show that Kaleido's representations transfer to other philosophical frameworks and datasets, confirming the benefit of an explicit, modular, and interpretable approach to value pluralism. We hope that our work will serve as a step to making more explicit the implicit values behind human decision-making and to steering AI systems to make decisions that are more in accordance with them

Dating Victorians: an experimental approach to stylochronometry

A thesis submitted for the degree of Doctor of Philosophy ofthe University of LutonThe writing style of a number of authors writing in English was empirically investigated for the purpose of detecting stylistic patterns in relation to advancing age. The aim was to identify the type of stylistic markers among lexical, syntactical, phonemic, entropic, character-based, and content ones that would be most able to discriminate between early, middle, and late works of the selected authors, and the best classification or prediction algorithm most suited for this task. Two pilot studies were initially conducted. The first one concentrated on Christina Georgina Rossetti and Edgar Allan Poe from whom personal letters and poetry were selected as the genres of study, along with a limited selection of variables. Results suggested that authors and genre vary inconsistently. The second pilot study was based on Shakespeare's plays using a wider selection of variables to assess their discriminating power in relation to a past study. It was observed that the selected variables were of satisfactory predictive power, hence judged suitable for the task. Subsequently, four experiments were conducted using the variables tested in the second pilot study and personal correspondence and poetry from two additional authors, Edna St Vincent Millay and William Butler Yeats. Stepwise multiple linear regression and regression trees were selected to deal with the first two prediction experiments, and ordinal logistic regression and artificial neural networks for two classification experiments. The first experiment revealed inconsistency in accuracy of prediction and total number of variables in the final models affected by differences in authorship and genre. The second experiment revealed inconsistencies for the same factors in terms of accuracy only. The third experiment showed total number of variables in the model and error in the final model to be affected in various degrees by authorship, genre, different variable types and order in which the variables had been calculated. The last experiment had all measurements affected by the four factors. Examination of whether differences in method within each task play an important part revealed significant influences of method, authorship, and genre for the prediction problems, whereas all factors including method and various interactions dominated in the classification problems. Given the current data and methods used, as well as the results obtained, generalizable conclusions for the wider author population have been avoided