1,141 research outputs found
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
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
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
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