EPJ Data Science最新文献

{"title":"The Schwurbelarchiv: a German-language dataset derived from multimodal Telegram content for the study of conspiracy theories.","authors":"Mathias Angermaier, Elisabeth Höldrich, João Pinheiro Neto, Jana Lasser","doi":"10.1140/epjds/s13688-026-00686-7","DOIUrl":"https://doi.org/10.1140/epjds/s13688-026-00686-7","url":null,"abstract":"<p><p>Sociality borne by language, as is the predominant digital trace on text-based social media platforms, harbours the raw material for exploring a multitude of social phenomena. Distinctively, the messaging service Telegram provides functionalities that allow for socially interactive as well as one-to-many communication. The Telegram dataset presented here contains over 5800 groups and channels discussing conspiracy-related topics with 63 million messages, originating from a data-hoarding initiative named the \"Schwurbelarchiv\" (from German schwurbeln: speaking nonsense). Uniquely, it includes the transcriptions of 2.5 million audio and video files. Our contribution is a processed, research-ready version of this data hoard: we parse, clean, and validate the raw archive, pseudonymise user data, and transcribe roughly 126,000 hours of audio and video content. In its original form the archive was stored in a format that is difficult to process and largely inaccessible for systematic research. This dataset publication details the structure, scope, and methodological specifics of the Schwurbelarchiv, emphasising its relevance for further research on the German-language conspiracy-related discourse. We validate its predominantly German origin by linguistic and temporal markers and situate it within the context of similar datasets. We describe process and extent of the transcription of multimedia files. Thanks to this effort the dataset uniquely supports analysis of text from originally multimodal sources like voice messages and videos to investigate online social dynamics and content dissemination. Researchers can employ this resource to explore societal dynamics for example related to conspiracy theories, misinformation, political extremism, and social network structures.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"73"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13481383/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148789739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Not your mean green: beyond averages in mapping socio-spatial inequities in urban greenery for smart cities.","authors":"Jenny Martinez, Javier Argota Sánchez-Vaquerizo, Sachit Mahajan","doi":"10.1140/epjds/s13688-026-00627-4","DOIUrl":"10.1140/epjds/s13688-026-00627-4","url":null,"abstract":"<p><p>As data-driven \"smart city\" agendas expand across Latin America, most urban performance metrics remain focused on infrastructure, connectivity, and aggregate efficiency, often neglecting who truly benefits. Urban greenery, a vital determinant of health and climate resilience, is one such blind spot. While some frameworks now consider \"green space,\" they do so at a coarse, citywide scale, overlooking how access is distributed across neighborhoods and social groups. This obscures critical equity gaps, particularly in cities marked by deep socio-spatial segregation. In this study, we develop a fully reproducible geospatial pipeline that integrates high-resolution canopy height models, public park data, gridded population estimates, and socioeconomic strata to assess how greenery is distributed, not just how much exists. Applied to Bogotá and Medellín, the method reveals stark disparities: population-weighted canopy coverage rises significantly between the lowest and highest strata, while access to public parks also shows measurable inequality, especially in high-density, underserved neighborhoods. These inequities persist despite progressive greening policies, revealing the limits of optimization when legacy segregation is ignored. Our open-source pipeline enables finer-grained, justice-oriented audits that go beyond averages to identify where greenery and its benefits are most lacking. By enabling fine-grained equity assessments, this approach underscores the importance of greenery distribution, not just quantity, as a critical indicator for inclusive and equitable smart cities.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"25"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12979267/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147467364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An agent-based model to investigate the effects of urban segregation around the clock on inequalities in health behaviour.","authors":"Clémentine Cottineau-Mugadza, Julien Perret, Romain Reuillon, Sébastien Rey-Coyrehourcq, Julie Vallée","doi":"10.1140/epjds/s13688-025-00603-4","DOIUrl":"10.1140/epjds/s13688-025-00603-4","url":null,"abstract":"<p><p>Social segregation in cities refers to the uneven spatial distribution of individuals from unequal social groups, such as affluent and economically vulnerable people. Social segregation may, in turn, produce social inequalities through contextual effects, since neighbourhood mixing or concentration plays a role in shaping individuals' opinions and behaviours in multiple life domains, including health. Because segregation and contextual effects occur at the places of residence as well as throughout the day, as people move between locations in a city, we aim to understand the social effect of urban segregation 'around the clock' on health behaviours (such as the choice of a healthy diet), using an empirical agent-based model initialised on the Paris region with a synthetic population. We built this synthetic population by pulling together data from two health & nutrition surveys conducted 6 years apart, data from the French census and data from an origin-destination survey. We then combined scenarios of residential patterns (random allocation vs. census-based allocation reflecting the empirical level of residential segregation) with scenarios of daily mobility (no daily moves, random moves or survey-based daily moves reflecting the empirical level of daytime segregation in Paris) to assess the effect of spatio-temporal segregation on the diffusion of health behaviours. While the same upward trend of healthy behaviours is obtained in all scenarios simulated, we find contrasted results with respect to social inequalities: 1/ when the agents' residence is allocated at random, social inequalities of health decrease in the long run; 2/ randomizing daily mobility can mitigate the increase in social inequalities in dietary behaviours induced by effective residential segregation, with this mitigation effect appearing as soon as a small proportion of daily moves are random; 3/ daytime segregation as it exists in Paris slightly reinforces the unequal distribution of health behaviours between the most and least educated groups compared with the sole effect of residential segregation.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1140/epjds/s13688-025-00603-4.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"5"},"PeriodicalIF":2.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12804204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generative AI voting: fair collective choice is resilient to LLM biases and inconsistencies.","authors":"Srijoni Majumdar, Edith Elkind, Evangelos Pournaras","doi":"10.1140/epjds/s13688-025-00612-3","DOIUrl":"10.1140/epjds/s13688-025-00612-3","url":null,"abstract":"<p><p>Recent breakthroughs in generative artificial intelligence (AI) and large language models (LLMs) unravel new capabilities for AI personal assistants to overcome cognitive bandwidth limitations of humans, providing decision support or even direct representation of abstained human voters at large scale. However, the quality of this representation and what underlying biases manifest when delegating collective decision making to LLMs is an alarming and timely challenge to tackle. By rigorously emulating more than >50K LLM voting personas in 363 real-world voting elections, we disentangle how AI-generated choices differ from human choices and how this affects collective decision outcomes. Complex preferential ballot formats show significant inconsistencies compared to simpler majoritarian elections, which demonstrate higher consistency. Strikingly, proportional ballot aggregation methods such as equal shares prove to be a win-win: fairer voting outcomes for humans and fairer AI representation, especially for voters likely to abstain. This novel underlying relationship proves paramount for building democratic resilience in scenarios of low voters turnout by voter fatigue: abstained voters are mitigated via AI representatives that recover representative and fair voting outcomes. These interdisciplinary insights provide decision support to policymakers and citizens for developing safeguards and policies for risks of using AI in democratic innovations.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1140/epjds/s13688-025-00612-3.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"24"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12963128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147376551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stigmergic influence of simple bots on human cooperation in digital environments.","authors":"Thomas Bassanetti, Stéphane Cezera, Maxime Delacroix, Ramón Escobedo, Adrien Blanchet, Clément Sire, Guy Theraulaz","doi":"10.1140/epjds/s13688-026-00653-2","DOIUrl":"10.1140/epjds/s13688-026-00653-2","url":null,"abstract":"<p><p>In the digital era, human cooperation is increasingly mediated by indirect social cues such as ratings, reviews, and other digital traces left in online environments. These traces often guide collective behavior via stigmergy, a coordination mechanism whereby individuals interact through modifications of a shared environment. In this study, we explore how simple model-driven bots can influence human cooperation or defection in a competitive rating game inspired by online marketplaces. Participants, unaware of the bots' presence, interacted with either four human partners or four bots exhibiting predefined behaviors-cooperative, neutral, deceptive, or optimized for group performance. We show that the presence and behavior of bots significantly affect human strategies and performance. Higher levels of cooperation among bots improve human outcomes but also increase the frequency of deceptive human strategies, suggesting exploitation of reliable social information. Conversely, in less cooperative environments, participants adopt more collaborative or neutral behaviors to preserve informational value. By classifying individuals into three behavioral profiles-collaborators, neutrals, and defectors-we develop a linear regression model using three cues: the average value of rated cells, the diversity of rated cells, and the player's rank. These cues allow accurate prediction of behavioral profile distributions across experimental conditions. An adaptive agent-based model further reproduces the empirical results. Our findings demonstrate that even simple bots can strongly influence collective dynamics in human groups. These insights have implications for the design of recommendation systems, the regulation of automated agents, and the understanding of cooperation and deception in digital societies.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1140/epjds/s13688-026-00653-2.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"49"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13183713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147981112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recovering scheduling preferences in dynamic departure time models.","authors":"Zhenyu Yang, Pietro Giardina, Nikolas Gerolimnis, André de Palma","doi":"10.1140/epjds/s13688-025-00608-z","DOIUrl":"10.1140/epjds/s13688-025-00608-z","url":null,"abstract":"<p><p>We aim to infer commuters' scheduling preferences from their observed arrival times, given an exogenous traffic congestion pattern. To do this, we employ a structural model that characterizes how users balance congestion costs against the penalties for arriving early or late relative to an ideal time. In this framework, each commuter selects an arrival time that minimizes her overall trip cost by considering the within-day congestion pattern along with her individual scheduling preference. By incorporating the distribution of these preferences and desired arrival times across the population, we can estimate the likelihood of observing arrivals at specific times. Using synthetic data, we then apply the maximum likelihood estimation (MLE) method to recover the parameters of the joint distribution of scheduling preferences and desired arrival times. Our numerical results demonstrate the effectiveness of the proposed method.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"28"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13061773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying feature importance for online content moderation.","authors":"Benedetta Tessa, Alejandro Moreo, Stefano Cresci, Tiziano Fagni, Fabrizio Sebastiani","doi":"10.1140/epjds/s13688-026-00659-w","DOIUrl":"10.1140/epjds/s13688-026-00659-w","url":null,"abstract":"<p><p>Accurately estimating how users respond to moderation interventions is key to designing effective and user-centred moderation strategies. This requires understanding which user characteristics are associated with different behavioural responses. We address this problem by analysing the informativeness of 753 socio-behavioural, linguistic, relational, and psychological features for predicting behavioural changes in 16.8K users affected by a large-scale moderation intervention on Reddit. We frame the task in terms of quantification, which is well-suited to estimating shifts in aggregate behaviour under distribution shift, and apply a greedy feature selection strategy to identify the most informative features and estimate their importance. Our results show that predictive performance varies substantially across tasks: changes in activity and toxicity can be estimated reliably, whereas changes in participation diversity are markedly harder to predict. We find that a small subset of features consistently improves performance across tasks, while many others are either task-specific or provide limited additional value. Importantly, models based on carefully selected features outperform both single feature groups and the full feature set, indicating that combining complementary signals is crucial for accurate estimation. Overall, our findings highlight the complexity and task-dependence of post-moderation user behaviour, suggesting that effective moderation strategies should be tailored not only to user characteristics but also to the specific behavioural outcomes of interest.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"15 1","pages":"70"},"PeriodicalIF":3.1,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148688633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Whose voice matters? Word embeddings reveal identity bias in news quotes.","authors":"Nnaemeka Ohamadike, Kevin Durrheim, Mpho Primus","doi":"10.1140/epjds/s13688-025-00541-1","DOIUrl":"https://doi.org/10.1140/epjds/s13688-025-00541-1","url":null,"abstract":"<p><p>This paper investigates identity bias (gender and race) in the South African news selection and representation of COVID-19 vaccination quotes. Social bias studies have qualitatively examined race and gender bias in South African news, given South Africa's apartheid history; yet, studies that examine and quantify these biases at the speaker level using news quotes from a representative South African news corpus remain limited. To address this gap, we examined race and gender bias in news selection and framing of quotes. We used word embedding trained on 22,627 vaccination quotes from 76 South African news sources between 2020 and 2023. These large-scale processing embeddings are unbiased by design but can learn and uncover biases hidden in language. Our findings reveal gender and race bias in the news selection and framing of quotes - journalists privilege White voices as more authoritative and connected to global and technical vaccination discourse but confine black voices to primarily localised contexts. They also quote male speakers more frequently in the news than females. In an era where human biases are becoming increasingly implicit, we argue that embeddings offer a robust tool to unearth, monitor, and evaluate these biases at the micro or speaker level in the news.</p><p><strong>Supplementary information: </strong>The online version contains supplementary material available at 10.1140/epjds/s13688-025-00541-1.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"14 1","pages":"30"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12006212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143974850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Safe spaces or toxic places? Content moderation and social dynamics of online eating disorder communities.","authors":"Kristina Lerman, Minh Duc Chu, Charles Bickham, Luca Luceri, Emilio Ferrara","doi":"10.1140/epjds/s13688-025-00575-5","DOIUrl":"10.1140/epjds/s13688-025-00575-5","url":null,"abstract":"<p><p>Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. While these platforms can provide valuable support networks, they may also amplify harmful content that glorifies disordered cognition and self-destructive behaviors. While social media platforms have implemented various content moderation strategies, from stringent to laissez-faire approaches, we lack a comprehensive understanding of how these different moderation practices interact with user engagement in online communities around these sensitive mental health topics. This study addresses this knowledge gap through a comparative analysis of eating disorder discussions across Twitter/X (2.6M tweets), Reddit (178K submissions), and TikTok (14K videos) spanning from 2019-2023. Our findings reveal that while users across all platforms engage similarly in expressing concerns and seeking support, platforms with weaker moderation (like Twitter/X) enable the formation of toxic echo chambers that amplify pro-anorexia rhetoric. These results demonstrate how moderation strategies significantly influence the development and impact of online communities, particularly in contexts involving mental health and self-harm.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"14 1","pages":"55"},"PeriodicalIF":2.5,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12296748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144728944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Milgram's experiment in the knowledge space: individual navigation strategies.","authors":"Manran Zhu, János Kertész","doi":"10.1140/epjds/s13688-025-00558-6","DOIUrl":"10.1140/epjds/s13688-025-00558-6","url":null,"abstract":"<p><p>Data deluge characteristic for our times has led to information overload, posing a significant challenge to effectively finding our way through the digital landscape. Addressing this issue requires an in-depth understanding of how we navigate through the abundance of information. Previous research has discovered multiple patterns in how individuals navigate in the geographic, social, and information spaces, yet individual differences in strategies for navigation in the knowledge space has remained largely unexplored. To bridge the gap, we conducted an online experiment where participants played a navigation game on Wikipedia and completed questionnaires about their personal information. Utilizing the hierarchical structure of the English Wikipedia and a graph embedding trained on it, we identified two navigation strategies and found that there are significant individual differences in the choices of them. Older, white and female participants tend to adopt a proximity-driven strategy, while younger participants prefer a hub-driven strategy. Our study connects social navigation to knowledge navigation: individuals' differing tendencies to use geographical and occupational information about the target person to navigate in the social space can be understood as different choices between the hub-driven and proximity-driven strategies in the knowledge space.</p>","PeriodicalId":11887,"journal":{"name":"EPJ Data Science","volume":"14 1","pages":"42"},"PeriodicalIF":3.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12141110/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144247072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}