Topic Models

This study investigates consumer perceptions of sustainable fashion by analyzing online discussions. It aims to bridge the gap between consumer understanding and industry practices by providing insights to guide companies and policymakers in developing more effective sustainability strategies. Adopting a data-driven, computational social science approach, this study analyses more than 116,000 posts and 283,000 comments from 20 Reddit communities focused on fashion and sustainability. Advanced natural language processing techniques, including topic modelling and sentiment analysis, are employed to uncover key themes and sentiment trends. The analysis identifies six key themes in consumer perceptions: product lifecycle management, material sourcing, size standardization, quality vs. price considerations, social responsibility, and consumer influence. Notably, discussions of circular design principles and innovative materials were largely absent from consumer conversations. While overall sentiment is predominantly positive, waste management and material sourcing evoke the least positive reactions, highlighting critical areas for industry improvement. By leveraging large-scale, real-world consumer discussions rather than traditional surveys, this study offers a novel perspective on sustainable fashion, capturing authentic consumer discourse and uncovering insights that may not emerge through conventional research methods.

This study investigates consumer perceptions of sustainable fashion
by analyzing online discussions. It aims to bridge the gap between
consumer understanding and industry practices by providing
insights to guide companies and policymakers in developing more
effective sustainability strategies. Adopting a data-driven, computational
social science approach, this study analyses more than 116,000
posts and 283,000 comments from 20 Reddit communities focused
on fashion and sustainability. Advanced natural language processing
techniques, including topic modelling and sentiment analysis, are
employed to uncover key themes and sentiment trends. The analysis
identifies six key themes in consumer perceptions: product lifecycle
management, material sourcing, size standardization, quality vs. price
considerations, social responsibility, and consumer influence.
Notably, discussions of circular design principles and innovative
materials were largely absent from consumer conversations. While
overall sentiment is predominantly positive, waste management and
material sourcing evoke the least positive reactions, highlighting
critical areas for industry improvement. By leveraging large-scale,
real-world consumer discussions rather than traditional surveys, this
study offers a novel perspective on sustainable fashion, capturing
authentic consumer discourse and uncovering insights that may not
emerge through conventional research methods.

Introduction: Effective flood early warning systems (FEWS) are crucial to mitigating flood impacts. Yet, their governance is often hindered by numerous systemic barriers. These systemic barriers reinforce social inequities, which in turn exacerbate flood risk management and hinder the achievement of disaster justice. Existing studies provide limited insights into the dominant barriers that most significantly hinder effective governance of FEWS. Moreover, research examining the underlying social injustices embedded in these barriers remains scarce, despite the intensi fying impacts of floods on existing social inequities, with the most severe con sequences affecting vulnerable populations. Methods: In this study, we employed a Latent Dirichlet Analysis (LDA) topic modeling to identify the dominant barriers to FEWS governance, followed by a qualitative content analysis to examine the underlying social injustice dimensions within these barriers. Results and discussion: The topic modeling identified five interpretable dominant barriers per FEWS component from the corpus. The analysis revealed that capability injustice is the most prevalent primary social injustice underpinning the identified key barriers, followed by distributional injus tice. Capability injustice, the unequal opportunties to utilize or access opportuni ties to enhance capabilities to operate or benefit from the system, remains an overarching dimension of injustice that is intensified by other forms of injustice. The study emphasizes the need for social justice to be considered in FEWS policy and governance. Going forward, addressing FEWS governance barriers requires attention to the underlying social injustices that perpetuate them, in order to avoid the risk of reinforcing existing inequalities within the system.

According to the textbook definition, a topic model aims to uncover the underlying topics of a corpus. Despite its widespread use across disciplines, the nature of these 'topics' has remained relatively underdefined. This research note attempts to fill this gap, drawing on empirical evidence to elucidate the practical application of the model. We argue that the frequency of terms within texts is influenced not only by their theme but also by factors such as genre and context, thus extending the notion of ' latent topics' beyond referential-semantic boundaries to include pragmatic considerations. Through case studies focusing on different genres, such as parliamentary speeches and online forums, we demonstrate the importance of pragmatics, which is often overlooked in well-known early applications that deal predominantly with formal written texts such as newspaper articles or academic papers.

Background: One of the most comprehensive approaches to depression is the biopsychosocial model. From this wider perspective, social sciences have criticized the reductionist biomedical discourse, which has been dominating expert discourses for a long time. As these discourses determine the horizon of attributions and interventions, their lay interpretation plays a central role in the coping with depression. Methods: In order to map these patterns, online depression forums are analyzed with natural language processing methods, where computational tools are complemented with a qualitative approach. Latent Dirichlet Allocation topic model of depression-related posts from the most popular English-speaking online health discussion forums (N = ~70 000) reveals the monolog (attributions and self-disclosures) and interactive (consultations and quasitherapeutic interactions) patterns. Results: Following the evaluation of various models 18 topics were differentiated: attributions referring to health, family, partnership and work issues; self-disclosures referring to contemplations, introducing the experience of suffering and well-being, along with diaries of everyday activities and hardships; consultations about psychotherapies, classifications, drugs and the experience; and quasi-therapeutic interactions relying on unconditional positive regards, recovery helpers experience or spirituality. These topics were evaluated from the perspective of the biopsychosocial model: the weight of each dimension was measured along with the discursive function. Conclusions: Biomedical discourse is underrepresented in lay discussions, while psychological discourse plays an overall dominant role. Even if actors are initially aware of the social mechanisms contributing to depression, they neglect these factors when it comes to considering the countermeasures.

This article investigates how the Russia-Ukraine war has reshaped the European Union's defence priorities and its pursuit of strategic autonomy. Using advanced text analytics and machine learning methods of over 26,000 European External Action Service (EEAS) documents, we examine shifts in topic prevalence across the Strategic Compass while considering broader geopolitical dynamics, including Brexit, China's positioning and US-NATO relations. Using EEAS documents spanning from one year before to two years after the Russian invasion, we employ a two-stage topic modelling approach. First, we classify EEAS documents under the Strategic Compass dimensions through majority voting, utilising text embedding and supervised learning models, followed by detailed mapping of documents to specific subtopics derived from the Strategic Compass framework by topic modelling. Our findings reveal a significant reorientation from a prewar emphasis on long-term capability development towards an increased focus on immediate crisis response and strengthened international partnerships postinvasion. While the EU demonstrated enhanced operational capacity and partnershipbuilding, persistent challenges remain in achieving comprehensive strategic autonomy and becoming an international actor. These results suggest that external crises can accelerate strategic autonomy in discourse. However, achieving genuine independence requires more effective crisis response, long-term capability development, and strong partnership management.

The rapid expansion of Artificial Intelligence (AI) in higher education has reshaped teaching, assessment, and academic writing practices, exposing the limitations of traditional plagiarism detection models. At the at the same time, the emergence of advanced algorithmic systems and generative AI has intensified ethical, pedagogical, and institutional debates concerning authorship, academic integrity, and fair assessment.  Objective: To critically analyze the impact of Artificial Intelligence on plagiarism detection in higher education, considering its technical, ethical, and pedagogical implications. A systematic literature review was conducted following the PRISMA protocol, using international databases including Scopus, Web of Science, ERIC, IEEE Xplore, and Google Scholar. A total of 963 records were identified, of which 18 studies met the eligibility criteria and were included in the final analysis. The findings reveal growing reliance on AI-based plagiarism detection systems, alongside persistent technical limitations such as overreliance on similarity scores, algorithmic bias, false positives, and reduced effectiveness in identifying AI-generated texts. The results also highlight significant effects on teaching and assessment practices, particularly when automated outputs are applied without adequate pedagogical mediation. Artificial Intelligence reshapes plagiarism detection practices but does not replace contextualized human judgment. Its responsible use requires clear institutional policies, strengthened academic and ethical literacy, and pedagogical approaches that prioritize formative processes over punitive measures, particularly within Global South contexts.

Prior to and during the pandemic, social media platforms such as Twitter and Facebook emerged as dynamic online spaces for diverse communities facilitating engagement and learning. The authors of this article have explored the use of social media with a focus on Twitter for engagement and student-centered design of online courses in higher education. As with all technology, social media is also riddled with complex issues and unfortunately, is increasingly considered unsafe. Students have often been hesitant in their use of social media, especially for coursework and unfortunately, this hesitation has only worsened. Considering this and recent developments, social media has become a questionable tool for use in education, yet remains integral to the lives of many, both personally and professionally. The emergence and popularity of generative artificial intelligence (GenAI) tools such as ChatGPT, Lensa AI, and Canva Magic Write present new challenges and opportunities and cannot be avoided by the educational communities. Is there hope for social media and AI tools during these uncertain times? Through the combination of a current literature review and qualitative collaborative autoethnographic research, the authors take a step back and engage in critical conversations about what we have learned from our uses of social media for engagement and learning in our online courses, with a focus on (1) the intentional uses of social media, (2) the challenges and concerning issues of social media tools, and (3) exploring the implications of artificial intelligence. Centering on the theme of "hope," the authors navigate these educational and technological landscapes and answer the question "where do we go from here?" The authors are faculty at a southwest border university teaching preservice and in-service teachers alongside those who want to learn more about education and design with learning technologies. Their voices represent faculty, teachers, and students who are engaging with and immediately impacted by the challenges and opportunities of rapidly advancing technologies.

International trade is one of the classic areas of study in economics. Its empirical analysis is a complex problem, given the amount of products, countries and years. Nowadays, given the availability of data, the tools used for the analysis can be complemented and enriched with new methodologies and techniques that go beyond the traditional approach. This new possibility opens a research gap, as new, data-driven, ways of understanding international trade, can help our understanding of the underlying phenomena. The present paper shows the application of the Latent Dirichlet allocation model, a well known technique in the area of Natural Language Processing, to search for latent dimensions in the product space of international trade, and their distribution across countries over time. We apply this technique to a dataset of countries’ exports of goods from 1962 to 2016. The results show that this technique can encode the main specialisation patterns of international trade. On the count...

Cuando comenzó la pandemia de COVID 19, las plataformas sociales tuvieron un rol central en la producción y acceso a la información. Este estudio identifica los tópicos de mayor interés y sus sentimientos asociados en Twitter en la conversación pública en lengua española en ese periodo. Asimismo, analiza el rol de Twitter como plataforma social involucrada en la conversación pública, como medio para la autocomunicación de masas y para amplificar la voz de un conjunto reducido de actores de alta visibilidad. Se recolectaron 231.375 tuits en España y América Latina durante dos meses. Se midieron indicadores de frecuencia y sentimientos, y se agruparon términos para identificar tópicos y determinar el interés de los usuarios sobre estos, mediante métodos digitales y de lenguaje computacional en R. La frecuencia de los principales términos es dinámica a lo largo del período estudiado, lo que sugiere diferentes percepciones de la pandemia. Los tópicos principales refieren a conversacione...

In this article we propose a novel methodology, which uses text similarity
techniques to infer precise citations from the judgments of the Court of Justice of the European Union (CJEU), including their content. We construct a complete network of citations to judgments on the level of singular text units or paragraphs. By contrast to previous literature, which takes into account only explicit citations of entire judgments, we also infer implicit citations, meaning the repetitions of legal arguments stemming from past judgments without explicit reference. On this basis we can differentiate between different categories and modes of citations. The latter is crucial for assessing the actual legal importance of judgments in the citation network. Our study is an important methodological step forward in integrating citation network analysis into legal studies, which significantly enhances our understanding of European Union law and the decision making of the CJEU.

While science is often portrayed as producing reliable knowledge, scientists tend to express caution about their claims, acknowledging nuances and doubt, all the more so in novel domains of research paved with unknowns. Uncertainty is an intrinsic aspect of scientific inquiry, particularly in recent fields such as astrobiology, which tackles numerous hard questions about the origin, evolution, and distribution of life on Earth and elsewhere. Mapping uncertainty in science matters for achieving a more accurate understanding of scientific knowledge. It also helps identify research domains at the frontiers of knowledge where unknowns are the most salient. In this article, we investigate the presence, distribution and context of uncertainty in the field of astrobiology. We analyze a comprehensive corpus of 3,698 research articles published in three major journals in the domain from 1968 to 2020. We use a linguistically motivated approach to identify expression of uncertainty in article full text. The corpus was further segmented into research topics using Latent Dirichlet Allocation (LDA) to investigate variations in uncertainty across subfields and over time. Our findings show that, while uncertainty has remained relatively stable over the 50 years covered by the corpus, constituting 20-25% of sentences on average, it varies significantly across research fields, highlighting areas where unknowns, doubts and speculations are more prevalent. The analysis also highlights relationships between expression of uncertainty and rhetorical structure. Indeed, higher uncertainty levels were observed in the beginning (introductions) and towards the end (conclusions) of research articles, while middle sections contained less uncertainty. Abstracts also tended to express a slightly higher level of uncertainty compared to main texts, especially with greater variability, suggesting their role in summarizing research and highlighting unknowns. To investigate the context of uncertainty, a lexical analysis was conducted to identify nouns most frequently associated with uncertainty within each topic. Terms such as "life," "planet," and "Mars" were found to be strongly associated with uncertainty. Conversely, terms related to experimentation and measurement, such as "sample" and "spectrum," were linked to an absence of uncertainty, pointing at a dichotomy between speculative and evidence-based lines of inquiry. The findings contribute to a better understanding of

Founded in 1974, Philosophiques has established itself as a leading journal of French-speaking philosophy in Quebec. Its fiftieth anniversary provides an opportunity to look back on half a century of articles, book reviews and thematic dossiers published in its pages. To this end, we present here the results of a computer-assisted thematic analysis of the complete corpus of the journal to date, comprising 1569 texts of philosophical content. This analysis carried out using two thematic modeling algorithms, LDA and Top2Vec, also provides an opportunity to compare the performance of these two approaches. The results reveal the diversity of topics addressed in the journal since the Quiet Revolution. The evolution of these topics over time highlights the various influences and currents that have marked contemporary Quebec philosophy, thus contributing to a better understanding of the history of philosophical thought in Quebec.

This study explores how computing science students (n = 335) use ChatGPT, their trust in its information, their navigation of plagiarism issues, and their confidence in addressing plagiarism and academic integrity. A mixed-methods approach was used, combining quantitative survey data with a qualitative thematic analysis of student comments to provide a comprehensive understanding of these issues. The findings reveal that ChatGPT has become integral to students' academic routines, with 87.8% using it weekly with variable frequency. Most students (70.3%) believe the university should allow ChatGPT use, and 66.6% think it is fair to use it for academic purposes despite 57.4% distrusting its information. Additionally, 53.8% worry about accidentally plagiarising when using ChatGPT. Overall, students have moderate confidence in addressing these academic integrity issues, with no differences between undergraduate and postgraduate students. Male students reported higher confidence in handling plagiarism and academic integrity issues than female students, suggesting underlying differences in how students perceive and interact with generative AI technologies. A thematic analysis of 74 student comments on their ChatGPT experience revealed four themes: (a) Usage and Role of ChatGPT, (b) Ethical and Responsible Use, (c) Limitations and Accuracy, and (d) Impact on Education and Need for Clear Guidelines. This study contributes to the ongoing debate on accepting and using ChatGPT, highlighting the need for institutions to provide clear guidelines and ethical considerations to ensure responsible use within educational contexts.

This systematic review aims to understand what research has been done on the use of AI as a classroom tutor and how that body of work should shape future research. A systematic review was conducted using key term searches in four major, peer-reviewed journal databases for relevant research and categorized them by research type, AI type, assistance type, and education level and analyzed them for study measures and key findings. After applying inclusion and exclusion criteria, a total of 34 research articles were analyzed for key findings. The review found that most studies have focused on chatbots supporting undergraduate level education. Key findings show that AI tutors may be most useful for students beginning at a lower level of expertise than their peers, and the use of techniques such as RAG may significantly enhance AI's usefulness as a tutor with niche and higherlevel subjects. This systematic review uniquely considers the last decade of research on AI use as a classroom tutor and provides insights into where future research on the subject may be most useful and impactful to the field of education.

Probabilistic topic models are a popular tool for the unsupervised analysis of text, providing both a predictive model of future text and a latent topic representation of the corpus. Recent studies have found that while there are suggestive connections between topic models and the way humans interpret data, these two often disagree. In this paper, we explore this disagreement from the perspective of the learning process rather than the output. We present a novel task, tag-and-cluster, which asks subjects to simultaneously annotate documents and cluster those annotations. We use these annotations as a novel approach for constructing a topic model, grounded in human interpretations of documents. We demonstrate that these topic models have features which distinguish them from traditional topic models.

Student gender differences in technology acceptance and use have persisted for years, giving rise to equity concerns in higher education (HE). To explore if such differences extend to generative artificial intelligence (genAI) chatbot use, we surveyed a large Norwegian HE student sample (n = 2692) using a fully mixed concurrent equal status design. Our findings show that men exhibit more frequent engagement with genAI chatbots across a broader spectrum of applications. Further, men demonstrate a heightened interest in genAI chatbots as tools and in their relevance to future career prospects. Women primarily utilize genAI chatbots in text-related tasks and express greater concerns regarding critical and independent thinking. Women also exhibit a stronger need to learn how to determine when it is wise to use and how to trust genAI chatbots. Consequences are discussed for the individual, society, and HE institutions in terms of social reproduction, diversity competence, and equitable teaching practices.

This paper presents an overview of the work carried out at the HMI group of the University of Twente in the domain of multi-party interaction. The process from automatic observations of behavioral aspects through interpretations resulting in recognized behavior is discussed for various modalities and levels. We show how a virtual meeting room can be used for visualization and evaluation of behavioral models as well as a research tool for studying the effect of modified stimuli on the perception of behavior.

The increasing use of artificial intelligence (AI) in education has raised questions about the implications of ChatGPT for teaching and learning. A systematic literature review was conducted to answer these questions, analyzing 112 scholarly articles to identify the potential benefits and challenges related to ChatGPT use in educational settings. The selection process was thorough to ensure a comprehensive analysis of the current academic discourse on AI tools in education. Our research sheds light on the significant impact of ChatGPT on improving student engagement and accessibility and the critical issues that need to be considered, including concerns about the quality and bias of generated responses, the risk of plagiarism, and the authenticity of educational content. The study aims to summarize the utilizations of ChatGPT in teaching and learning by addressing the identified benefits and challenges through targeted strategies. The authors outlined some recommendations that will ensure that the integration of ChatGPT into educational frameworks enhances learning outcomes while safeguarding academic standards.

Many educators and professionals in different industries may need to become more familiar with the basic concepts of artificial intelligence (AI) and generative artificial intelligence (Gen-AI). Therefore, this paper aims to introduce some of the basic concepts of AI and Gen-AI. The approach of this explanatory paper is first to introduce some of the underlying concepts, such as artificial intelligence, machine learning, deep learning, artificial neural networks, and large language models (LLMs), that would allow the reader to better understand generative AI. The paper also discusses some of the applications and implications of generative AI on businesses and education, followed by the current challenges associated with generative AI.

The recent emergence of generative AI (GenAI) tools such as ChatGPT, Midjourney, and Gemini have introduced revolutionary capabilities that are predicted to transform numerous facets of society fundamentally. In higher education (HE), the advent of GenAI presents a pivotal moment that may profoundly alter learning and teaching practices in aspects such as inaccuracy, bias, overreliance on technology and algorithms, and limited access to educational AI resources that require in-depth investigation. To evaluate the implications of adopting GenAI in HE, a team of academics and field experts have co-authored this paper, which analyzes the potential for the responsible integration of GenAI into HE and provides recommendations about this integration. This paper recommends strategies for integrating GenAI into HE to create the following positive outcomes: raise awareness about disruptive change, train faculty, change teaching and assessment practices, partner with students, impart AI learning literacies, bridge the digital divide, and conduct applied research. Finally, we propose four preliminary scale levels of a GenAI adoption for faculty. At each level, we suggest courses of action to facilitate progress to the next stage in the adoption of GenAI. This study offers a valuable set of recommendations to decision-makers and faculty, enabling them to prepare for the responsible and judicious integration of GenAI into HE.

Activity mining in traffic scenes aims to automatically explain the complex interactions among moving objects recorded with a surveillance camera. Traditional machine learning algorithms generate a model and validate it with manually labeled data, which is a time-consuming and expensive task. The common issue is that these models often get outdated when external variables take place during posterior recording such as dynamic background, illumination, and different weather conditions. Those changes practically impose a new domain that often makes the original model inaccurate for clustering and classification tasks. If we directly apply a statistical model trained in one domain to other over the same stream, the performance of the algorithm will notably decrease due to distinct activity representations and different marginal and conditional distributions. We approach this problem in two stages: 1) we present mature results on a hierarchical Bayesian model designed to represent every ...

Recent approaches in traffic and crowd scene analysis make extensive use of non-parametric hierarchical Bayesian models for intelligent clustering of features into activities. Although this has yielded impressive results, it requires the use of time consuming Bayesian inference during both training and classification. Therefore, we seek to limit Bayesian inference to the training stage, where unsupervised clustering is performed to extract semantically meaningful activities from the scene. In the testing stage, we use discriminative classifiers, taking advantage of their relative simplicity and fast inference. Experiments on publicly available data-sets show that our approach is comparable in classification accuracy to state-of-the-art methods and provides a significant speed-up in the testing phase.

In various real-world applications of distributed and multi-view vision systems, the ability to learn unseen actions in an online fashion is paramount, as most of the actions are not known or sufficient training data is not available at design time. We propose a novel approach which combines the unsupervised learning capabilities of Hierarchical Dirichlet Processes (HDP) with Temporal Self-Similarity Maps (SSM) representations, which have been shown to be suitable for aggregating multi-view information without further model knowledge. Furthermore, the HDP model, being almost completely data-driven, provides us with a system that works almost “out-of-the-box”. Various experiments performed on the extensive JAR-AIBO dataset show promising results, with clustering accuracies up to 60% for a 56-class problem.

The richness of social media data has opened a new avenue for social science research to gain insights into human behaviors and experiences. In particular, emerging data-driven approaches relying on topic models provide entirely new perspectives on interpreting social phenomena. However, the short, text-heavy, and unstructured nature of social media content often leads to methodological challenges in both data collection and analysis. In order to bridge the developing field of computational science and empirical social research, this study aims to evaluate the performance of four topic modeling techniques; namely latent Dirichlet allocation (LDA), non-negative matrix factorization (NMF), Top2Vec, and BERTopic. In view of the interplay between human relations and digital media, this research takes Twitter posts as the reference point and assesses the performance of different algorithms concerning their strengths and weaknesses in a social science context. Based on certain details dur...

The needs of travellers vary across cultures. When it comes to culinary aspects, there is a strong connection between gastronomy and culture. To optimise service offerings, investigation of the essential aspects of dining experiences in relation to cultural backgrounds is of great importance. In the age of digitalisation, tourists share their dining experiences throughout their multiphasic travel journey via online platforms. By considering nine distinct cultural backgrounds, this research aims to investigate tourist experiences based on TripAdvisor restaurant reviews through topic modelling, using the city of Salzburg as its study context. Depending on one’s cultural circumstances, the findings demonstrate that the most important aspects include staff, food-menu items, value for money, restaurant physical appearance, food authenticity, overall service, menu offers, food quality, atmosphere, and recommendations. This study advances the state-of-the-art knowledge of societal culture ...

As a result of travel activities, overtourism has become a global issue. Even after the COVID-19 pandemic, the topic of overtourism would benefit localized overcrowding as a new occurrence in the tourism industry. Since there is no specific measurement to evaluate tourist experiences at crowded attractions, this study aims to explore the perception and feelings of tourists when they visit popular and crowded attractions through topic modeling and sentiment analysis based on TripAdvisor online reviews as of the end of 2019. By investigating the top 10 attractions in Paris, the results present 24 topics frequently discussed by tourists. Examples of some topics related to overtourism are safety, service, queuing, and social interaction. Specifically, tourists felt the most negative towards safety and security among all the identified topics. By bridging overtourism, text analytics, and user-generated-content, this study contributes to the field of tourist experiences and crowd management.

The rapid advancement of generative artificial intelligence (GAI) has opened new pathways for enhancing educational technologies, particularly through the integration of learning analytics (LA). This paper explores the dynamic relationship between LA and GAI, assessing how their convergence could potentially transform educational outcomes. LA, which involves the measurement, collection, analysis, and reporting of data about learners and their contexts, serves as a crucial tool for understanding and improving learning processes. Combined with GAI, capable of creating content, simulating educational interactions, and generating personalized learning resources, the potential for tailored educational experiences is significantly expanded. This synergy not only enhances adaptive learning environments but also supports realtime decision-making, feedback, and intervention strategies. By examining various case studies and experimental implementations, the study highlights the efficacy of GAI in interpreting complex datasets provided by LA, thereby promoting an adaptive and responsive learning ecosystem. Additionally, ethical considerations, challenges, and future directions of this interdisciplinary approach are discussed to guide current and future applications. This paper proposes an exploratory study on the synergy between LA and GAI, using the AI4Design system, a web-based platform based on GAI for Design learning, as the framework where GAI complements LA. A case study is proposed using ChatGPT as the GAI engine. The first results show the power of the synergy GAI-LA.

This dialogue sketches a research program that links brains, texts, and literary history using the conceptual toolkit of complex dynamics. We began from the asymmetry between whole-brain cognition and the linguistic traces available to large language models (LLMs): human reading and poetic composition emerge from distributed neural activity extending well beyond lexical mechanisms. From a dynamical perspective, meaning is not a static property “in” words but a temporally unfolding trajectory through a high-dimensional cognitive state space; texts are compressed traces of that motion.

This motivates virtual reading: since every text necessarily engages lexical processing, and word embeddings provide a tractable geometry of lexical relations, a text can be modeled as a path through embedding space. The global structure of that path (returns, pivots, closures, drift) can reflect extra-lexical constraints—affect, attention, memory—even though embeddings are purely lexical. In principle, neuroimaging readers while they read could align virtual trajectories with whole-brain activity patterns.

We then connect micro-trajectories to macro-history via cultural evolution: large-scale DH evidence of directional change becomes movement through a cultural design space. Finally, virtual reading is framed as an operational extension of a naturalist theory of form, where shareability holds that “computational form is the same for all competent readers.”

This article describes and evaluates the application of the supervised sentiment analysis in political communication through a real-time classifier of political opinions in Spanish tweets using machine learning techniques, both on a local computer and using distributed computing for big data problems. We describe the associated emerging methods and techniques and analyze the opportunities that these innovations represent for political communication

Doctor en Comunicación, Cultura y Educación y licenciado en Economía por la Usal. Ha desarrollado su trabajo en investigación de audiencias, metodologías de investigación, estructura del sistema audiovisual y las industrias culturales, televisión, educación y comunicación, publicando artículos y libros a nivel nacional e internacional.