Leveraging cognitive computing for advanced behavioral and emotional data insights

Behavior is ubiquitous, and behavior intelligence and insight play an important role in data understanding and business problem-solving. Behavior Informatics [1,2] emerges as an important tool for discovering behavior intelligence and behavior insight. As a computational concept, behavior captures the aspects of the demographics of behavioral subjects and objects; social relationships or norms governing the interactions between behaviors of an individual or a group; behavior sequences or networks and their dynamics; and the impact or effect generated by the behaviors undertaken by subjects on objects. Accordingly, a behavior model [2] captures the subject and the object of a behavior or behavior sequence, the activities conducted by its subject on objects, and the relationships between activities; behavior subject, object, activities and relationships are characterized by their respective attributes. As a result, a behavior is represented as a behavior attributes-based vector; and a subject's behaviors at a time period form a vector-based sequence, namely, represented as a behavior attribute vector-based matrix [3]. With such behavior modeling and from the informatics perspective, behavior informatics takes a top-down approach to systematically and deeply represent, model, reason about, and aggregate behaviors [4]; and a bottom-up approach to analyze and learn behavior occurrences, non-occurrences, dynamics, impact, and utility [2]. Accordingly, for a real-life problem, first, its data is converted to behavioral data according to the above behavior model, characterized by the relevant activities that form behavior sequences, and the properties of subjects, objects, activities, and relationships. Second, analytical tasks, such as behavior pattern analysis, abnormal behavior detection, coupled analysis of group behaviors, modeling of behavior impact and utility, discovery of high impact and high utility behaviors, analysis of non-occurring behaviors, and analysis of behavior evolution and dynamics, can be undertaken on such behavioral data. In this way, complex behaviors are quantifiable, computable [5], and manageable. This talk introduces some of real-life applications of behavior informatics in core business, capital markets and government services. It involves complex individual and group behaviors in relevant business, the interactions between clients and service providers, and relevant behavior sequences and attributes. The examples demonstrate the personalized and early prediction, the prevention and intervention of abnormal behaviors, and the active and tailored management of suspicious clients. Examples include the detection of pool manipulation through analyzing coupled sequences [3] of trading behaviors from multiple associated accounts in stock markets, the intervention of high-impact [6] behaviors in social security for preventing overpayments, the quantification and identification of high-utility [7] behaviors, the identification of and tailored intervention on self-finalizing versus non-self-finalizing taxpaying behaviors [8,9], and even the impact of non-occurring behaviors [10] in debt recovery and prevention. The real-life case studies show the value and potential of behavior informatics for handling complex and challenging risk management, fraud and non-compliance, and for active and tailored client management in business problems. The examples are associated with highly significant economic benefits and social impact as a result of applying the resultant behavior insight and behavior intelligence.

Investigating the ‘latent’ deficit hypothesis: Age at time of head injury, implicit and executive functions and behavioral insight

IntroductionBig data analytics and machine learning have transformed digital marketing by enabling data-driven insights for personalization. This study investigates the role of engagement metrics, sentiment analysis, and consumer segmentation in enhancing marketing effectiveness. Specifically, it examines how these technologies process consumer interaction data to uncover actionable insights, segment audiences, and drive purchase conversions.MethodThe study employed a mixed-methods approach, integrating big data analytics and machine learning techniques. Descriptive statistics highlighted engagement patterns, while k-means clustering segmented consumers based on behavioural and emotional data. Sentiment analysis, conducted using Natural Language Processing (NLP), captured consumer emotions as positive, neutral, or negative. Regression analysis evaluated the influence of social media activity, click-through rates, session duration, and sentiment scores on purchase conversion rates.ResultsDescriptive analysis revealed significant variability in consumer engagement and sentiment, with 37.5% of consumers expressing positive sentiment. Clustering identified three distinct consumer segments, reflecting differences in engagement and sentiment. Regression analysis showed that sentiment had a positive but statistically insignificant relationship with purchase conversions, while other metrics, such as click-through rates and session duration, exhibited minimal impact. The overall explanatory power of the regression model was low (R-squared = 0.001), indicating the need for additional factors to understand purchase behaviour.ConclusionThe findings emphasize the potential of big data analytics and machine learning in consumer segmentation and sentiment analysis. However, their direct impact on purchase conversion is limited without integrating broader variables. A holistic, adaptive framework combining behavioural, emotional, and contextual insights is essential for maximizing marketing personalization and driving outcomes in dynamic digital environments.

Abstract: Market Mechanics: Behavioral Insights and Economic Analysis explores the intersection of psychology and economics to illuminate how real-world decision-making diverges from traditional rational models. Bridging foundational theory with empirical research, the book provides a comprehensive analysis of individual and collective behavior in markets. From heuristics and biases to investor psychology and policy design, each chapter critically examines how cognitive limitations, social preferences, and emotional influences shape economic outcomes. Core themes include deviations from the Efficient Market Hypothesis, the impact of trust and norms in cross-cultural settings, and the growing role of AI and behavioral data in forecasting and regulation. Through rigorous yet accessible exposition, the book integrates insights from behavioral game theory, finance, macroeconomics, and digital platforms to present a holistic framework for understanding market dynamics. Rich with case studies, experimental findings, and policy implications, this volume is designed for researchers, graduate students, and policymakers seeking a deeper understanding of economic behavior beyond conventional assumptions. The concluding chapters offer forward-looking perspectives on ethical behavioral interventions and the integration of behavioral models into algorithmic systems, positioning the field as a cornerstone of evidence-based economic governance in the 21st century. Keywords behavioral economics, decision-making, market behavior, heuristics, biases, game theory, behavioral finance,

Behavioral instruments in renewable energy and the role of big data: A policy perspective

Recent outbreaks of infectious diseases-from seasonal influenza and COVID-19 to vector-borne threats such as dengue in Europe-have shown that individual behaviours play a critical role in shaping disease transmission and the effectiveness of public health interventions. Traditional surveillance systems are essential for tracking the progress of epidemics, but they often do not collect information on how the population is responding to the ongoing epidemic. This workshop explores how behavioural insights can be systematically integrated into surveillance and modelling to support preparedness and response strategies. The workshop has four main objectives: 1) to discuss participatory methods to monitor preventive behaviours during outbreaks; 2) to examine how data from social media can provide early signals of behavioural change; 3) to present innovative modelling approaches that embed behavioural dynamics; 4) to reflect on equity and inclusion in surveillance, especially among groups often left out of digital or formal health systems. Each of the 4 presentations addresses a pillar of the Next Generation EU funded project BEHAVE-MOD, a multidisciplinary initiative focused on respiratory and vector-borne diseases in Italy. The first explores the design and implementation of a participatory surveillance platform to track preventive behaviours in near real time. The second discusses how social media listening can capture public sentiment and behavioural trends, offering a timely supplement to survey-based methods. The third presentation introduces innovative approaches to integrating behavioural variables into epidemiological and predictive models, drawing on findings from the ERC project “Modelling the IMpact of huMan behavioUrs on iNfections sprEad”. The final presentation addresses equity in surveillance, focusing on how marginalised or digitally excluded populations can be meaningfully included in participatory and digital systems. The presentations collectively examine how behavioural monitoring can be integrated into infectious disease surveillance and modelling through an interdisciplinary approach. Drawing on diverse data sources and grounded in empirical and methodological innovation, the session explores how behavioural dynamics can enhance epidemic intelligence systems and public health response. The format includes 4 brief presentations (10 minutes each), followed by a 20-minute interactive discussion. Participants will be invited to respond to a live poll (e.g. using Mentimeter) to share experiences or identify key challenges in applying these approaches in their own settings. Selected responses will be used to guide a facilitated discussion on ethical, practical, and political challenges. This workshop offers concrete insights for researchers, policymakers, and practitioners committed to advancing the integration of behavioural data into infectious disease surveillance and modelling-toward more adaptive, timely, and people-centred systems. Key messages • Embedding behavioural data into surveillance and modelling strengthens innovation and resilience timely, adaptive public health responses. • Participatory and digital methods can capture behavioural dynamics but must ensure equity and inclusion.

Modern educational systems increasingly demand sophisticated analytical tools to assess and enhance student performance through personalized learning approaches. Yet, educational analytics models often lack comprehensive integration of behavioural, cognitive, and emotional insights, limiting their predictive accuracy and real-world applicability. While traditional machine learning approaches such as random forest and neural networks have been applied to educational data, they typically present trade-offs between interpretability and predictive capability, failing to capture student learning processes’ complex, multidimensional nature. This research introduces CognifyNet, a novel hybrid AI-driven educational analytics model that combines ensemble learning principles with deep neural network architectures to analyse student behaviours, cognitive patterns, and engagement levels through an innovative two-stage fusion mechanism. The model integrates random forest decision-making with multi-layer perceptron feature learning, incorporating sentiment analysis and advanced data processing pipelines to generate personalized learning trajectories while maintaining model transparency. Evaluated through rigorous 5-fold cross-validation on a comprehensive dataset of 1200 anonymized student records and validated across multiple educational platforms, including UCI Student Performance and Open University Learning Analytics datasets, CognifyNet demonstrates superior performance over conventional approaches, achieving 10.5% reduction in mean squared error and 83% reduction in mean absolute error compared to baseline random forest models, with statistical significance confirmed through paired t-tests (p < 0.01). The model’s adaptive architecture incorporates bias mitigation mechanisms that reduce demographic parity differences from 18% to 7% while maintaining predictive accuracy, ensuring equitable analytics across diverse student populations. These findings establish CognifyNet as a transformative tool for data-informed, student-centred educational strategies, offering educators actionable insights for early intervention and personalized support while bridging the critical gap between artificial intelligence capabilities and practical educational implementation.

Fostering Creativity Through Behavioral and Emotional Insights in Meetings

In response to the stock market's volatile nature, this research examines stock index forecasting evolution from traditional econometric models to advanced machine learning techniques. Market volatility, influenced by economic conditions, investor sentiment, and market interconnectedness, often renders linear models inadequate. While fundamental, conventional methods like multiple regression and ARMA face limitations with non-linear, noisy data, prompting development of machine learning approaches such as BP neural networks, SVM, and attention-enhanced CNN-LSTM models. These advanced techniques better capture data complexity, significantly improving prediction accuracy. The study explores both macro factors (economic linkages) and micro elements (herding behavior, loss aversion), alongside innovations like social media sentiment analysis that incorporate emotional and behavioral insights. Despite progress, challenges remain in balancing model complexity with accuracy and overcoming traditional statistical constraints in non-linear environments. This review emphasizes the necessity for integrated, fuzzy prediction models that consider multiple influences, with potential applications extending to other time series like commodity prices. These findings underscore the need for flexible, accurate forecasting methodologies to help authorities and investors navigate the unpredictable financial landscape.

Eye-tracking is a biometrics technique that has started to find applications in research related to our interaction with the built environment. Depending on the focus of a given study, the collection of valence and arousal measurements can also be conducted to acquire emotional, cognitive, and behavioral insights and correlate them with eye-tracking data. These measurements can give architects and designers a basis for data-driven decision-making throughout the design process. In instances involving existing structures, biometric data can also be utilized for post-occupancy analysis. This entry will discuss eye-tracking and eye-tracking simulation in the context of our current understanding of the importance of our interaction with the built environment for both physical and mental well-being.

Behavioural sciences have complemented medical and epidemiological sciences in the response to the SARS-CoV-2 pandemic. As vaccination uptake continues to increase across the EU/EEA – including booster vaccinations – behavioural science research remains important for both pandemic policy, planning of services and communication. From a behavioural perspective, the following three areas are key as the pandemic progresses: (i) attaining and maintaining high levels of vaccination including booster doses across all groups in society, including socially vulnerable populations, (ii) informing sustainable pandemic policies and ensuring adherence to basic prevention measures to protect the most vulnerable population, and (iii) facilitating population preparedness and willingness to support and adhere to the reimposition of restrictions locally or regionally whenever outbreaks may occur. Based on mixed-methods research, expert consultations, and engagement with communities, behavioural data and interventions can thus be important to prevent and effectively respond to local or regional outbreaks, and to minimise socioeconomic and health disparities. In this Perspective, we briefly outline these topics from a European viewpoint, while recognising the importance of considering the specific context in individual countries.

It's well known that financial markets are very unstable and hard to predict, which makes it hard for both traditional analytical models and investors to make decisions. Machine learning methods have recently shown promise in finding complicated patterns in large amounts of financial data. At the same time, behavioral finance insights have shown how investor psychology affects market movements. This study investigates the enhancement of stock market forecasting accuracy through the integration of advanced machine learning (ML) models with behavioral data, including market sentiment and investor biases, to gain deeper insights into investor decision-making. We examine the literature on conventional forecasting techniques in comparison to contemporary machine learning methodologies, and we construct a predictive framework that integrates historical stock data with behavioral indicators such as news and social media sentiment, fear-greed indices, and other relevant metrics. We use a range of models, such as regression-based models, ensemble classifiers, and deep learning (LSTM networks), and we add behavioral features to these models. We anticipate that machine learning models will surpass traditional methods in forecasting stock trends, and that the incorporation of behavioral variables will further improve predictive accuracy. Initial results suggest enhanced predictive accuracy (e.g., diminished error rates and increased directional precision) when sentiment and other investor-related variables are incorporated. This research enhances the fields of finance and AI by presenting a comprehensive forecasting methodology that integrates quantitative data with qualitative behavioral insights, thereby offering potential advantages to traders, investment firms, and policymakers in comprehending and predicting market dynamics.

This study delves into the influence of behavioural insights and marketing techniques on fashion consumer decision-making. It strategically identifies core behavioural elements, assesses their application in marketing, and examines consumer perceptions. Understanding and leveraging these insights enables businesses to effectively tailor their marketing efforts to resonate with their target audience. The reviewed literature provides a comprehensive integration of research from marketing, psychology, and behavioural science. The research methodology involves a qualitative survey encompassing 197 fashion consumers, as well as interviews with a psychologist and a digital merchandiser. Through this approach, the study sheds light on the impact of emotional factors, cognitive biases, social influence, and personality on decision-making. These findings illustrate how marketing strategies utilize these insights to influence consumer perceptions, attitudes, and behaviours. The study highlights the utilization of AI technology to target consumers based on behavioural data, with potential implications for transforming market research. Furthermore, it emphasizes the necessity of an interdisciplinary approach to comprehensively understand fashion consumer behaviour, given that self-reported data may not fully represent actual behaviours. The outcomes of this study not only validate existing research but also contribute to a more holistic understanding of fashion consumer behaviour. Future research initiatives could explore demographic variables, levels of fashion involvement, and cross-cultural comparisons. Moreover, combining self-reported data with objective measures promises to provide deeper insights into the subconscious factors that influence decision-making.

Klimagesundes Verhalten nimmt eine Schlüsselposition in der Gestaltung nachhaltiger Strategien zur Gesundheitsförderung ein. Dieser Beitrag zeigt, wie Verhaltensdaten genutzt werden können, um individuelle Routinen und strukturelle Rahmenbedingungen besser zu verstehen und wirksame Maßnahmen evidenzbasiert zu konzipieren. Auf Basis des interdisziplinären Ansatzes Behavioural and Cultural Insights (BCI) sowie des theoretischen COM-B-Frameworks (Capability, Opportunity, Motivation – Behaviour) wird erläutert, wie psychologische, soziale und kulturelle Faktoren das menschliche Verhalten beeinflussen und welche Rolle die systematische Verhaltensdatenerhebung für die Förderung klimagesunder Routinen spielt.Einerseits erlauben quantitative Ansätze wie Online-Umfragen, Experience Sampling (Echtzeiterhebung von Erleben und Verhalten im Alltag) und (Online‑)Experimente die Analyse großer Stichproben und das Aufdecken von Verhaltensmustern. Andererseits liefern qualitative Methoden (z. B. Interviews, Tagebuchstudien) vertiefte Einblicke in Entscheidungsprozesse und soziale Normen. Zu sogenannten Mixed-Methods-Ansätzen zusammengeführt können diese Verfahren Barrieren identifizieren – etwa mangelndes Wissen oder unzureichende Infrastruktur – und gezielt adressieren.Anhand der Projekte HEATCOM und PACE wird exemplarisch illustriert, wie Hitzeschutz- und klimagesundes Verhalten diesem integrativen Konzept folgend untersucht wird. Zugleich werden ethische Fragen sowie Aspekte des Datenschutzes diskutiert und aufgezeigt, wie Manipulationsvorwürfen durch Transparenz und wissenschaftliche Evidenz begegnet werden kann. Abschließend werden Herausforderungen in der politischen Umsetzung erörtert und Handlungsempfehlungen für Entscheidungsträger*innen formuliert, um Verhaltensdaten erfolgreich in Maßnahmen zur Klimaanpassung und Gesundheitsförderung zu integrieren.

Background Lassa fever is among the top emerging infectious diseases whose effective control depends on behavioural insights regarding its transmission, prevention and control. The study sought to explore behavioural insights - awareness, knowledge, lived experiences and behaviours – to inform Lassa fever transmission, prevention and control efforts in Eastern Sierra Leone. Methods This was a qualitative cross-sectional study conducted through focus group discussions (FGDs) and key informant interviews (KIIs), targeting diverse, information-rich community members. Thematic data analysis in Atlas.ti.22 involved analysis of interview transcripts to derive concepts, the classification and interpretation of these concepts to generate themes, capturing the essence of related behavioural insights. Results Total study participants were 114 - ten FGDs and 14 KIIs. Four themes emerged: knowledge gap on Lassa fever causation and transmission pathways; syndromic overlap between Lassa fever and other diseases; Lassa fever-related cultural and traditional health dilemmas and gendered health paradox. Conclusions Determinants of health behaviour extend beyond awareness, knowledge and understanding, to include crucial aspects like behavioural enablers and cultural factors. A context-aware approach is required wherein Lassa fever-related policies and programs prioritize the generation and utilization of context-specific behavioural data to drive effective health behaviour change.