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检索条件"丛书名=Lecture notes in computer science,"

共 300228 条记录，以下是51-60 订阅

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ClimateMiSt: Climate Change Misinformation and Stance Detection Dataset 16th

ClimateMiSt: Climate Change Misinformation and Stance Detect...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Choi, YeonJung Shang, Lanyu Wang, Dong Univ Illinois Sch Informat Sci Champaign IL 61820 USA

ISBN: (纸本)9783031785375;9783031785382

Climate change has been a worldwide concern for more than 50 years, and climate change misinformation has also become a critical issue as it questions the causes and effects of climate change, thereby disrupting climate action. Climate misinformation has been a major obstacle to mitigating climate change and its effects, aggravating the issue and polarizing the public. In this paper, we introduce ClimateMiSt, a new climate change misinformation and stance detection dataset consisting of social media data with manually verified labels. The data is collected from Twitter/X and our dataset contains 146,670 tweets. We implement state-of-the-art baseline models for both misinformation and stance detection on our dataset and discover that GPT-4 outperforms them in both tasks. To the best of our knowledge, ClimateMiSt is the first dataset focused on climate change that includes both veracity and stance annotations collected from a social media platform. Our novel dataset can be used for climate change misinformation and stance detection, and it can further contribute to research in this field.

关键词： Climage Change Climate Change Dataset Misinformation Detection Stance Detection Online Social Media

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A Transaction-Level Model for Blockchain Privacy 28th

A Transaction-Level Model for Blockchain Privacy

引用

28th International Conference on Financial Cryptography and Data Security

作者： Wicht, Francois-Xavier Wang, Zhipeng Le, Duc V. Cachin, Christian Univ Bern Inst Comp Sci Bern Switzerland Imperial Coll London London England Visa Res Foster City CA USA

ISBN: (纸本)9783031786785;9783031786792

Considerable work explores blockchain privacy notions. Yet, it usually employs entirely different models and notations, complicating potential comparisons. In this work, we use the Transaction Directed Acyclic Graph (TDAG) and extend it to capture blockchain privacy notions (PDAG). We give consistent definitions for untraceability and unlinkability. Moreover, we specify conditions on a blockchain system to achieve each aforementioned privacy notion. Thus, we can compare the two most prominent privacy-preserving blockchains - Monero and Zcash, in terms of privacy guarantees. Finally, we unify linking heuristics from the literature with our graph notation and review a good portion of research on blockchain privacy.

关键词： Blockchain privacy Untraceability Unlinkability

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Would Friedman Burn Your Tokens? 28th

Would Friedman Burn Your Tokens?

引用

28th International Conference on Financial Cryptography and Data Security

作者： Kiayias, Aggelos Lazos, Philip Schlegel, Jan Christoph Univ Edinburgh IOG Edinburgh Midlothian Scotland IOG London England City Univ London London England

ISBN: (纸本)9783031786754;9783031786761

Cryptocurrencies come with a variety of tokenomic policies as well as aspirations of desirable monetary characteristics that have been described by proponents as "sound money" or even "ultra sound money." These propositions are typically devoid of economic analysis so it is a pertinent question how such aspirations fit in the wider context of monetary economic theory. In this work, we develop a framework that determines the optimal token supply policy of a cryptocurrency, as well as investigate how such policy may be algorithmically implemented. Our findings suggest that the optimal policy complies with the Friedman rule and it is dependent on the risk free rate, as well as the growth of the cryptocurrency platform. Furthermore, we demonstrate a wide set of conditions under which such policy can be implemented via contractions and expansions of token supply that can be realized algorithmically with block rewards, taxation of consumption and burning the proceeds, and blockchain oracles.

关键词： Tokenomics Friedman rule Token Burning Transaction Fees

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Opinion and Self-confidence in Influence Networks: A Coupled Dynamics Model 16th

Opinion and Self-confidence in Influence Networks: A Coupled...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Kravitzch, Emmanuel Varma, Vineeth S. Berthet, Antoine O. Hayel, Yezekael Avignon Univ Lab Informat Avignon Avignon France Univ Lorraine CRAN CNRS Dept Controle Identificat Diagnost CID Nancy France Univ Paris Saclay CNRS CentraleSupelec Lab Signaux & Syst L2S Orsay France

ISBN: (纸本)9783031785474;9783031785481

In this paper, we develop a novel model of opinion dynamics that takes into account the variability of self-confidence. This agent-based model aims at combining both opinion and self-confidence, two notions often assimilated in many modeling works, although well distinct in practice. We analyze our model under two types of opinion space, namely a standard interval and a circular structure under which the system presents some intriguing specificity. We give some insights concerning the global equilibria of the co-evolving dynamics, and also highlight some salient features of the global system's trajectory. Finally, we conduct several numerical simulations to support the analytical results.

关键词： opinion dynamics bounded-confidence influence networks

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Categorising Corruption in the Vaccine Discourse: A General Taxonomy, Data Set, and Evaluation of LLMs for Classifying Corruption Dialogue in Social Media 16th

Categorising Corruption in the Vaccine Discourse: A General ...

引用

16th International Conference on Social Networks Analysis and Mining

作者： dos Santos, Vitor Gaboardi Santos, Guto Leoni Egli, Antonia Kahvazadeh, Estatira Doolin, Bill Endo, Patricia Takako Lynn, Theo Dublin City Univ Dublin Ireland Georgia Inst Technol Atlanta GA USA Trinity Coll Dublin Dublin Ireland Univ Pernambuco Caruaru Brazil

ISBN: (纸本)9783031785405;9783031785412

Real or perceived corruption can have a damaging effect on health care services and outcomes. In particular, research suggests perceived corruption had a significant impact on COVID-19 vaccination. Given the role of social media in health communications, identifying and understanding perceived corruption related to vaccines and vaccination is critical to build societal cohesion and public trust in health institutions and strategies, manage and combat misinformation and disinformation, and design more effective policies, interventions, and communications strategies. There is a dearth of research on binary and multi-class classification of corruption dialogues in health or otherwise. We address this gap by introducing a general hierarchical corruption dialogue taxonomy (HCDT) and formulating binary and multi-class classification tasks based on the HCDT. We also create a vaccine-specific labelled dataset for each task, and fine-tune three large language models (BERT, RoBERTa, and BERTweet) based on these datasets. We evaluate the performance of these models in the binary and multi-class classification tasks. While all models performed similarly for the binary task, RoBERTa performed best for multi-class classification of corruption dialogue.

关键词： Corruption Large Language Models BERT Twitter Multi-class Classification Vaccine COVID-19

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Exploring the Capability of ChatGPT to Reproduce Human Labels for Social Computing Tasks 16th

Exploring the Capability of ChatGPT to Reproduce Human Label...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Zhu, Yiming Zhang, Peixian Haq, Ehsan-Ul Hui, Pan Tyson, Gareth Hong Kong Univ Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9783031785474;9783031785481

Harnessing the potential of large language models (LLMs) like ChatGPT can help address social challenges through inclusive, ethical, and sustainable means. In this paper, we investigate the extent to which ChatGPT can annotate data for social computing tasks, aiming to reduce the complexity and cost of undertaking web research. To evaluate ChatGPT's potential, we re-annotate seven datasets using ChatGPT, covering topics related to pressing social issues like COVID-19 misinformation, social bot deception, cyberbully, clickbait news, and the Russo-Ukrainian War. Our findings demonstrate that ChatGPT exhibits promise in handling these data annotation tasks, albeit with some challenges. Across the seven datasets, ChatGPT achieves an average annotation F1-score of 72.00%. Its performance excels in clickbait news annotation, correctly labeling 89.66% of the data. However, we also observe significant variations in performance across individual labels. We believe that this research opens new avenues for analysis and can reduce barriers to engaging in social computing research.

关键词： ChatGPT Crowdsourcing Social Data Annotations

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Exploring Transformative Professional Development Within K-12 Computing Education 17th

Exploring Transformative Professional Development Within K-1...

引用

17th International Conference on Informatics in Schools: Situation, Evolution and Perspectives-ISSEP

作者： Sentance, Sue Whyte, Robert Kirby, Diana Univ Cambridge Raspberry Pi Comp Educ Res Ctr Cambridge England Raspberry Pi Fdn Cambridge England

ISBN: (纸本)9783031734731;9783031734748

Many computing educators find themselves teaching a subject that is relatively new to them, making access to high-quality, effective professional development (PD) essential. However computing education research does not always unpack the approach being taken to PD, which may reflect underpinning values and beliefs about teachers' role in the process. The study reported in this paper set out to explore computing PD opportunities using Kennedy's framework of transformative, malleable and transmissive PD, whereby 'transformative' PD refers to approaches that encourage collaborative inquiry and critical professionalism. In the study, 341 computing teachers in primary and secondary education in the UK and Ireland reported on PD they had considered impactful. Results showed that most teachers highlighted transmissive forms of PD as being impactful, primarily delivery-focused training courses, and only 18 teachers described PD categorised as transformative. Most teachers reported that PD was impactful if it built on their prior knowledge. As it is likely that many PD programs are designed around transmissive approaches to PD, we argue that computing teachers should be supported to engage with a broader range of PD opportunities, particularly those that are focused on inquiry and teacher agency.

关键词： computing education K-12 education teacher education

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Enhancing School Success Prediction with FRC and Merged GNN 16th

Enhancing School Success Prediction with FRC and Merged GNN

引用

16th International Conference on Social Networks Analysis and Mining

作者： Aktas, Melike Yildiz Khatri, Aadyant Almutairi, Mariam Alkulaib, Lulwah Lu, Chang-Tien Virginia Tech Dept Comp Sci Falls Church VA 22043 USA Kuwait Univ Dept Comp Sci Kuwait Kuwait

ISBN: (纸本)9783031785474;9783031785481

This study presents an innovative framework for predicting school success, leveraging Large Language Models (LLMs) to define ground truth labels based on comprehensive school information with Factor-Reasoning-Classification (FRC) prompting. In this article, we conceptualize school data as a complex social network and create two different graphs where schools are defined as nodes. Similarity Graph captures school similarities, integrating factors such as graduation rates, ACT scores, socioeconomic conditions, crime rates, and community resources. Geographic Proximity Graph models spatial relationships among schools using geographical coordinates. We define Merged GNN that enhances prediction accuracy by incorporating both similarity-based and spatial proximity-based information. Our approach leverages Graph Neural Networks (GNN) to predict the most probable labels the LLM model identifies. Experimental results on the school success dataset not only demonstrate the superior predictive performance of our methodology over baseline models but also highlight the importance of integrating diverse sources of information for accurate prediction and analysis.

关键词： Data labeling School success prediction Graph neural network Classification Spatial analysis

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Knowledge Graphs (KG) Assisted Variational Autoencoder (VAE) for Large-Scale Anomaly and Event Detection 16th

Knowledge Graphs (KG) Assisted Variational Autoencoder (VAE)...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Zhao, Ying Naval Postgrad Sch Monterey CA 93943 USA

ISBN: (纸本)9783031785535;9783031785542

This work focuses on general ML/AI assisted analytic processes for monitoring, detection, and classification of anomaly signals from multi-modality sensor data. Specifically, I will show series of variational autoencoders (VAE) including unsupervised AI transformers and related workflow pipelines applied to maritime surveillance in the different time scales of hours, minutes, and seconds. I show these developments using the distributed acoustic sensing (DAS) data set. The DAS data set is from the Sandia National Laboratories. DAS is a special type of fiber optic seafloor communications cables to interrogate the submarine environment at Arctic Alaska. Acoustic heatmaps can be generated to detect waves, ships, marine mammals, and other events. There are 18000 channels and sampled at 1kHZ for the data in 2022. The data set is used to demonstrate the VAE methodology for detecting anomaly, event, and classify objects. The results can enable processing and data analytical capabilities critical to actionable intelligence for mission planning and emerging behavior detection.

关键词： anomaly detection event detection machine learning ML artificial intelligence AI generative AI AI transformer variational autoencoder VAE distributed acoustic sensing DAS heatmap latent variables knowledge graph KG

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Navigating Sexualization: A Thematic Analysis of Sexualized Character Design in Modern Video Game Narratives 17th

Navigating Sexualization: A Thematic Analysis of Sexualized ...

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17th International Conference on Interactive Digital Storytelling

作者： Hazer, Zanobia Andersson, Sofia Eladharie, Mirjam Palosaari Stockholm Univ Dept Comp & Syst Sci Stockholm Sweden

ISBN: (纸本)9783031784491;9783031784507

This paper discusses the games Final Fantasy 7 Remake and The Last of Us 2, focusing on which design attributes contribute to the nuances of problematic and non-problematic sexualization of game characters in relation to its contextual relevance in the narrative. Data collection resulted in 1003 images that were analyzed, and a content analysis identified 739 initial codes and the themes of Physical Attributes, Actions, Camera Positioning, Body Language, and Traits and Emotions. The study's findings highlight the importance of considering narrative context and the player's engagement in understanding the effects of sexualization in video games. Additionally, both games illustrate how sexualization can amplify feelings of objectification or provide a safe space for exploring identity, depending on these factors.

关键词： Games Character Design Narrative design Game characters Objectification Sexualization Sexual empowerment Video Games Thematic analysis

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