arXiv - CS - Social and Information Networks最新文献_第2页

Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions 让我们一起影响算法：数百万粉丝如何建立对算法的集体理解并组织协调算法行动

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10670

Qing Xiao, Yuhang Zheng, Xianzhe Fan, Bingbing Zhang, Zhicong Lu

{"title":"Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions","authors":"Qing Xiao, Yuhang Zheng, Xianzhe Fan, Bingbing Zhang, Zhicong Lu","doi":"arxiv-2409.10670","DOIUrl":"https://doi.org/arxiv-2409.10670","url":null,"abstract":"Previous research pays attention to how users strategically understand and\u0000consciously interact with algorithms but mainly focuses on an individual level,\u0000making it difficult to explore how users within communities could develop a\u0000collective understanding of algorithms and organize collective algorithmic\u0000actions. Through a two-year ethnography of online fan activities, this study\u0000investigates 43 core fans who always organize large-scale fans' collective\u0000actions and their corresponding general fan groups. This study aims to reveal\u0000how these core fans mobilize millions of general fans through collective\u0000algorithmic actions. These core fans reported the rhetorical strategies used to\u0000persuade general fans, the steps taken to build a collective understanding of\u0000algorithms, and the collaborative processes that adapt collective actions\u0000across platforms and cultures. Our findings highlight the key factors that\u0000enable computer-supported collective algorithmic actions and extend collective\u0000action research into large-scale domain targeting algorithms.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Graph Pooling Based on Minimum Description Length 基于最小描述长度的分层图池化

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10263

Jan von Pichowski, Christopher Blöcker, Ingo Scholtes

引用次数: 0

Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings 用于可解释和极化感知网络嵌入的有符号图自动编码器

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10452

Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michalis Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis

{"title":"Signed Graph Autoencoder for Explainable and Polarization-Aware Network Embeddings","authors":"Nikolaos Nakis, Chrysoula Kosma, Giannis Nikolentzos, Michalis Chatzianastasis, Iakovos Evdaimon, Michalis Vazirgiannis","doi":"arxiv-2409.10452","DOIUrl":"https://doi.org/arxiv-2409.10452","url":null,"abstract":"Autoencoders based on Graph Neural Networks (GNNs) have garnered significant\u0000attention in recent years for their ability to extract informative latent\u0000representations, characterizing the structure of complex topologies, such as\u0000graphs. Despite the prevalence of Graph Autoencoders, there has been limited\u0000focus on developing and evaluating explainable neural-based graph generative\u0000models specifically designed for signed networks. To address this gap, we\u0000propose the Signed Graph Archetypal Autoencoder (SGAAE) framework. SGAAE\u0000extracts node-level representations that express node memberships over distinct\u0000extreme profiles, referred to as archetypes, within the network. This is\u0000achieved by projecting the graph onto a learned polytope, which governs its\u0000polarization. The framework employs a recently proposed likelihood for\u0000analyzing signed networks based on the Skellam distribution, combined with\u0000relational archetypal analysis and GNNs. Our experimental evaluation\u0000demonstrates the SGAAEs' capability to successfully infer node memberships over\u0000the different underlying latent structures while extracting competing\u0000communities formed through the participation of the opposing views in the\u0000network. Additionally, we introduce the 2-level network polarization problem\u0000and show how SGAAE is able to characterize such a setting. The proposed model\u0000achieves high performance in different tasks of signed link prediction across\u0000four real-world datasets, outperforming several baseline models.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Semantics Preserving Emoji Recommendation with Large Language Models 利用大型语言模型进行语义保存型表情符号推荐

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10760

Zhongyi Qiu, Kangyi Qiu, Hanjia Lyu, Wei Xiong, Jiebo Luo

{"title":"Semantics Preserving Emoji Recommendation with Large Language Models","authors":"Zhongyi Qiu, Kangyi Qiu, Hanjia Lyu, Wei Xiong, Jiebo Luo","doi":"arxiv-2409.10760","DOIUrl":"https://doi.org/arxiv-2409.10760","url":null,"abstract":"Emojis have become an integral part of digital communication, enriching text\u0000by conveying emotions, tone, and intent. Existing emoji recommendation methods\u0000are primarily evaluated based on their ability to match the exact emoji a user\u0000chooses in the original text. However, they ignore the essence of users'\u0000behavior on social media in that each text can correspond to multiple\u0000reasonable emojis. To better assess a model's ability to align with such\u0000real-world emoji usage, we propose a new semantics preserving evaluation\u0000framework for emoji recommendation, which measures a model's ability to\u0000recommend emojis that maintain the semantic consistency with the user's text.\u0000To evaluate how well a model preserves semantics, we assess whether the\u0000predicted affective state, demographic profile, and attitudinal stance of the\u0000user remain unchanged. If these attributes are preserved, we consider the\u0000recommended emojis to have maintained the original semantics. The advanced\u0000abilities of Large Language Models (LLMs) in understanding and generating\u0000nuanced, contextually relevant output make them well-suited for handling the\u0000complexities of semantics preserving emoji recommendation. To this end, we\u0000construct a comprehensive benchmark to systematically assess the performance of\u0000six proprietary and open-source LLMs using different prompting techniques on\u0000our task. Our experiments demonstrate that GPT-4o outperforms other LLMs,\u0000achieving a semantics preservation score of 79.23%. Additionally, we conduct\u0000case studies to analyze model biases in downstream classification tasks and\u0000evaluate the diversity of the recommended emojis.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Impact Of Emotions on Information Seeking And Sharing Behaviors During Pandemic 大流行病期间情绪对信息搜索和共享行为的影响

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10754

Smitha Muthya Sudheendra, Hao Xu, Jisu Huh, Jaideep Srivastava

{"title":"Impact Of Emotions on Information Seeking And Sharing Behaviors During Pandemic","authors":"Smitha Muthya Sudheendra, Hao Xu, Jisu Huh, Jaideep Srivastava","doi":"arxiv-2409.10754","DOIUrl":"https://doi.org/arxiv-2409.10754","url":null,"abstract":"We propose a novel approach to assess the public's coping behavior during the\u0000COVID-19 outbreak by examining the emotions. Specifically, we explore (1)\u0000changes in the public's emotions with the COVID-19 crisis progression and (2)\u0000the impacts of the public's emotions on their information-seeking,\u0000information-sharing behaviors, and compliance with stay-at-home policies. We\u0000base the study on the appraisal tendency framework, detect the public's\u0000emotions by fine-tuning a pre-trained RoBERTa model, and cross-analyze\u0000third-party behavioral data. We demonstrate the feasibility and reliability of\u0000our proposed approach in providing a large-scale examination of the publi's\u0000emotions and coping behaviors in a real-world crisis: COVID-19. The approach\u0000complements prior crisis communication research, mainly based on self-reported,\u0000small-scale experiments and survey data. Our results show that anger and fear\u0000are more prominent than other emotions experienced by the public at the\u0000pandemic's outbreak stage. Results also show that the extent of low certainty\u0000and passive emotions (e.g., sadness, fear) was related to increased\u0000information-seeking and information-sharing behaviors. Additionally,\u0000high-certainty (e.g., anger) and low-certainty (e.g., sadness, fear) emotions\u0000during the outbreak correlated to the public's compliance with stay-at-home\u0000orders.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142269353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning 针对嗜异图形学习的参数化拉普拉奇灵活扩散范围

arXiv - CS - Social and Information Networks Pub Date : 2024-09-15 DOI: arxiv-2409.09888

Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang

{"title":"Flexible Diffusion Scopes with Parameterized Laplacian for Heterophilic Graph Learning","authors":"Qincheng Lu, Jiaqi Zhu, Sitao Luan, Xiao-Wen Chang","doi":"arxiv-2409.09888","DOIUrl":"https://doi.org/arxiv-2409.09888","url":null,"abstract":"The ability of Graph Neural Networks (GNNs) to capture long-range and global\u0000topology information is limited by the scope of conventional graph Laplacian,\u0000leading to unsatisfactory performance on some datasets, particularly on\u0000heterophilic graphs. To address this limitation, we propose a new class of\u0000parameterized Laplacian matrices, which provably offers more flexibility in\u0000controlling the diffusion distance between nodes than the conventional graph\u0000Laplacian, allowing long-range information to be adaptively captured through\u0000diffusion on graph. Specifically, we first prove that the diffusion distance\u0000and spectral distance on graph have an order-preserving relationship. With this\u0000result, we demonstrate that the parameterized Laplacian can accelerate the\u0000diffusion of long-range information, and the parameters in the Laplacian enable\u0000flexibility of the diffusion scopes. Based on the theoretical results, we\u0000propose topology-guided rewiring mechanism to capture helpful long-range\u0000neighborhood information for heterophilic graphs. With this mechanism and the\u0000new Laplacian, we propose two GNNs with flexible diffusion scopes: namely the\u0000Parameterized Diffusion based Graph Convolutional Networks (PD-GCN) and Graph\u0000Attention Networks (PD-GAT). Synthetic experiments reveal the high correlations\u0000between the parameters of the new Laplacian and the performance of\u0000parameterized GNNs under various graph homophily levels, which verifies that\u0000our new proposed GNNs indeed have the ability to adjust the parameters to\u0000adaptively capture the global information for different levels of heterophilic\u0000graphs. They also outperform the state-of-the-art (SOTA) models on 6 out of 7\u0000real-world benchmark datasets, which further confirms their superiority.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks 动态欺诈检测：将强化学习融入图神经网络

arXiv - CS - Social and Information Networks Pub Date : 2024-09-15 DOI: arxiv-2409.09892

Yuxin Dong, Jianhua Yao, Jiajing Wang, Yingbin Liang, Shuhan Liao, Minheng Xiao

{"title":"Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks","authors":"Yuxin Dong, Jianhua Yao, Jiajing Wang, Yingbin Liang, Shuhan Liao, Minheng Xiao","doi":"arxiv-2409.09892","DOIUrl":"https://doi.org/arxiv-2409.09892","url":null,"abstract":"Financial fraud refers to the act of obtaining financial benefits through\u0000dishonest means. Such behavior not only disrupts the order of the financial\u0000market but also harms economic and social development and breeds other illegal\u0000and criminal activities. With the popularization of the internet and online\u0000payment methods, many fraudulent activities and money laundering behaviors in\u0000life have shifted from offline to online, posing a great challenge to\u0000regulatory authorities. How to efficiently detect these financial fraud\u0000activities has become an urgent issue that needs to be resolved. Graph neural\u0000networks are a type of deep learning model that can utilize the interactive\u0000relationships within graph structures, and they have been widely applied in the\u0000field of fraud detection. However, there are still some issues. First,\u0000fraudulent activities only account for a very small part of transaction\u0000transfers, leading to an inevitable problem of label imbalance in fraud\u0000detection. At the same time, fraudsters often disguise their behavior, which\u0000can have a negative impact on the final prediction results. In addition,\u0000existing research has overlooked the importance of balancing neighbor\u0000information and central node information. For example, when the central node\u0000has too many neighbors, the features of the central node itself are often\u0000neglected. Finally, fraud activities and patterns are constantly changing over\u0000time, so considering the dynamic evolution of graph edge relationships is also\u0000very important.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

What you say or how you say it? Predicting Conflict Outcomes in Real and LLM-Generated Conversations 说什么还是怎么说？预测真实对话和 LLM 生成的对话中的冲突结果

arXiv - CS - Social and Information Networks Pub Date : 2024-09-14 DOI: arxiv-2409.09338

Priya Ronald D'Costa, Evan Rowbotham, Xinlan Emily Hu

引用次数: 0

Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design 实现令人满意的公共无障碍环境：通过在线评论为包容性城市设计提供众包方法

arXiv - CS - Social and Information Networks Pub Date : 2024-09-13 DOI: arxiv-2409.08459

Lingyao Li, Songhua Hu, Yinpei Dai, Min Deng, Parisa Momeni, Gabriel Laverghetta, Lizhou Fan, Zihui Ma, Xi Wang, Siyuan Ma, Jay Ligatti, Libby Hemphill

{"title":"Toward satisfactory public accessibility: A crowdsourcing approach through online reviews to inclusive urban design","authors":"Lingyao Li, Songhua Hu, Yinpei Dai, Min Deng, Parisa Momeni, Gabriel Laverghetta, Lizhou Fan, Zihui Ma, Xi Wang, Siyuan Ma, Jay Ligatti, Libby Hemphill","doi":"arxiv-2409.08459","DOIUrl":"https://doi.org/arxiv-2409.08459","url":null,"abstract":"As urban populations grow, the need for accessible urban design has become\u0000urgent. Traditional survey methods for assessing public perceptions of\u0000accessibility are often limited in scope. Crowdsourcing via online reviews\u0000offers a valuable alternative to understanding public perceptions, and\u0000advancements in large language models can facilitate their use. This study uses\u0000Google Maps reviews across the United States and fine-tunes Llama 3 model with\u0000the Low-Rank Adaptation technique to analyze public sentiment on accessibility.\u0000At the POI level, most categories -- restaurants, retail, hotels, and\u0000healthcare -- show negative sentiments. Socio-spatial analysis reveals that\u0000areas with higher proportions of white residents and greater socioeconomic\u0000status report more positive sentiment, while areas with more elderly,\u0000highly-educated residents exhibit more negative sentiment. Interestingly, no\u0000clear link is found between the presence of disabilities and public sentiments.\u0000Overall, this study highlights the potential of crowdsourcing for identifying\u0000accessibility challenges and providing insights for urban planners.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

User Identity Linkage on Social Networks: A Review of Modern Techniques and Applications 社交网络上的用户身份链接：现代技术与应用综述

arXiv - CS - Social and Information Networks Pub Date : 2024-09-13 DOI: arxiv-2409.08966

Caterina Senette, Marco Siino, Maurizio Tesconi

{"title":"User Identity Linkage on Social Networks: A Review of Modern Techniques and Applications","authors":"Caterina Senette, Marco Siino, Maurizio Tesconi","doi":"arxiv-2409.08966","DOIUrl":"https://doi.org/arxiv-2409.08966","url":null,"abstract":"In an Online Social Network (OSN), users can create a unique public persona\u0000by crafting a user identity that may encompass profile details, content, and\u0000network-related information. As a result, a relevant task of interest is\u0000related to the ability to link identities across different OSNs. Linking users\u0000across social networks can have multiple implications in several contexts both\u0000at the individual level and at the group level. At the individual level, the\u0000main interest in linking the same identity across social networks is to enable\u0000a better knowledge of each user. At the group level, linking user identities\u0000through different OSNs helps in predicting user behaviors, network dynamics,\u0000information diffusion, and migration phenomena across social media. The process\u0000of tying together user accounts on different OSNs is challenging and has\u0000attracted more and more research attention in the last fifteen years. The\u0000purpose of this work is to provide a comprehensive review of recent studies\u0000(from 2016 to the present) on User Identity Linkage (UIL) methods across online\u0000social networks. This review aims to offer guidance for other researchers in\u0000the field by outlining the main problem formulations, the different feature\u0000extraction strategies, algorithms, machine learning models, datasets, and\u0000evaluation metrics proposed by researchers working in this area. The proposed\u0000overview takes a pragmatic perspective to highlight the concrete possibilities\u0000for accomplishing this task depending on the type of available data.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263475","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0