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To Reply or to Quote: Comparing Conversational Framing Strategies on Twitter

ACM Journal on Computing and Sustainable Societies 2024年第1期2卷 1-27页

作者： Himanshu Zade Spencer Williams Theresa T. Tran Christina Smith Sukrit Venkatagiri Gary Hsieh Kate Starbird Human Centered Design & Engineering University of Washington Department of Computer Science Swarthmore College Human Centered Design & Engineering and Center for an Informed Public University of Washington

Social media platform affordances allow users to interact with content and with each other in diverse ways. For example, on Twitter,1 users can like, reply, retweet, or quote another tweet. Though it’s clear that these different features allow various types of interactions, open questions remain about how these different affordances shape the conversations. We examine how two similar, but distinct conversational features on Twitter — specifically reply vs. quote — are used differently. Focusing on the polarized discourse around Robert Mueller’s congressional testimony in July 2019, we look at how these features are employed in conversations between politically aligned and opposed accounts. We use a mixed methods approach, employing grounded qualitative analysis to identify the different conversational and framing strategies salient in that discourse and then quantitatively analyzing how those techniques differed across the different features and political alignments. Our research (1) demonstrates that the quote feature is more often used to broadcast and reply is more often used to reframe the conversation; (2) identifies the different framing strategies that emerge through the use of these features when engaging with politically aligned vs. opposed accounts; (3) discusses how reply and quote features may be re-designed to reduce the adversarial tone of polarized conversations on Twitter-like platforms.

关键词： Twitter social network analysis microblogging platforms social media platforms quote tweets reply tweets conversational framing

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Working in Extended Reality in the Wild: Worker and Bystander Experiences of XR Virtual Displays in Real-World Settings

arXiv

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arXiv 2024年

作者： Pavanatto, Leonardo Biener, Verena Chandran, Jennifer Kalamkar, Snehanjali Lu, Feiyu Dudley, John J. Hu, Jinghui Nikki Ramirez-Saffy, G. Kristensson, Per Ola Giovannelli, Alexander Schlueter, Luke Müller, Jörg Grubert, Jens Bowman, Doug A. The Center for Human-Computer Interaction Department of Computer Science Virginia Tech BlacksburgVA United States The Visualization Research Center University of Stuttgart Germany Coburg University of Applied Sciences and Arts Germany The University of Bayreuth Germany The Department of Engineering University of Cambridge United Kingdom

Although access to sufficient screen space is crucial to knowledge work, workers often find themselves with limited access to display infrastructure in remote or public settings. While virtual displays can be used to extend the available screen space through extended reality (XR) head-worn displays (HWD), we must better understand the implications of working with them in public settings from both users’ and bystanders’ viewpoints. To this end, we conducted two user studies. We first explored the usage of a hybrid AR display across real-world settings and tasks. We focused on how users take advantage of virtual displays and what social and environmental factors impact their usage of the system. A second study investigated the differences between working with a laptop, an AR system, or a VR system in public. We focused on a single location and participants performed a predefined task to enable direct comparisons between the conditions while also gathering data from bystanders. The combined results suggest a positive acceptance of XR technology in public settings and show that virtual displays can be used to accompany existing devices. We highlighted some environmental and social factors. We saw that previous XR experience and personality can influence how people perceive the use of XR in public. In addition, we confirmed that using XR in public still makes users stand out and that bystanders are curious about the devices, yet have no clear understanding of how they can be used. Copyright © 2024, The Authors. All rights reserved.

关键词： Virtual environments

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Span-level Emotion-Cause-Category Triplet Extraction with Instruction Tuning LLMs and Data Augmentation

arXiv

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arXiv 2025年

作者： Li, Xiangju Yang, Dong Zhu, Xiaogang Huang, Faliang Zhang, Peng Zhao, Zhongying College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266500 China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning530100 China

Span-level emotion-cause-category triplet extraction represents a novel and complex challenge within emotion cause analysis. This task involves identifying emotion spans, cause spans, and their associated emotion categories within the text to form structured triplets. While prior research has predominantly concentrated on clause-level emotion-cause pair extraction and span-level emotion-cause detection, these methods often confront challenges originating from redundant information retrieval and difficulty in accurately determining emotion categories, particularly when emotions are expressed implicitly or ambiguously. To overcome these challenges, this study explores a fine-grained approach to span-level emotion-cause-category triplet extraction and introduces an innovative framework that leverages instruction tuning and data augmentation techniques based on large language models. The proposed method employs task-specific triplet extraction instructions and utilizes low-rank adaptation to fine-tune large language models, eliminating the necessity for intricate task-specific architectures. Furthermore, a prompt-based data augmentation strategy is developed to address data scarcity by guiding large language models in generating high-quality synthetic training data. Extensive experimental evaluations demonstrate that the proposed approach significantly outperforms existing baseline methods, achieving at least a 12.8% improvement in span-level emotion-cause-category triplet extraction metrics. The results demonstrate the method’s effectiveness and robustness, offering a promising avenue for advancing research in emotion cause analysis. The source code is available at https://***/zxgnlp/InstruDa-LLM. Copyright © 2025, The Authors. All rights reserved.

关键词： Natural language processing systems

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DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting 29

DP-Rewrite: Towards Reproducibility and Transparency in Diff...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Igamberdiev, Timour Arnold, Thomas Habernal, Ivan Trustworthy Human Language Technologies Ubiquitous Knowledge Processing Lab Department of Computer Science Technical University of Darmstadt Germany

Text rewriting with differential privacy (DP) provides concrete theoretical guarantees for protecting the privacy of individuals in textual documents. In practice, existing systems may lack the means to validate their privacy-preserving claims, leading to problems of transparency and reproducibility. We introduce DP-Rewrite, an open-source framework for differentially private text rewriting which aims to solve these problems by being modular, extensible, and highly customizable. Our system incorporates a variety of downstream datasets, models, pre-training procedures, and evaluation metrics to provide a flexible way to lead and validate private text rewriting research. To demonstrate our software in practice, we provide a set of experiments as a case study on the ADePT DP text rewriting system, detecting a privacy leak in its pre-training approach. Our system is publicly available, and we hope that it will help the community to make DP text rewriting research more accessible and transparent. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Transparency

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VAction: A Lightweight and Integrated VR Training System for Authentic Film-Shooting Experience 25

VAction: A Lightweight and Integrated VR Training System for...

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2025 CHI Conference on human Factors in Computing Systems, CHI 2025

作者： Wang, Shaocong Qu, Che Yu, Minjing Zhou, Chao Wang, Yuntao Wen, Yu-Hui Shi, Yuanchun Liu, Yong-Jin Department of Computer Science and Technology Tsinghua University Beijing China Film & TV Technology Department Beijing Film Academy Beijing China College of Intelligence and Computing Tianjin University Tianjin China Human Computer Interaction Technology Intelligent Information Processing Laboratory Institute of Software Chinese Acadamy of Sciences Beijing China School of Computer Science and Technology Beijing Jiaotong University Beijing China

ISBN: (纸本)9798400713941

The film industry exerts significant economic and cultural influence, and its rapid development is contingent upon the expertise of industry professionals, underscoring the critical importance of film-shooting education. However, this process typically necessitates multiple practice in complex professional venues using expensive equipment, presenting a significant obstacle for ordinary learners who struggle to access such training environments. Despite VR technology has already shown its potential in education, existing research has not addressed the crucial learning component of replicating the shooting process. Moreover, the limited functionality of traditional controllers hinder the fulfillment of the educational requirements. Therefore, we developed VAction VR system, combining high-fidelity virtual environments with a custom-designed controller to simulate the real-world camera operation experience. The system's lightweight design ensures cost-effective and efficient deployment. Experiment results demonstrated that VAction significantly outperforms traditional methods in both practice effectiveness and user experience, indicating its potential and usefulness in film-shooting education. © 2025 Copyright held by the owner/author(s).

关键词： Film Production Education interaction Design Training System Virtual Reality

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Personalized News Recommendation with Multi-granularity Candidate-aware User Modeling

arXiv

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arXiv 2025年

作者： Li, Qiang Lin, Xinze Lv, Shenghao Huang, Faliang Li, Xiangju Shandong Women’s University School of Data and Computer Science Jinan250002 China Shandong University of Science and Technology College of Computer Science and Engineering Qiangdao266500 China Nanning Normal University Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning530100 China

Matching candidate news with user interests is crucial for personalized news recommendations. Most existing methods can represent a user’s reading interests through a single profile based on clicked news, which may not fully capture the diversity of user interests. Although some approaches incorporate candidate news or topic information, they remain insufficient because they neglect the multi-granularity relatedness between candidate news and user interests. To address this, this study proposed a multi-granularity candidate-aware user modeling framework that integrated user interest features across various levels of granularity. It consisted of two main components: candidate news encoding and user modeling. A news textual information extractor and a knowledge-enhanced entity information extractor can capture candidate news features, and word-level, entity-level, and news-level candidate-aware mechanisms can provide a comprehensive representation of user interests. Extensive experiments on a real-world dataset demonstrated that the proposed model could significantly outperform baseline models∗ Copyright © 2025, The Authors. All rights reserved.

关键词： Recommender systems

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Communication Sequences Indicate Team Cohesion: A Mixed-Methods Study of Ad Hoc League of Legends Teams

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Proceedings of the ACM on human-computer interaction 2022年第CHIPLAY期6卷 1–27页

作者： Tan, Evelyn T S Rogers, Katja Nacke, Lennart E. Drachen, Anders Wade, Alex Department of Computer Science University of York United Kingdom Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo Canada Stratford School of Interaction Design and Business HCI Games Group Games Institute University of Waterloo WaterlooON Canada Maersk McKinney Moeller Institute University of Southern Denmark Denmark Department of Psychology University of York United Kingdom

Team cohesion is a widely known predictor of performance and collaborative satisfaction. However, how it develops and can be assessed, especially in fast-paced ad hoc dynamic teams, remains unclear. An unobtrusive and objective behavioural measure of cohesion would help identify determinants of cohesion in these teams. We investigated team communication as a potential measure in a mixed-methods study with 48 teams (n=135) in the digital game League of Legends. We first established that cohesion shows similar performance and satisfaction in League of Legends. teams as in non-game teams and confirmed a positive relationship between communication word frequency and cohesion. Further, we conducted an in-depth exploratory qualitative analysis of the communication sequences in a high-cohesion and a low-cohesion team. High cohesion is associated with sequences of apology-→encouragement, suggestion-→agree/acknowledge, answer-→answer, and answer-→question, while low-cohesion is associated with sequences of opinion/analysis-→opinion/analysis, disagree-→disagree, command-→disagree, and frustration-→frustration. Our findings also show that cohesion is important to team satisfaction independently of the match outcomes. We highlight that communication sequences are more useful than frequencies to determine team cohesion via player interactions. © 2022 ACM.

关键词： computer programming

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Sustainable Foodtures: Exploring Roles of Future Technology in Sustainable Food Shopping 22

Sustainable Foodtures: Exploring Roles of Future Technology ...

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12th Nordic Conference on human-computer interaction: Participative Computing for Sustainable Futures, NordiCHI 2022

作者： Lindrup, Martin Valdemar Anker Cheon, EunJeong Skov, Mikael B. Raptis, Dimitrios Comber, Rob Department of Computer Science Aalborg University Denmark School of Information Studies Syracuse University United States Department of Media Technology and Interaction Design Kth Royal Institute of Technology Sweden

ISBN: (纸本)9781450396998

Several efforts have been made to use technology to encourage people to buy more environmentally sustainable food. However, only a small amount of research has attempted to understand the roles of technology in food shopping by incorporating perspectives from various stakeholders. We aimed to explore how the design space for future technology in sustainable food shopping is negotiated among sustainability-aware individuals, sustainability professionals, and designers through co-design workshops and speculative enactments. We identify three roles of technology in sustainable food shopping: technology as guidance, technology as mediator, and technology for reflection. Additional insights are provided regarding how parts of these roles for future technology are rejected, adopted, or adjusted when enacted in everyday food shopping. We discuss design implications for the roles of sustainability technology in terms of data accuracy and transparency, as well as embodied data experiences. Furthermore, we provide methodological considerations for speculative enactments in SHCI. © 2022 ACM.

关键词： Sustainable development

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Machine Learning-Based Automated Detection of ADHD Using Heart Rate Variability Data 16th

Machine Learning-Based Automated Detection of ADHD Using Hea...

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16th International Conference on Bioinformatics and Computational Biology, BICOB 2024

作者： Ji, Yanqing Zhang-Lea, Janet Tran, John Department of Electrical and Computer Engineering Gonzaga University SpokaneWA United States Department of Human Physiology Gonzaga University SpokaneWA United States Department of Psychiatry and Behavioral Science University of California San Francisco Fresno FresnoCA United States

This study addresses the pressing need for effective methods in detecting Attention-Deficit/Hyperactivity Disorder (ADHD), a neurodevelopmental condition significantly impacting individuals' attention, impulse control, and activity regulation. Leveraging advancements in machine learning and wearable technology, the research explores the potential of Heart Rate Variability (HRV) data as a novel source for ADHD detection. Six machine learning algorithms, including Logistic Regression, Random Forest, XGBoost, LightGBM, Neural Network, and Support Vector Machine, were rigorously investigated using an HRV dataset, marking a pioneering effort in utilizing HRV data for ADHD identification. The results demonstrate promising performance, with Logistic Regression exhibiting the highest F1 score (0.71), and Support Vector Machine achieving the highest Matthews Correlation Coefficient (0.44). This study showcases the capacity of machine learning utilizing HRV data for identifying ADHD, contributing to the evolving landscape of machine learning applications in mental health diagnostics. © 2024, EasyChair. All rights reserved.

关键词： Adversarial machine learning

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Deep Learning-Based computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images

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SN computer science 2023年第5期4卷 502页

作者： Kumar, Sumit Bhupati Bhambu, Pawan Pachar, Sunita Cotrina-Aliaga, Juan Carlos Arias-Gonzáles, José Luis Department of Computer Science and Engineering College of Engineering Roorkee India Department of IoT K L Deemed to be University Vaddeswaram Guntur 522302 India Department of Computer Science Engineering VGU University Jaipur Jaipur India IBM Department GLA University Mathura India Faculty of Human Medicine Universidad Privada San Juan Bautista Chincha Peru Pontificia Universidad Católica del Perú San Miguel Peru

Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured;however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Breast cancer Classification computer-aided diagnosis Deep learning Machine learning Mammography

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