arXiv subjects

Daniel Gatica-Perez

Publications and source records attributed to Daniel Gatica-Perez.

At least 19 recordsLinked to original sources

arXiv2026

The EMN Country Factsheets Structured Dataset

Each year, the European Migration Network (EMN) country factsheets deliver an overview of key migration and international protection developments within all EMN Member States and observer countries. The factsheets include both a textual component and a visual component. In this paper, we introduce a curated dataset of the textual component of these reports over 35 countries and 13 years (2012-2024.) The dataset was created to facilitate European-level research on migration policies and developments, and promote the use of reliable sources about migration in data science and media research, particularly at a time when the spread of online misinformation about migration constitutes a serious issue. The dataset transforms the original document texts into a tabular format, with columns corresponding to country, year, section, subsection, content, and harmonized title section. We illustrate the value of the dataset with concrete analyses and propose envisioned applications and uses of the dataset. The dataset is accessible through a DOI link.

cs.IR↗

arXiv2026

Framing Migration News with LLMs: Structured CoT as a Support for Human Interpretation

Frame analysis of migration news is a socially consequential task: media scholars and researchers who study how migration is narrated need tools that are not only accurate, but transparent, auditable, and accessible within the resource constraints typical of academic research groups. Existing LLM-based approaches rely on proprietary APIs and large models that raise concerns about data privacy, reproducibility and equitable access among media researchers. This work studies how a locally deployable open-source LLM can support interpretable frame analysis as an assistive tool. We introduce a Structured Chain-of-Thought (SCoT) prompting approach using Llama3-8B, enabling step-by-step justifications grounded in predefined framing categories. This structured design allows users to audit model outputs and examine alternative interpretations in a task that is inherently subjective. We evaluate our approach on a dataset of migration-related news and show that SCoT improves classification performance over zero-shot and few-shot baselines while remaining feasible on a single GPU. Then, we conduct a human-centered evaluation in which annotators assess the coherence and influence of "the model's reasoning". Results indicate that SCoT explanations are generally perceived as logical (mean score 4.1/5, though with notable variation across texts) and can prompt reflection on initial interpretations, even when disagreement persists. Our findings highlight both the potential and risks of LLM-assisted frame analysis. While structured reasoning can increase the traceability of model outputs and support critical interpretation, it can also influence human judgment in subtle ways. By enabling local deployment and emphasizing human-in-the-loop interaction, this work contributes to discussions on responsible and accessible computational tools for the study of socially impactful media narratives.

cs.CL↗

arXiv2026

Gradual Voluntary Participation: A Framework for Participatory AI Governance in Journalism

The integration of AI into journalism challenges participatory design (PD), particularly with respect to stakeholder influence, workplace perceptions, and organizational dynamics. Traditional PD assumes that users can shape technologies, yet AI systems resist influence due to opaque data, fixed architectures, and inaccessible objectives. Through interviews with 10 journalists, we identify the perception gap, showing that trust in AI depends on perceived agency within workplace participatory workflows. Informed by these findings, we introduce the Gradual Voluntary Participation (GVP) framework in journalism and its five core principles, reconceptualizing participation as a gradual and voluntary process that can be operationalized at the newsroom level, beyond fixed workshops or one-time preference-elicitation campaigns. Addressing epistemic burdens, participatory ceilings, and performative consultations, GVP treats gradualism and voluntariness as design dimensions that shape perception, legitimacy, and ownership. Moving beyond unidimensional ladder metaphors and adopting a bidimensional matrix structure, the framework maps stakeholders across depth and scope, offering a new model for local participatory AI governance that balances technological transformation with stakeholder empowerment in rapidly evolving hybrid workplaces.

cs.HC↗

arXiv2026

FAccT-Checked: A Narrative Review of Authority Reconfigurations and Retention in AI-Mediated Journalism

Building on recent interpretivist approaches, we conduct a critical narrative review across journalism studies, human-computer interaction, and FAccT scholarship, conceptualizing editorial authority as the conjunction of decision rights, epistemic warrant, and responsibility. We provide a comprehensive theoretical framework for addressing how concerns on fairness, accountability and transparency emerge, interact, and persist within AI mediated journalistic practice. We identify and describe two concurrent authority reconfigurations driven by AI adoption. First, an internal migration of authority, in which editorial judgment is progressively deferred to large language models (LLMs) embedded within newsroom workflows. This migration occurs not through explicit policy decisions, but through interactional, cognitive, and organizational mechanisms that legitimize AI generated outputs while obscuring responsibility and weakening individual and professional agency. Second, we analyze an external migration of authority, whereby decision making power shifts from news organizations toward platforms, vendors, and infrastructural providers that supply AI systems and distribution channels, exacerbating existing power asymmetries within the media ecosystem. Unaddressed, these reconfigurations risk rendering fairness hard to maintain, accountability difficult to assign and transparency performative. We examine participatory approaches to AI design and deployment in journalism as potential mechanisms for retaining or reclaiming editorial authority. We critically assess both their promise and their structural limitations, highlighting how participation can either meaningfully redistribute authority or function as a tokenistic practice that leaves underlying power relations intact.

cs.CY↗

arXiv2026

Migrant Voices, Local News: Insights on Bridging Community Needs with Media Content

Research shows news consumption differs across demographics, yet little is known about non-mainstream audiences, especially in relation to local media. Our study addresses this gap by examining how French-speaking migrants in a mid-size European city engage with local news, and whether their needs are reflected in coverage. Eight community members participated in focus groups, whose insights guided the selection of natural language processing methods (topic modeling, information retrieval, sentiment analysis, and readability) applied to over 2000 hyper-local news articles. Results showed that while articles frequently covered local events, gaps remained in topics important to participants. Sentiment analysis revealed a generally positive tone, and readability measures indicated an intermediate-advanced French level, raising questions about accessibility for integration. Our work contributes to bridging the gap between local news platforms' content and diverse readers' needs, and could inform local media organizations about opportunities to expand their current news story coverage to appeal to more diverse audiences.

cs.CL↗

arXiv2025

Co-Designing with Multiple Stakeholders and Datasets: A Community-Centered Process to Understand Youth Deviance in the Italian City of Turin

This paper presents the co-design and design evaluation of Sbocciamo Torino civic tool, which helps understand and act upon the issues of youth deviance in the Italian city of Turin through multi-stakeholder collaboration and collaborative data analysis. Rooted in research through design and participatory design methodologies, the civic tool integrates a data dashboard, stakeholder committee, and structured co-design sessions to facilitate collaborative analysis and intervention planning. The civic tool was developed in partnership with municipal authorities, law enforcement, NGOs, and social services, and reflects their institutional priorities while centering community knowledge. We describe the iterative co-design process, including stakeholder workshops for design, validation, training, and evaluation. The civic tool's impact on stakeholder trust, collaboration, and decision-making was assessed through surveys and open-ended questionnaires. Our findings show that stakeholders valued the inclusive design approach and data-driven collaboration while revealing barriers in communication, data literacy, and operational coordination. Furthermore, political and institutional support was identified as critical to the civic tool's success. This paper contributes to research on community technologies by demonstrating how civic tools can be collaboratively developed to navigate wicked social problems through participatory design.

cs.HC↗

arXiv2025

Assessment of Personality Dimensions Across Situations in Dyadic Role-Play Scenarios

Prior research indicates that users prefer assistive technologies whose personalities align with their own. This has sparked interest in automatic personality perception (APP), which aims to predict an individual's perceived personality traits. Previous studies in APP have treated personalities as static traits, independent of context. However, perceived personalities can vary by context and situation as shown in psychological research. In this study, we investigate the relationship between conversational speech and perceived personality for participants engaged in two work situations (a neutral interview and a stressful client interaction). Our key findings are: 1) perceived personalities differ significantly across interactions, 2) loudness, sound level, and spectral flux features are indicative of perceived extraversion, agreeableness, conscientiousness, and openness in neutral interactions, while neuroticism correlates with these features in stressful contexts, 3) handcrafted acoustic features and non-verbal features outperform speaker embeddings in inference of perceived personality, and 4) stressful interactions are more predictive of neuroticism, aligning with existing psychological research.

eess.AS↗

arXiv2025

Towards Open Diversity-Aware Social Interactions

Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much-needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet of Us", an online platform that uses diversity-aware Artificial Intelligence to mediate and empower human social interactions. We discuss the multiple facets of diversity in social settings, the multidisciplinary work that is required to reap the benefits of diversity, and the vision for a diversity-aware hybrid human-AI society.

cs.HC↗

arXiv2025

DiversityOne: A Multi-Country Smartphone Sensor Dataset for Everyday Life Behavior Modeling

Understanding everyday life behavior of young adults through personal devices, e.g., smartphones and smartwatches, is key for various applications, from enhancing the user experience in mobile apps to enabling appropriate interventions in digital health apps. Towards this goal, previous studies have relied on datasets combining passive sensor data with human-provided annotations or self-reports. However, many existing datasets are limited in scope, often focusing on specific countries primarily in the Global North, involving a small number of participants, or using a limited range of pre-processed sensors. These limitations restrict the ability to capture cross-country variations of human behavior, including the possibility of studying model generalization, and robustness. To address this gap, we introduce DiversityOne, a dataset which spans eight countries (China, Denmark, India, Italy, Mexico, Mongolia, Paraguay, and the United Kingdom) and includes data from 782 college students over four weeks. DiversityOne contains data from 26 smartphone sensor modalities and 350K+ self-reports. As of today, it is one of the largest and most diverse publicly available datasets, while featuring extensive demographic and psychosocial survey data. DiversityOne opens the possibility of studying important research problems in ubiquitous computing, particularly in domain adaptation and generalization across countries, all research areas so far largely underexplored because of the lack of adequate datasets.

cs.CY↗

arXiv2024

Generative AI Literacy: Twelve Defining Competencies

This paper introduces a competency-based model for generative artificial intelligence (AI) literacy covering essential skills and knowledge areas necessary to interact with generative AI. The competencies range from foundational AI literacy to prompt engineering and programming skills, including ethical and legal considerations. These twelve competencies offer a framework for individuals, policymakers, government officials, and educators looking to navigate and take advantage of the potential of generative AI responsibly. Embedding these competencies into educational programs and professional training initiatives can equip individuals to become responsible and informed users and creators of generative AI. The competencies follow a logical progression and serve as a roadmap for individuals seeking to get familiar with generative AI and for researchers and policymakers to develop assessments, educational programs, guidelines, and regulations.

cs.HC↗

arXiv2024

Human Interest or Conflict? Leveraging LLMs for Automated Framing Analysis in TV Shows

In the current media landscape, understanding the framing of information is crucial for critical consumption and informed decision making. Framing analysis is a valuable tool for identifying the underlying perspectives used to present information, and has been applied to a variety of media formats, including television programs. However, manual analysis of framing can be time-consuming and labor-intensive. This is where large language models (LLMs) can play a key role. In this paper, we propose a novel approach to use prompt-engineering to identify the framing of spoken content in television programs. Our findings indicate that prompt-engineering LLMs can be used as a support tool to identify frames, with agreement rates between human and machine reaching up to 43\%. As LLMs are still under development, we believe that our approach has the potential to be refined and further improved. The potential of this technology for interactive media applications is vast, including the development of support tools for journalists, educational resources for students of journalism learning about framing and related concepts, and interactive media experiences for audiences.

cs.HC↗

arXiv2024

M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial Training

Over the years, multimodal mobile sensing has been used extensively for inferences regarding health and well being, behavior, and context. However, a significant challenge hindering the widespread deployment of such models in real world scenarios is the issue of distribution shift. This is the phenomenon where the distribution of data in the training set differs from the distribution of data in the real world, the deployment environment. While extensively explored in computer vision and natural language processing, and while prior research in mobile sensing briefly addresses this concern, current work primarily focuses on models dealing with a single modality of data, such as audio or accelerometer readings, and consequently, there is little research on unsupervised domain adaptation when dealing with multimodal sensor data. To address this gap, we did extensive experiments with domain adversarial neural networks (DANN) showing that they can effectively handle distribution shifts in multimodal sensor data. Moreover, we proposed a novel improvement over DANN, called M3BAT, unsupervised domain adaptation for multimodal mobile sensing with multi-branch adversarial training, to account for the multimodality of sensor data during domain adaptation with multiple branches. Through extensive experiments conducted on two multimodal mobile sensing datasets, three inference tasks, and 14 source-target domain pairs, including both regression and classification, we demonstrate that our approach performs effectively on unseen domains. Compared to directly deploying a model trained in the source domain to the target domain, the model shows performance increases up to 12% AUC (area under the receiver operating characteristics curves) on classification tasks, and up to 0.13 MAE (mean absolute error) on regression tasks.

cs.LG↗

arXiv2023

Inferring Mood-While-Eating with Smartphone Sensing and Community-Based Model Personalization

The interplay between mood and eating episodes has been extensively researched, revealing a connection between the two. Previous studies have relied on questionnaires and mobile phone self-reports to investigate the relationship between mood and eating. However, current literature exhibits several limitations: a lack of investigation into the generalization of mood inference models trained with data from various everyday life situations to specific contexts like eating; an absence of studies using sensor data to explore the intersection of mood and eating; and inadequate examination of model personalization techniques within limited label settings, a common challenge in mood inference (i.e., far fewer negative mood reports compared to positive or neutral reports). In this study, we sought to examine everyday eating behavior and mood using two datasets of college students in Mexico (N_mex = 84, 1843 mood-while-eating reports) and eight countries (N_mul = 678, 24K mood-while-eating reports), which contain both passive smartphone sensing and self-report data. Our results indicate that generic mood inference models experience a decline in performance in specific contexts, such as during eating, highlighting the issue of sub-context shifts in mobile sensing. Moreover, we discovered that population-level (non-personalized) and hybrid (partially personalized) modeling techniques fall short in the commonly used three-class mood inference task (positive, neutral, negative). To overcome these limitations, we implemented a novel community-based personalization approach. Our findings demonstrate that mood-while-eating can be inferred with accuracies 63.8% (with F1-score of 62.5) for the MEX dataset and 88.3% (with F1-score of 85.7) with the MUL dataset using community-based models, surpassing those achieved with traditional methods.

cs.HC↗

arXiv2023

Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country Diversity

Understanding the social context of eating is crucial for promoting healthy eating behaviors. Multimodal smartphone sensor data could provide valuable insights into eating behavior, particularly in mobile food diaries and mobile health apps. However, research on the social context of eating with smartphone sensor data is limited, despite extensive studies in nutrition and behavioral science. Moreover, the impact of country differences on the social context of eating, as measured by multimodal phone sensor data and self-reports, remains under-explored. To address this research gap, our study focuses on a dataset of approximately 24K self-reports on eating events provided by 678 college students in eight countries to investigate the country diversity that emerges from smartphone sensors during eating events for different social contexts (alone or with others). Our analysis revealed that while some smartphone usage features during eating events were similar across countries, others exhibited unique trends in each country. We further studied how user and country-specific factors impact social context inference by developing machine learning models with population-level (non-personalized) and hybrid (partially personalized) experimental setups. We showed that models based on the hybrid approach achieve AUC scores up to 0.75 with XGBoost models. These findings emphasize the importance of considering country differences in building and deploying machine learning models to minimize biases and improve generalization across different populations.

cs.HC↗

arXiv2023

Learning About Social Context from Smartphone Data: Generalization Across Countries and Daily Life Moments

Understanding how social situations unfold in people's daily lives is relevant to designing mobile systems that can support users in their personal goals, well-being, and activities. As an alternative to questionnaires, some studies have used passively collected smartphone sensor data to infer social context (i.e., being alone or not) with machine learning models. However, the few existing studies have focused on specific daily life occasions and limited geographic cohorts in one or two countries. This limits the understanding of how inference models work in terms of generalization to everyday life occasions and multiple countries. In this paper, we used a novel, large-scale, and multimodal smartphone sensing dataset with over 216K self-reports collected from 581 young adults in five countries (Mongolia, Italy, Denmark, UK, Paraguay), first to understand whether social context inference is feasible with sensor data, and then, to know how behavioral and country-level diversity affects inferences. We found that several sensors are informative of social context, that partially personalized multi-country models (trained and tested with data from all countries) and country-specific models (trained and tested within countries) can achieve similar performance above 90% AUC, and that models do not generalize well to unseen countries regardless of geographic proximity. These findings confirm the importance of the diversity of mobile data, to better understand social context inference models in different countries.

cs.HC↗

arXiv2023

Examining European Press Coverage of the Covid-19 No-Vax Movement: An NLP Framework

This paper examines how the European press dealt with the no-vax reactions against the Covid-19 vaccine and the dis- and misinformation associated with this movement. Using a curated dataset of 1786 articles from 19 European newspapers on the anti-vaccine movement over a period of 22 months in 2020-2021, we used Natural Language Processing techniques including topic modeling, sentiment analysis, semantic relationship with word embeddings, political analysis, named entity recognition, and semantic networks, to understand the specific role of the European traditional press in the disinformation ecosystem. The results of this multi-angle analysis demonstrate that the European well-established press actively opposed a variety of hoaxes mainly spread on social media, and was critical of the anti-vax trend, regardless of the political orientation of the newspaper. This confirms the relevance of studying the role of high-quality press in the disinformation ecosystem.

cs.CL↗

arXiv2023

Framing the News:From Human Perception to Large Language Model Inferences

Identifying the frames of news is important to understand the articles' vision, intention, message to be conveyed, and which aspects of the news are emphasized. Framing is a widely studied concept in journalism, and has emerged as a new topic in computing, with the potential to automate processes and facilitate the work of journalism professionals. In this paper, we study this issue with articles related to the Covid-19 anti-vaccine movement. First, to understand the perspectives used to treat this theme, we developed a protocol for human labeling of frames for 1786 headlines of No-Vax movement articles of European newspapers from 5 countries. Headlines are key units in the written press, and worth of analysis as many people only read headlines (or use them to guide their decision for further reading.) Second, considering advances in Natural Language Processing (NLP) with large language models, we investigated two approaches for frame inference of news headlines: first with a GPT-3.5 fine-tuning approach, and second with GPT-3.5 prompt-engineering. Our work contributes to the study and analysis of the performance that these models have to facilitate journalistic tasks like classification of frames, while understanding whether the models are able to replicate human perception in the identification of these frames.

cs.CL↗

arXiv2023

ProGAP: Progressive Graph Neural Networks with Differential Privacy Guarantees

Graph Neural Networks (GNNs) have become a popular tool for learning on graphs, but their widespread use raises privacy concerns as graph data can contain personal or sensitive information. Differentially private GNN models have been recently proposed to preserve privacy while still allowing for effective learning over graph-structured datasets. However, achieving an ideal balance between accuracy and privacy in GNNs remains challenging due to the intrinsic structural connectivity of graphs. In this paper, we propose a new differentially private GNN called ProGAP that uses a progressive training scheme to improve such accuracy-privacy trade-offs. Combined with the aggregation perturbation technique to ensure differential privacy, ProGAP splits a GNN into a sequence of overlapping submodels that are trained progressively, expanding from the first submodel to the complete model. Specifically, each submodel is trained over the privately aggregated node embeddings learned and cached by the previous submodels, leading to an increased expressive power compared to previous approaches while limiting the incurred privacy costs. We formally prove that ProGAP ensures edge-level and node-level privacy guarantees for both training and inference stages, and evaluate its performance on benchmark graph datasets. Experimental results demonstrate that ProGAP can achieve up to 5-10% higher accuracy than existing state-of-the-art differentially private GNNs. Our code is available at https://github.com/sisaman/ProGAP.

cs.LG↗