Topic modeling—a text‐mining technique often used to uncover thematic structures in large collections of texts—has been increasingly frequently used in the context of the analysis of scholarly output. In this study, we construct a corpus of 19,488 texts published since 1971 in seven leading journals in the field of bioethics and philosophy of medicine, and we use a machine learning algorithm to identify almost 100 topics representing distinct themes of interest in the field. On the basis of intertopic correlations, (...) we group the content‐based topics into eight clusters, thus providing a novel, fine‐grained intellectual map of bioethics and philosophy of medicine. Moreover, we conduct a number of diachronic analyses, examining how the “prominence” of different topics has changed across time. In this way, we are able to observe the distinct patterns in which bioethics and philosophy of medicine have evolved and changed their focus over the past half a century. (shrink)

The COVID-19 pandemic has created an urgent need for volunteers to complement overwhelmed public health systems. This study aims to explore Chinese people’s attitudes toward volunteerism amid the COVID-19 pandemic. To this end, we identify the latent topics in volunteerism-related microblogs on Weibo, the Chinese equivalent of Twitter using the topic modeling analysis via Latent Dirichlet Allocation. To further investigate the public sentiment toward the topics generated by LDA, we also conducted sentiment analysis on the sample posts using the (...) open-source natural language processing technique from Baidu. Through an in-depth analysis of 91,933 Weibo posts, this study captures 10 topics that are, in turn, distributed into five factors associated with volunteerism in China as motive fulfillment, fear of COVID-19, individual characteristic, government support, and community effect. The results show that motive fulfillment, government support, and community effect are the factors that could enhance positive attitudes toward volunteerism since the topics related to these factors report high proportions of positive emotion. Fear of COVID-19 and individual characteristic are the factors inducing negative sentiment toward volunteerism as the topics related to these factors show relatively high proportions of negative emotion. The provision of tailored strategies based on the factors could potentially enhance Chinese people’s willingness to participate in volunteer activities during the COVID-19 pandemic. (shrink)

This article offers a comprehensive overview of Business Sustainability (BuS), and directly addresses the lack of consensus around this important concept. Through a mixed-methods approach, we conduct the first systematic literature review of BuS employing Latent Dirichlet Allocation (LDA) topic modeling to uncover hidden thematic structures, Narrative Synthesis to refine and extend BuS definitions within different contexts, and the LDA-HSIM method to classify topics and design a new framework. We analyzed an extensive dataset comprising 92,311 articles sourced from 11,579 (...) journal outlets. From this dataset, we identified 9,561 articles suitable for LDA topic modeling by applying funnel criteria, focusing on articles with clear theoretical underpinnings. A text extraction technique enabled us to identify and analyze theories used in BuS studies. This analysis revealed 150 underlying theories that advance the BuS concept across different research topics. The study contributes to BuS theory development with great potential to improve ethical decision-making by establishing meaningful, context-specific definitions and providing clear guidance for future researchers in selecting appropriate theoretical perspectives for their work. We identify research gaps, propose a prioritized research agenda focused on theory development, and formulate key implications for practitioners and policymakers. This study demonstrates the effectiveness of machine learning methods in conducting large-scale literature reviews to accelerate theoretical advancements and generate research agendas. (shrink)

The primary purpose of this study is to examine the relationship between the dynamic capabilities embedded in ESG management, which are being pursued by global companies, and corporate performance amid increasing uncertainty. Furthermore, the secondary purpose is to examine the function of environmental uncertainty moderating the DCs-performance relationship. Concerning the analysis tool, this study employs topic modeling with Word2Vec embedding that analyzes unstructured data. This was employed as an alternative method beyond the limitations of the traditional approach, i.e., survey (...) or interview. A DCs dictionary was constructed by redesigning the 12 detailed dimensions of Teece’s DCs into 10 dimensions, and then time series scores of individual global companies were extracted by applying this dictionary to the sustainability reports of 97 companies. Sustainability reports of 153 companies among Fortune Global 500 companies announced in 2020 were originally collected, but in the process of collecting additional financial data about these companies from OSIRIS, a total of only 97 companies was selected in the end due to omission of data. A fixed effect panel analysis was conducted, and the main findings are as follows: First, the DCs embedded in ESG management have a positive or negative direct effect on corporate performance. In particular, a statistically significant relationship was not observed in the innovation oriented capabilities, whereas a statistically significant positive relationship was observed in the customer oriented capabilities. Second, uncertainty moderates the relationship between DCs and corporate performance positively or negatively. Interestingly, the moderating effect of uncertainty only appears in the function of the sensing and reconfiguring capabilities. From this, it can be seen that the function of DCs, which is embedded in the ESG management of global companies, is limited due to the imbalance between the sensing-seizing-reconfiguring capabilities. These findings imply that, despite the positive function of DCs embedded in ESG management, costs and benefits occur at the same time, and DCs can improve performance only if there is an organizational adaptation strategy suitable for uncertainty. Accordingly, business managers need to recognize the importance of pursuing sensing-seizing-reconfiguring capabilities in a balanced way to improve corporate performance through ESG management under uncertainty. (shrink)

Mobile app stores provide an extremely rich source of information on app descriptions, characteristics, and usage, and analyzing these data provides insights and a deeper understanding of the nature of apps. However, manual analysis of this vast amount of information on mobile apps is not a simple and straightforward task; it is costly in terms of human effort and time. Computational methods such as topic modeling can provide an efficient and satisfactory approach to mobile app information analysis. Topic (...) modeling is a type of statistical modeling technique for discovering abstract topics that occur in a set of documents. This study explores the relationship between features of Arabic apps and investigates how well the current predefined Google Play app categories represent the type and genre of Arabic mobile apps. Based on the textual app description analysis, we aim to design and develop a sustainable classification system using the Latent Dirichlet Allocation method of topic modeling in order to cover the Arabic apps classification in Google Play app store. Our study supports the hypothesis that the textual app descriptions are effective in suggesting new categories for Arabic mobile apps in Google Play app store. Also, the results indicated that the current classification on Google Play app store is not suitable for our case study “Arabic apps,” as well as it is not sustainable, as it can not cover the new app types including Arabic apps. This study offers an important contribution to Arabic app analysis and design, to improve app search and exploration in several domains such as business, marketing, and technical development. Furthermore, it provides insights for the future of Arabic app research and provides guidance for the development of an Arabic app dashboard that will support users on how to select an app based on their specific needs. (shrink)

What is philosophy of science? Numerous manuals, anthologies or essays provide carefully reconstructed vantage points on the discipline that have been gained through expert and piecemeal historical analyses. In this paper, we address the question from a complementary perspective: we target the content of one major journal of the field—Philosophy of Science—and apply unsupervised text-mining methods to its complete corpus, from its start in 1934 until 2015. By running topic-modeling algorithms over the full-text corpus, we identified 126 key research (...) topics that span across 82 years. We also tracked their evolution and fluctuating significance over time in the journal articles. Our results concur with and document known and lesser-known episodes of the philosophy of science, including the rise and fall of logic and language-related topics, the relative stability of a metaphysical and ontological questioning (space and time, causation, natural kinds, realism), the significance of epistemological issues about the nature of scientific knowledge as well as the rise of a recent philosophy of biology and other trends. These analyses exemplify how computational text-mining methods can be used to provide an empirical large-scale and data-driven perspective on the history of philosophy of science that is complementary to other current historical approaches. (shrink)

Despite the growing significance of corporate social responsibility (CSR) in corporate public relations (PR) strategies, there has been a paucity of comprehensive reviews of the extant CSR literature within the PR discipline. The aim of this study is to investigate the relationship between the concept of CSR and the discipline of PR through a hybrid concept-discipline-focused review. We employed latent dirichlet allocation (LDA) via topic modeling as a novel approach for intellectual structure analysis. The dataset for this review comprised (...) 246 peer-reviewed articles on CSR published between 1975 and 2022. Based on the LDA results, five topics were identified and discussed: the political CSR stream, social media-oriented CSR, consumer-oriented CSR, CSR reporting, and business-oriented CSR. Additionally, implications for both academic and practical applications were drawn, along with recommendations for future research. This study is pioneering in its contribution to the literature and practice of CSR-related PR research, offering insights into the intellectual structures and trends within this field. (shrink)

Misinformation surrounding the Sustainable Development Goals (SDGs) has contributed to the formation of misbeliefs among the public. The purpose of this paper is to investigate public sentiment and misbeliefs about the SDGs on the YouTube platform.,The authors extracted 8,016 comments from YouTube videos associated with SDGs. The authors used a pre-trained Python library NRC lexicon for sentiment and emotion analysis, and to extract latent topics, the authors used BERTopic for topic modeling.,The authors found eight emotions, with negativity outweighing positivity, (...) in the comment section. In addition, the authors identified the top 20 topics discussing various SDGs and SDG-related misbeliefs.,The authors reported topics related to public misbeliefs about SDGs and associated keywords. These keywords can be used to formulate social media content moderation strategies to screen out content that creates these misbeliefs. The result of hierarchical clustering can be used to devise and optimize response strategies by governments and policymakers to counter public misbeliefs.,This study represents an initial endeavor to gain a deeper understanding of the public’s misbeliefs regarding SDGs. The authors identified novel misbeliefs about SDGs that previous literature has not studied. Furthermore, the authors introduce an algorithm BERTopic for topic modeling that leverages transformer architecture for context-aware topic modeling. (shrink)

Though only established as a discipline since the 1970s, philosophy of biology has already triggered investigations about its own history The Oxford handbook of philosophy of biology, Oxford University Press, New York, pp 11–33, 2008). When it comes to assessing the road since travelled—the research questions that have been pursued—manuals and ontologies also offer specific viewpoints, highlighting dedicated domains of inquiry and select work. In this article, we propose to approach the history of the philosophy of biology with a complementary (...) data-driven perspective that makes use of statistical algorithms applied to the complete full-text corpus of one major journal of the field—Biology and Philosophy—from its launch in 1986 up until 2017. By running text-mining and topic-modeling algorithms, we identified 67 key research topics that span across these 32 years. We also investigated the evolution of these topics over time and their fluctuating significance in the journal articles. Our results concur with known episodes or traits of the discipline—for instance, the significance of evolution-related topics or the decrease of articles with a marked historical dimension—but also highlight a diversity of topics that is much richer than what is usually acknowledged. (shrink)

As a discipline of its own, the philosophy of science can be traced back to the founding of its academic journals, some of which go back to the first half of the twentieth century. While the discipline has been the object of many historical studies, notably focusing on specific schools or major figures of the field, little work has focused on the journals themselves. Here, we investigate contemporary philosophy of science by means of computational text-mining approaches: we apply topic-modeling (...) algorithms to eight major philosophy of science journals, from the 1930s up until 2017. Based on the full-text content of some 15,897 articles, we identified 25 research themes and 8 thematic clusters that show how the research agenda of the philosophy of science has changed in its content over the course of the last eight decades, up to the philosophy of science we now know. We also show how each one of the journals contributed in its own way to this thematic evolution. (shrink)

Conducting emotion analysis and generating users' feedback from social media platforms may help understand their emotional responses to video products, such as a documentary on the lockdown of Wuhan during COVID-19. The results of emotion analysis could be used to make further user recommendations for marketing purposes. In our study, we try to understand how users respond to a documentary through YouTube comments. We chose “The lockdown: One month in Wuhan” YouTube documentary, and applied emotion analysis as well as a (...) machine learning approach to the comments. We first cleaned the data and then introduced an emotion analysis based on the statistical characteristics and lexicon combination. After that, we applied the Latent Dirichlet Allocation topic modeling approach to further generate main topics with keywords from the comments and visualized the distribution by visualizing the topics. The result shows trust, joy, and anticipation are the most prominent emotions dominating the comments. The major three themes, which account for 70% of all comments, are discussing stories about fighting against the virus, medical workers being heroes, and medical workers being respected. Further discussion has been conducted on the changing of different sentiments over time for the ongoing health crisis. This study proves that emotion analysis and LDA topic modeling could be used to generate explanations of users' opinions and feelings about video products, which could support user recommendations in marketing. (shrink)

In online communities, such as Twitter, Facebook, or Reddit, millions of pieces of contents are generated by users every day, and these user-generated contents show a great variety of topics discussed that make the online community vivid and attractive. However, the reasons why UGCs show great variety and how a firm can influence this variety was unknown, which had been an obstacle to understanding and managing UGCs’ variety. This study fills these two gaps based on variety-seeking theory and topic (...) modeling, which is a technique in machine learning. We extract, quantitatively, the topic of the UGCs using topic modeling and divide UGCs into two types: single topic and multiple topics. The user’s tendency to choose the type of UGC is used to measure variety-seeking behavior. We found that users have an intrinsic preference for variety when producing UGCs; the more single topic UGCs were produced in the past, the higher the probability of producing multiple topics UGC and the lower the probability of producing single topic UGC would be in the next, and vice versa. Furthermore, we discussed the effect of language/linguistic style matching between firm feedbacks and UGCs on users’ variety-seeking tendencies in UGCs’ production. This study makes three contributions: broadening variety-seeking theory to new behavior, that is content production behavior, and the results demonstrated that people would show a variety-seeking behavior in producing UGCs. a new feasible method to measure the variety of UGCs by using topic modeling to extract the topics of UGCs and then measure the variety-seeking behavior in producing UGCs by analyzing the choice between single topic and multiple topics. guidance for the firm to alter LSM of feedbacks to influence the variety of UGCs. (shrink)

This book uses computer modeling to investigate trends in what is published in leading philosophy journals over the last century and a half. The notable trends include the rise of realism from a fringe view to the mainstream metaphysical outlook, the increase in specialization, and the increasing depth of integration between philosophy and physical sciences. It also contains a guide to how to do similar investigations, and discussions of the strengths and weaknesses of the approach.

This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation and supervised LDA approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the (...) class='Hi'>topic “guest conflicts” in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners. (shrink)

This article explores and critically evaluates the potential contribution to discourse studies of topic modelling, a group of machine learning methods which have been used with the aim of automatically discovering thematic information in large collections of texts. We critically evaluate the utility of the thematic grouping of texts into ‘topics’ emerging from a large collection of online patient comments about the National Health Service in England. We take two approaches to this, one inspired by methods adopted in (...) existing topic modelling research and the other using more established methods of discourse analysis. In the study, we compare the insights produced by each approach and consider the extent to which the automatically generated topics might be of use to discourse analysts attempting to organise and study sizeable datasets. We found that the topic modelling approach was able to group texts into ‘topics’ that were truly thematically coherent with a mixed degree of success, while the more traditional approach to discourse analysis consistently provided a more nuanced perspective on the data which was ultimately closer to the ‘reality’ of the texts it contains. This study thus highlights issues concerning the use of topic modelling and offers recommendations and caveats to researchers employing such approaches to studying discourse in the future. (shrink)

Sallie McFague’s theological models construct a tensive relationship between conceptual structures and symbolic, metaphorical language to interpret the defining and elusive aspects of theological phenomena and loci. Computational models of language can extend and formalize the conceptual structures of theological models to develop computer-augmented interpretations of theological texts. Previously unclear is whether computational models can retain the tensive symbolism essential for theological investigation. I demonstrate affirmatively by constructing a computational topic model of the moral theology of Thomas Aquinas from (...) Summa Theologica (Second Part, in English translation) useful for interpreting not only the Thomistic text but also recent papal encyclicals. (shrink)

Topic modelling is a popular unsupervised method for text processing that provides interpretable document representation. One of the most high-level approaches is additively regularized topic models (ARTM). This method features better quality than other methods due to its flexibility and advanced regularization abilities. However, it is challenging to find an optimal learning strategy to create high-quality topics because a user needs to select the regularizers with their values and determine the order of application. Moreover, it may require (...) many real runs or model training which makes this task time consuming. At the current moment, there is a lack of research on parameter optimization for ARTM-based models. Our work proposes an approach that formalizes the learning strategy into a vector of parameters which can be solved with evolutionary approach. We also propose a surrogate-based modification which utilizes machine learning methods that makes the approach for parameters search time efficient. We investigate different optimization algorithms (evolutionary and Bayesian) and their modifications with surrogates in application to topic modelling optimization using the proposed learning strategy approach. An experimental study conducted on English and Russian datasets indicates that the proposed approaches are able to find high-quality parameter solutions for ARTM and substantially reduce the execution time of the search. (shrink)

In this paper, we describe a model that learns semantic representations from the distributional statistics of language. This model, however, goes beyond the common bag‐of‐words paradigm, and infers semantic representations by taking into account the inherent sequential nature of linguistic data. The model we describe, which we refer to as a Hidden Markov Topics model, is a natural extension of the current state of the art in Bayesian bag‐of‐words models, that is, the Topics model of Griffiths, Steyvers, and Tenenbaum (2007), (...) preserving its strengths while extending its scope to incorporate more fine‐grained linguistic information. (shrink)

A significant portion of the world’s text is tagged by readers on social bookmarking websites. Credit attribution is an inherent problem in these corpora because most pages have multiple tags, but the tags do not always apply with equal specificity across the whole document. Solving the credit attribution problem requires associating each word in a document with the most appropriate tags and vice versa. This paper introduces Labeled LDA, a topic model that constrains Latent Dirichlet Allocation by defining a (...) one-to-one correspondence between LDA’s latent topics and user tags. This allows Labeled LDA to directly learn word-tag correspondences. We demonstrate Labeled LDA’s improved expressiveness over traditional LDA with visualizations of a corpus of tagged web pages from del.icio.us. Labeled LDA outperforms SVMs by more than 3 to 1 when extracting tag-specific document snippets. As a multi-label text classifier, our model is competitive with a discriminative baseline on a variety of datasets. (shrink)

Selected response items and constructed response items are often found in the same test. Conventional psychometric models for these two types of items typically focus on using the scores for correctness of the responses. Recent research suggests, however, that more information may be available from the CR items than just scores for correctness. In this study, we describe an approach in which a statistical topic model along with a diagnostic classification model was applied to a mixed item format formative (...) test of English and Language Arts. The DCM was used to estimate students’ mastery status of reading skills. These mastery statuses were then included in a topic model as covariates to predict students’ use of each of the latent topics in their written answers to a CR item. This approach enabled investigation of the effects of mastery status of reading skills on writing patterns. Results indicated that one of the skills, Integration of Knowledge and Ideas, helped detect and explain students’ writing patterns with respect to students’ use of individual topics. (shrink)

We propose a new approach for tracing value change. Value change may lead to a mismatch between current value priorities in society and the values for which technologies were designed in the past, such as energy technologies based on fossil fuels, which were developed when sustainability was not considered a very important value. Better anticipating value change is essential to avoid a lack of social acceptance and moral acceptability of technologies. While value change can be studied historically and qualitatively, we (...) propose a more quantitative approach that uses large text corpora. It uses probabilistic topic models, which allow us to trace values that are latent. We demonstrate the approach for five types of value change in technology. Our approach is useful for testing hypotheses about value change, such as verifying whether value change has occurred and identifying patterns of value change. The approach can be used to trace value change for various technologies and text corpora, including scientific articles, newspaper articles, and policy documents. (shrink)

Abbildungsverzeichnis 8Abkürzungsverzeichnis 9I Verfahrensentwicklung: Topic-informierte Diskursanalyse zur Analyse von Diskurstransformationen im Zeitverlauf1 Einleitung 112 Forschungsstand 202.1 Wissenschaftliche Dichotomien in der Krise 222.1.1 Big Data und das Aufbrechen etablierter Forschungsparadigmen 222.1.2 Von der Notwendigkeit eines Digitalen Forschungsparadigmas 242.2 Digital Humanities zwischen Transparadigmatischem Forschungsprogramm und paradigmatischer Positionierung 252.2.1 Digital Humanities als eigenständige Wissenschaftsdisziplin? 252.2.2 Digital Humanities zwischen digitalem Forschungsparadigma und methodischer Neutralität 272.3 Methodenstreit 2.0: Digital Humanities zwischen 'End of Theory' und 'Anything goes' 292.3.1 Datengeleitete versus theoriegeleitete Forschung 302.3.2 End of (...) Theory in den Computational Sciences? 332.3.3 Anything goes - Methodenanarchismus als digitales Paradigma? 352.4 Digital Humanities zwischen 'Digital' und 'Humanities' 392.4.1 Der Qualitative Turn in den Digital Humanities 392.4.2 Cultural Turn in den Digital Humanities als mögliche Brückenbildung? 402.5 Bridging the Digital Gap - Dritte Wege für die Digital Humanities 423 Bridging the Paradigm Gap - Grundlegung eines integrativen Forschungsansatzes 433.1 Von inkommensurablen Paradigmen... 443.2...über eine Übersetzung des sozialwissenschaftlichen Forschungsprozesses in die Digital Humanities 463.2.1 Der sozialwissenschaftliche Forschungsprozess im Mixed-Methods Design 473.2.2 Vier Positionierungen im Forschungsprozess 473.3...zur Konzeptionierung eines integrativen Forschungsansatzes im Mixed-Methods-Design 494 Epistemologische Ausrichtung und theoretische Perspektive 504.1 Sozialer Wandel als sozialer Konstruktionsprozess 524.1.1 Sozialkonstruktivistische Tradition - Wissenssoziologie nach Berger und Luckmann 534.1.2 Vom Sozialkonstruktivismus zum Diskurskonstruktivismus 554.2 Sozialer Wandel als diskursiver Konstruktionsprozess 574.2.1 Diskursbegriff 584.2.2 Eigenschaften von Diskursen 604.2.3 Regeln des Diskurses 634.3 Diskursforschung meets Digital Humanities 645 Methodologische Ausrichtung 665.1 Bridging the Methodological Gap - Text-Mining meets Diskursforschung 665.1.1 Text-Mining-Verfahren 685.1.2 Text-Mining meets Diskursforschung 705.2 Mixed-Methods meets Digital Humanities 715.2.1 Mixed-Methods-Forschung als Drittes Forschungsparadigma 715.2.2 Mixed-Methods-Forschung innerhalb einer pragmatistischen Ausrichtung 726 Forschungsdesign 766.1 Designs in der Mixed-Methods-Forschung 766.2 Das Exploratory Design 796.3 Erweiterung des Exploratory Design 816.4 Forschungsdesign für eine Topic-informierte Diskursanalyse 826.4.1 Variation 1: Sichtung des Forschungsstandes als qualitative Exploration 826.4.2 Variation 2: Erweiterung um eine quantitative Exploration 836.4.3 Variation 3: Erweiterung um eine dritte, qualitative Phase 836.5 Übersetzung des Forschungsdesigns in die Digital Humanities 846.5.1 Distant Reading 856.5.2 Das Interpretationsproblem im Distant Reading-Verfahren 866.5.3 Blended Reading als Überbrückungsmöglichkeit 866.5.4 Close Reading 876.6 Überblick Forschungsdesign 886.7 Verfahrensablauf 887 Methodik 957.1 Sozialwissenschaftliche Diskursanalyse 967.2 Die Kombination aus Diskursanalyse und Text-Mining-Verfahren 987.3 Topic-Modeling-Verfahren 99. (shrink)

As a full-blown research topic, numerical cognition is investigated by a variety of disciplines including cognitive science, developmental and educational psychology, linguistics, anthropology and, more recently, biology and neuroscience. However, despite the great progress achieved by such a broad and diversified scientific inquiry, we are still lacking a comprehensive theory that could explain how numerical concepts are learned by the human brain. In this perspective, I argue that computer simulation should have a primary role in filling this gap because (...) it allows identifying the finer-grained computational mechanisms underlying complex behavior and cognition. Modeling efforts will be most effective if carried out at cross-disciplinary intersections, as attested by the recent success in simulating human cognition using techniques developed in the fields of artificial intelligence and machine learning. In this respect, deep learning models have provided valuable insights into our most basic quantification abilities, showing how numerosity perception could emerge in multi-layered neural networks that learn the statistical structure of their visual environment. Nevertheless, this modeling approach has not yet scaled to more sophisticated cognitive skills that are foundational to higher-level mathematical thinking, such as those involving the use of symbolic numbers and arithmetic principles. I will discuss promising directions to push deep learning into this uncharted territory. If successful, such endeavor would allow simulating the acquisition of numerical concepts in its full complexity, guiding empirical investigation on the richest soil and possibly offering far-reaching implications for educational practice. (shrink)

Among other things, Baumard et al.'s considers the enforcement and establishment of moral norms, the interpersonal comparison of welfare, and the structure of fairness norms. This commentary draws attention to the relevance of the game theory literature to the first and second topic, and the social psychology literature to the third topic.

During the past two decades non-cooperative game theory has become a central topic in economic theory. Many scholars have contributed to this revolution, none more than John Nash. Following the publication of von Neumann and Morgenstern's book, it was Nash's papers in the early fifties which pointed the way for future research in game theory. The notion of Nash equilibrium is indispensable. Nash's formulation of the bargaining problem and the Nash bargaining solution constitute the cornerstone of modern bargaining theory. (...) His insights into the non-cooperative foundations of cooperative game theory initiated an area of research known as the Nash program. Nash's analysis of the demand game in which he uses a perturbation of a game to select an equilibrium inspired the construction of several refinements of the notion of Nash equilibrium. A scholar's influence does not necessarily qualify him for a Nobel prize. One may argue that such awards are a social institution established to serve social goals. It is legitimate to ask what message the Swedish Academy sends to the scientific community and the rest of the world. (shrink)

How can the development of ideas in a scientific field be studied over time? We apply unsupervised topic modeling to the ACL Anthology to analyze historical trends in the field of Computational Linguistics from 1978 to 2006. We induce topic clusters using Latent Dirichlet Allocation, and examine the strength of each topic over time. Our methods find trends in the field including the rise of probabilistic methods starting in 1988, a steady increase in applications, and a sharp (...) decline of research in semantics and understanding between 1978 and 2001, possibly rising again after 2001. We also introduce a model of the diversity of ideas, topic entropy, using it to show that COLING is a more diverse conference than ACL, but that both conferences as well as EMNLP are becoming broader over time. Finally, we apply Jensen-Shannon divergence of topic distributions to show that all three conferences are converging in the topics they cover. (shrink)

Conversation, talk, the communicative process, can be compared to the symphonic play of a piece of music. There is an orchestra, the musicians, whose tones and notes must flow, complement and harmonize with one another. There is a main theme. As the music builds variations on the theme are played, and new themes and subthemes are introduced. The basinett responds to the strings, the bass emphasizes the mood of the violin, while the french horn adds a new melody. The subtlety (...) and evenness of flow, the seeming simplicity of the piece, the rich interleaving of subdevelopments, variations, tangents, and recapitulations, are what make a piece of music great.Coherent communication is much the same. It looks easy. It flows. It seems effortless. In reality, of course, it is rich and complex, in both form and content. As in music, conversants may begin a conversation slowly, tentatively, with some simple (or complex) topic in mind. Slowly the momentum builds, the connections surface, and the themes become defined. As in music, these themes are then elaborated upon in various ways. Topic elaborations often spawn their own variations. New themes and subthemes are introduced. All the while, as in music, original themes reappear and are interwoven with the new ones. The twists and turns of the conversation seem to occur effortlessly.In music, there is usually one composer in control of which instrument plays next; one composer who is in charge of what the instrument will say. There is a single coordinator of theme introductions, developments, variations, and interweavings. The composition is done off-line and usually involves editing and re-editing for flaws. In discourse, however, we have on-line development with multiple parties responsible for flow coordination. All parties have to organize their own turns, themes, and variations, in addition to their being able to integrate these items into co-conversants'.In their book, “Understanding Computers and Cognition,” Winograd and Flores (1987) cite the following from Gadamer (Winograd and Flores, 1987, p. 32):It is not so much our judgments as it is our prejudices that constitute our being ... the historicity of our existence entails that prejudices, in the literal sense of the word, constitute the initial directedness of our whole ability to experience. Prejudices are biases of our openness to the world. They are simply conditions whereby we experience something — whereby what we encounter says something to us. (Gadamer, Philosophical Hermeneutics, 1976, p. 9). (shrink)

Prevention is a pervasive phenomenon. It is about blocking an effect before it happens or stopping it as it unfolds: vaccines prevent (the unfolding of) diseases; seat belts prevent events causing serious injuries; circuit breaks prevent the manifestation of overcurrents. Many disciplines in the information sciences deal with modeling and reasoning about prevention. Examples include risk and security management as well as medical and legal informatics. Having a proper conceptualization of this phenomenon is crucial for devising proper modeling mechanisms and (...) tools to support these disciplines. Forming such a conceptualization is a matter of Formal Ontology. In fact, prevention and related notions have become a topic of interest in this area. In this paper, with the support of Unified Foundational Ontology (UFO), we conduct an ontological analysis of this and other related notions, namely, the notions of countermeasures and countermeasure mechanisms, including the notion of antidotes. As a result of this conceptual clarification process, we propose an ontology-based reusable module extending UFO and capturing the relations between these elements. Finally, we employ this module to address a few cases in risk management. (shrink)

The following list contains a survey of some important and recent research in modeling face-to-face conversation. The list below is a presented as a guide to the literature by topic and date; we include complete citations afterwards in alphabetical order. For brevity, research works are keyed by first author and date only (we use these keys on the slides as well as in this list). Of course, most papers are multiply authored. The list is not intended to be exhaustive. (...) Our primary aim is simply to provide bibliographic information for all the research that we will refer to during the ESSLLI class itself. The entries also provide a sampling from ongoing research projects so that you can get an overall sense of the state of the field and begin to follow up topics of particular interest to you. (shrink)

The consumption of meat is a topic of concern because of its environmental and ethical implications. Consequently, plant-based meat alternatives have gained popularity. However, the consumption of these alternatives remains limited and understanding the factors that influence consumers’ intentions to consume them is vital. Current study aims to examine the crucial factors (i.e., knowledge of sustainable food production, problem awareness, outcome efficacy, descriptive social norms, injunctive social norms, and personal norms) that affect consumers’ intentions to consume plant-based meat alternatives, (...) promoting sustainable and ethical food choices. For this purpose, an online survey was conducted and data from 397 participants in China were analyzed using partial least squares structural equation modeling. The findings revealed that knowledge of sustainable food production, problem awareness, outcome efficacy, descriptive social norms, injunctive social norms, and personal norms had significant positive influences on the intention to consume plant-based meat alternatives. Findings can assist policymakers and practitioners in developing targeted strategies to promote sustainable and ethical food choices among consumers, such as improving their knowledge of sustainable food production, raising awareness of problems, promoting social norms, and fostering personal norms related to plant-based meat alternatives consumption. (shrink)

Modeling is a relatively new topic in biblical and related subjects—it was first introduced in the 1970s—and it is controversial because the application of social-scientific models raises the difficult question of the cultural gap between the present societies, where the models are usually developed, and the ancient cultural context to which the models are applied.Because biblical and related studies may not belong to the most familiar scholarly fields of the readers of this journal, I first sketch an overall picture (...) of the development of the discipline and its main methods (Section 2). The subsequent sections summarize the arguments presented for and against modeling in biblical studies (Section 3), and discuss .. (shrink)

We study the coverage of European funds by the mass media and the relationship between media sentiment and public sentiment. We analyze 31,570 media articles published across all European Union (EU) countries between January 1, 2020, and December 31, 2023 — a period significantly shaped by the COVID-19 pandemic and major EU funding initiatives —using the BERTopic modeling technique. We find that public discussions related to European funds can be categorized into 11 distinct topics, ranging from project developments in Central (...) and Eastern Europe to employment and economic measures. Our results show that during the period studied, public sentiment towards European funds is generally stable yet negative, with notable fluctuations corresponding to specific events. Significant positive and negative peaks in sentiment are observed during periods associated with successful project implementations or corruption scandals. We also find significant geographical variation in media coverage and sentiment across EU member states. While Greece, Croatia, and Bulgaria exhibit high media coverage and more negative sentiment, Denmark, Ireland, and Portugal display more positive media sentiments. Our results suggest a negative relationship between media sentiment scores and the proportion of negative responses concerning the EU and the European Parliament (i.e., a proxy for Euroscepticism). (shrink)

This paper focuses on the inferential configuration of arguments, generally referred to as argument scheme. After outlining our approach, denominated Argumentum Model of Topics (AMT, see Rigotti and Greco Morasso 2006, 2009; Rigotti 2006, 2008, 2009), we compare it to other modern and contemporary approaches, to eventually illustrate some advantages offered by it. In spite of the evident connection with the tradition of topics, emerging also from AMT’s denomination, its involvement in the contemporary dialogue on argument schemes should not be (...) overlooked. The model builds in particular on the theoretical and methodological perspective of pragma-dialectics in its extended version, reconciling dialectic and rhetoric; nevertheless, it also takes into account numerous other contributions to the study of argument schemes. Aiming at a representation of argument schemes able to monitor the inferential cohesion and completeness of arguments, AMT focuses on two components of argument scheme that could be distinguished, readapting pragma-dialectical terms, as procedural and material respectively. The procedural component is based on the semantic-ontological structure, which generates the inferential connection from which the logical form of the argument is derived. The material component integrates into the argument scheme the implicit and explicit premises bound to the contextual common ground (Rigotti 2006). In this paper, the comparison of the AMT to other approaches focuses on the inferential configuration of arguments and not on the typologies of argument schemes and on the principles they are based on, which the authors intend to tackle in a further paper. (shrink)

Prevention is a pervasive phenomenon. It is about blocking an effect before it happens or stopping it as it unfolds: vaccines prevent (the unfolding of) diseases; seat belts prevent events causing serious injuries; circuit breaks prevent the manifestation of overcurrents. Many disciplines in the information sciences deal with modeling and reasoning about prevention. Examples include risk and security management as well as medical and legal informatics. Having a proper conceptualization of this phenomenon is crucial for devising proper modeling mechanisms and (...) tools to support these disciplines. Forming such a conceptualization is a matter of Formal Ontology. In fact, prevention and related notions have become a topic of interest in this area. In this paper, with the support of Unified Foundational Ontology (UFO), we conduct an ontological analysis of this and other related notions, namely, the notions of countermeasures and countermeasure mechanisms, including the notion of antidotes. As a result of this conceptual clarification process, we propose an ontology-based reusable module extending UFO and capturing the relations between these elements. Finally, we employ this module to address a few cases in risk management. (shrink)

This dissertation investigates some philosophical issues using computational models. Chapter 1 presents a Lewis-Skyrms signaling game that can exhibit a type of compositionality novel to the signaling game literature. The structure of the signaling game is motivated by an analogy to the alarm calls of putty-nosed monkeys (Cercopithecus nictitans). Putty-nosed monkeys display a compositional system of alarm calls with a semantics that is sensitive to the ordering of terms. This sensitivity to the ordering of terms has not been previously modeled (...) with a Lewis-Skyrms signaling game literature. Signaling games are valued for showing how communicative systems can arise with minimal learning tools. Simulation results show that basic (Roth-Erev) reinforcement learning is sufficient for the acquisition of a compositional signaling system sensitive to the ordering of terms. Chapter 2 investigates social epistemology in the context of an effect of cognitive biases called the illusory truth effect. The illusory truth effect is exhibited when repeated exposure to a statement increases an individual's credence in that statement. While most investigations of the illusory truth effect focus on individuals' belief formation, humans typically form beliefs within a social structure. This is particularly relevant because various social structures can give rise to repeated exposure to statements; e.g. a popular book might recurringly be discussed in one's social circle, or one or two foundational papers might always be cited by a particular lab. So, how does the illusory truth effect influence learning and belief formation in a group? This chapter uses network models to investigate this question. These models show that the illusory truth effect can be very detrimental to a group's belief formation. The effect causes networks to prematurely settle on a belief, in part, through repeated exposure to data points that are not independently generated. Previous research has indicated that the probability of such failures is near zero when networks are large or scientists are forced to explore unpopular and risky science. However, simulation results show that the harmful consequences of the illusory truth effect are robust even in large networks with mandatory exploration. Finally, Chapter 3 shows how a rudimentary type of abstraction can obtain in Lewis-Skyrms signaling games. Here, abstraction is understood as occurring when different particulars take on the same functional role. Some abstraction may be guided by innate biases, and the chapter develops an analogy of reasoning about strategic thinking in chess to highlight some epistemic concerns that are raised by the presentation of abstraction. Concretely, the signaling game models of this chapter are developed in the context of two tasks that are much simpler than strategizing in chess. The first task is a transitive inference task that has been substantially studied in both humans and non-human animals. General features of the models developed for the transitive inference task are then carried over to a model for the second task of learning rudimentary grammatical structures. This second task is based on studies of human infants and non-human primates' ability to learn ``nonsense grammars". Closing discussion highlights some strengths of the abstraction exhibited in the Lewis-Skyrms signaling game models. (shrink)

Context has emerged as a central concept in a variety of contemporary approaches to reasoning. The conference at which the papers in this volume were presented, CONTEXT 2001, was the third international, interdisciplinary conference on the topic of context, and was held in Dundee, Scotland on July 27-30, 2001.

An innovative, multifaceted approach to scientific experiments as designed by and shaped through interaction with the modeling process The role of scientific modeling in mediation between theories and phenomena is a critical topic within the philosophy of science, touching on issues from climate modeling to synthetic models in biology, high energy particle physics, and cognitive sciences. Offering a radically new conception of the role of data in the scientific modeling process as well as a new awareness of the problematic (...) aspects of data, this cutting-edge volume offers a multifaceted view on experiments as designed and shaped in interaction with the modeling process. Contributors address such issues as the construction of models in conjunction with scientific experimentation; the status of measurement and the function of experiment in the identification of relevant parameters; how the phenomena under study are reconceived when accounted for by a model; and the interplay between experimenting, modeling, and simulation when results do not mesh. Highlighting the mediating role of models and the model-dependence of data measurement, this volume proposes a normative and conceptual innovation in scientific modeling--that the phenomena to be investigated and modeled must not be precisely identified at the start but specified during the course of the interactions arising between experimental and modeling activities. Contributors: Nancy D. Cartwright, U of California, San Diego; Anthony Chemero, U of Cincinnati; Ronald N. Giere, U of Minnesota; Jenann Ismael, U of Arizona; Tarja Knuuttila, U of South Carolina; Andrea Loettgers, U of Bern, Switzerland; Deborah Mayo, Virginia Tech; Joseph Rouse, Wesleyan U; Paul Teller, U of California, Davis; Michael Weisberg, U of Pennsylvania; Eric Winsberg, U of South Florida. (shrink)

Once a central topic of philosophy of science, scientific explanation attracted less attention in the last two decades. My aim in this paper is to argue for a newsort of approach towards scientific explanation. In a first step I propose a classification of different approaches through a set of dichotomic characteristics. Taken into account the tendencies in actual philosophy of science I see a local, dynamic and non-theory driven approach as a plausible one. Considering models as bearers of explanations (...) will provide a proper frame for such an approach. In the second part I make some suggestions for a working agenda that will further articulate a sketchy account of explanation through models proposed by Hartmann and Frigg. (shrink)

The main approaches to semiotic inquiry today contradict the idea of the individual as a separate and self-sufficient entity. The body of an organism in the micro- and macrocosm is not an isolated biological entity, it does not belong to the individual, it is not a separate and self-sufficient sphere in itself. The body is an organism that lives in relation to other bodies, it is intercorporeal and interdependent. This concept of the body finds confirmation in cultural practices and worldviews (...) based on intercorporeity, interdependency, exposition and opening, though nowadays such practices are almost extinct. An approach to semiotics that is global and at once capable of surpassing the illusory idea of definitive and ultimate boundaries to identity presupposes dialogue and otherness. Otherness obliges identity to question the tendency to totalizing closure and to reorganize itself always anew in a process related to ‘infinity’, as Emmanuel Levinas teaches us, or to ‘infinite semiosis’, to say it with Charles Sanders Peirce. Another topic of this paper is the interrelation in anthroposemiosis between man and machine and the implications involved for the future of humanity. Our overall purpose is to develop global semiotics in the direction of “semioethics”, as proposed by S.Petrilli and A. Ponzio and their ongoing research. (shrink)

A persuasion dialogue is a dialogue in which a conflict between agents with respect to their points of view arises at the beginning of the talk and the agents have the shared, global goal of resolving the conflict and at least one agent has the persuasive aim to convince the other party to accept an opposing point of view. I argue that the persuasive force of argument may have not only extreme values but also intermediate strength. That is, I wish (...) to introduce two additional types of the effects of persuasion in addition to successful and unsuccessful ones (cf. Van Eemeren and Houtlosser in Argumentation 14(3):293–305, 2000; Advances in pragma-dialectics. Sic Sat, Amsterdam, 2002; Walton in A pragmatic theory of fallacy. University of Alabama Press, Tuscaloosa, 1995; Walton and Krabbe in Commitment in dialogue: basic concepts of interpersonal reasoning. State University of New York Press, Albany, New York, 1995). I propose a model which provides for modified versions of the standpoint of an agent needed in order to bring about two possible outcomes of a persuasion dialogue. These two outcomes I label partially-successful and over-successful. I call the potential, not yet verbalised, standpoint of an agent here the original topic t. Based on some aspects of relevance theory (Sperber and Wilson in Relevance: communication and cognition. Blackwell, Oxford, 1986; Wilson and Sperber in The handbook of pragmatics. Blackwell Publishing, Malden, 2006), I explain that the modified version of the original topic t is an implicature created from the original topic t and from a specific mental topic which belongs to, what I call the beneficial cognitive model (hence BCM). I define BCMi,t as a set of topics which are within the area of agent i’s interest of persuasion with respect to t. (shrink)

Liquidity risk arises from the inability to unwind or hedge trading positions at the prevailing market prices. The risk of liquidity is a wide and complex topic as it depends on several factors and causes. While much has been written on the subject, there exists no clear-cut mathematical description of the phenomena and typical market risk modeling methods fail to identify the effect of illiquidity risk. In this paper, we do not propose a definitive one either, but we attempt (...) to derive novel mathematical algorithms for the dynamic modeling of trading volumes during the closeout period from the perspective of multiple-asset portfolio, as well as for financial entities with different subsidiary firms and multiple agents. The robust modeling techniques are based on the application of initial-value-problem differential equations technique for portfolio selection and risk management purposes. This paper provides some crucial parameters for the assessment of the trading volumes of multiple-asset portfolio during the closeout period, where the mathematical proofs for each theorem and corollary are provided. Based on the new developed econophysics theory, this paper presents for the first time a closed-form solution for key parameters for the estimation of trading volumes and liquidity risk, such as the unwinding constant, half-life, and mean lifetime and discusses how these novel parameters can be estimated and incorporated into the proposed techniques. The developed modeling algorithms are appealing in terms of theory and are promising for practical econophysics applications, particularly in developing dynamic and robust portfolio management algorithms in light of the 2007–2009 global financial crunch. In addition, they can be applied to artificial intelligence and machine learning for the policymaking process, reinforcement machine learning techniques for the Internet of Things data analytics, expert systems in finance, FinTech, and within big data ecosystems. (shrink)

This dissertation challenges two prevalent views on the topic of scientific explanation: that science explains by revealing causal mechanisms, and that science explains by unifying our knowledge of the world. ;My methodological strategy is to compare our best current philosophical accounts of scientific explanation with evidence from contemporary scientific research. In particular, I focus on evidence from dynamical explanations, that is, explanations which appeal to nonlinear dynamical modeling for their force. Nonlinear dynamical modeling is a type of mathematical modeling (...) which is used by scientists in many different disciplines, including population biology, ecology, physics, and psychology. ;Chapter 1 argues that dynamical explanations are a philosophically important class of explanations, based on their prevalence and their close relationship with the semantic view of theories. ;Chapters 2 and 3 conclude that extant causal and unification accounts encounter serious difficulties when applied to dynamical explanations. Chapter 2 argues that causal relevance is neither a necessary nor a sufficient condition for explanatory relevance. Chapter 3 clarifies and evaluates different claims about the unifying power of science. Here, I argue that dynamical explanations provide some support for the thesis that unification is constitutive of explanation. However, dynamical explanations also indicate that some of the intuitions embodied in extant unification accounts are untenable. ;Chapter 4 concludes by advancing diagnostic criticisms of existing causal and unification accounts. I explore the relationships between dynamical explanations and three conceptions of scientific explanation, as well as the relationships between different types of inference and scientific understanding. I suggest that philosophers look to cognitive science and the semantic view of theories for aid in constructing a viable positive account of explanation. ;The technical appendix introduces several key definitions and concepts of dynamical systems theory. (shrink)

This paper investigates how unsupervised machine learning methods might make hermeneutic interpretive text analysis more objective in the social sciences. Through a close examination of the uses of topic modeling—a popular unsupervised approach in the social sciences—it argues that the primary way in which unsupervised learning supports interpretation is by allowing interpreters to discover unanticipated information in larger and more diverse corpora and by improving the transparency of the interpretive process. This view highlights that unsupervised modeling does not eliminate (...) the researchers’ judgments from the process of producing evidence for social scientific theories. The paper shows this by distinguishing between two prevalent attitudes toward topic modeling, i.e., topic realism and topic instrumentalism. Under neither can modeling provide social scientific evidence without the researchers’ interpretive engagement with the original text materials. Thus the unsupervised text analysis cannot improve the objectivity of interpretation by alleviating the problem of underdetermination in interpretive debate. The paper argues that the sense in which unsupervised methods can improve objectivity is by providing researchers with the resources to justify to others that their interpretations are correct. This kind of objectivity seeks to reduce suspicions in collective debate that interpretations are the products of arbitrary processes influenced by the researchers’ idiosyncratic decisions or starting points. The paper discusses this view in relation to alternative approaches to formalizing interpretation and identifies several limitations on what unsupervised learning can be expected to achieve in terms of supporting interpretive work. (shrink)

Medieval literature is argumentative, since it argues for an idealized vision of reality acceptable to a proposed audience. Its narrative mode is description, performed according to the principles of the art of topical invention, derived from Cicero's De Inventione. The topoi or loci are features (circumstantiae) of a person or thing that are common to it as a class, such as tempus or locus for things. When filled out, according to the point of view desired by the author, public, context, (...) etc., they become the attributes (argumenta) of a particular human being or action.According to the author, all descriptions should be interpreted by reference to such a technique of topical invention, a method which will allow new explanations of the texts. The examples of the locus amoenus in various Latin and French works show how traditional and conventional models were adapted and specialized, by various devices, to fit new formal or conceptual intentions and new contexts. The examples and models proposed to the student learning composition by Masters such as Matthew of Vendôme, were given not to be copied, but to be imitated through topical invention, that is adaptation to a particular intention, through specializing devices. (shrink)

User modeling is a key topic in many applications, mainly social networks and information retrieval systems. To assess the effectiveness of a user modeling approach, its capability to classify personal characteristics (e.g., the gender, age, or consumption grade of the users) is evaluated. Due to the fact that some of the attributes to predict are multiclass (e.g., age usually encompasses multiple ranges), assessing fairness in user modeling becomes a challenge since most of the related metrics work with binary attributes. (...) As a workaround, the original multiclass attributes are usually binarized to meet standard fairness metrics definitions where both the target class and sensitive attribute (such as gender or age) are binary. However, this alters the original conditions, and fairness is evaluated on classes that differ from those used in the classification. In this article, we extend the definitions of four existing fairness metrics (related to disparate impact and disparate mistreatment) from binary to multiclass scenarios, considering different settings where either the target class or the sensitive attribute includes more than two groups. Our work endeavors to bridge the gap between formal definitions and real use cases in bias detection. The results of the experiments, conducted on four real-world datasets by leveraging two state-of-the-art graph neural network-based models for user modeling, show that the proposed generalization of fairness metrics can lead to a more effective and fine-grained comprehension of disadvantaged sensitive groups and, in some cases, to a better analysis of machine learning models originally deemed to be fair. The source code and the preprocessed datasets are available at the following link: https://github.com/erasmopurif/toward-responsible-fairness-analysis. (shrink)

DeStefano, Oey, Brockbank, and Vul explore interdisciplinary collaboration using data‐driven measures of research topics and co‐authorship, constructed from a rich dataset of over 11,000 Cogsci conference papers. Findings suggest the cognitive science research community has become increasingly integrated in the last 19 years.