arXiv - CS - Social and Information Networks最新文献

Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks 数字时代的社区塑造：分析在线社交网络话语分裂的时空融合框架

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI: arxiv-2409.11665

Amirhossein Dezhboro, Jose Emmanuel Ramirez-Marquez, Aleksandra Krstikj

{"title":"Community Shaping in the Digital Age: A Temporal Fusion Framework for Analyzing Discourse Fragmentation in Online Social Networks","authors":"Amirhossein Dezhboro, Jose Emmanuel Ramirez-Marquez, Aleksandra Krstikj","doi":"arxiv-2409.11665","DOIUrl":"https://doi.org/arxiv-2409.11665","url":null,"abstract":"This research presents a framework for analyzing the dynamics of online\u0000communities in social media platforms, utilizing a temporal fusion of text and\u0000network data. By combining text classification and dynamic social network\u0000analysis, we uncover mechanisms driving community formation and evolution,\u0000revealing the influence of real-world events. We introduced fourteen key\u0000elements based on social science theories to evaluate social media dynamics,\u0000validating our framework through a case study of Twitter data during major U.S.\u0000events in 2020. Our analysis centers on discrimination discourse, identifying\u0000sexism, racism, xenophobia, ableism, homophobia, and religious intolerance as\u0000main fragments. Results demonstrate rapid community emergence and dissolution\u0000cycles representative of discourse fragments. We reveal how real-world\u0000circumstances impact discourse dominance and how social media contributes to\u0000echo chamber formation and societal polarization. Our comprehensive approach\u0000provides insights into discourse fragmentation, opinion dynamics, and\u0000structural aspects of online communities, offering a methodology for\u0000understanding the complex interplay between online interactions and societal\u0000trends.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263396","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

"It Might be Technically Impressive, But It's Practically Useless to Us": Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry "技术上可能令人印象深刻，但实际上对我们毫无用处"：新闻行业围绕人工智能开展跨职能合作的实践、挑战和机遇

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI: arxiv-2409.12000

Qing Xiao, Xianzhe Fan, Felix M. Simon, Bingbing Zhang, Motahhare Eslami

{"title":"\"It Might be Technically Impressive, But It's Practically Useless to Us\": Practices, Challenges, and Opportunities for Cross-Functional Collaboration around AI within the News Industry","authors":"Qing Xiao, Xianzhe Fan, Felix M. Simon, Bingbing Zhang, Motahhare Eslami","doi":"arxiv-2409.12000","DOIUrl":"https://doi.org/arxiv-2409.12000","url":null,"abstract":"Recently, an increasing number of news organizations have integrated\u0000artificial intelligence (AI) into their workflows, leading to a further influx\u0000of AI technologists and data workers into the news industry. This has initiated\u0000cross-functional collaborations between these professionals and journalists.\u0000While prior research has explored the impact of AI-related roles entering the\u0000news industry, there is a lack of studies on how cross-functional collaboration\u0000unfolds between AI professionals and journalists. Through interviews with 17\u0000journalists, 6 AI technologists, and 3 AI workers with cross-functional\u0000experience from leading news organizations, we investigate the current\u0000practices, challenges, and opportunities for cross-functional collaboration\u0000around AI in today's news industry. We first study how journalists and AI\u0000professionals perceive existing cross-collaboration strategies. We further\u0000explore the challenges of cross-functional collaboration and provide\u0000recommendations for enhancing future cross-functional collaboration around AI\u0000in the news industry.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263400","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

A novel DFS/BFS approach towards link prediction 用于链路预测的新型 DFS/BFS 方法

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI: arxiv-2409.11687

Jens Dörpinghaus, Tobias Hübenthal, Denis Stepanov

引用次数: 0

Skill matching at scale: freelancer-project alignment for efficient multilingual candidate retrieval 大规模技能匹配：自由职业者-项目对齐，实现高效多语言候选人检索

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI: arxiv-2409.12097

Warren Jouanneau, Marc Palyart, Emma Jouffroy

引用次数: 0

My Views Do Not Reflect Those of My Employer: Differences in Behavior of Organizations' Official and Personal Social Media Accounts 我的观点并不反映我雇主的观点：组织官方和个人社交媒体账户的行为差异

arXiv - CS - Social and Information Networks Pub Date : 2024-09-18 DOI: arxiv-2409.11759

Esa Palosaari, Ted Hsuan Yun Chen, Arttu Malkamäki, Mikko Kivelä

{"title":"My Views Do Not Reflect Those of My Employer: Differences in Behavior of Organizations' Official and Personal Social Media Accounts","authors":"Esa Palosaari, Ted Hsuan Yun Chen, Arttu Malkamäki, Mikko Kivelä","doi":"arxiv-2409.11759","DOIUrl":"https://doi.org/arxiv-2409.11759","url":null,"abstract":"On social media, the boundaries between people's private and public lives\u0000often blur. The need to navigate both roles, which are governed by distinct\u0000norms, impacts how individuals conduct themselves online, and presents\u0000methodological challenges for researchers. We conduct a systematic exploration\u0000on how an organization's official Twitter accounts and its members' personal\u0000accounts differ. Using a climate change Twitter data set as our case, we find\u0000substantial differences in activity and connectivity across the organizational\u0000levels we examined. The levels differed considerably in their overall retweet\u0000network structures, and accounts within each level were more likely to have\u0000similar connections than accounts at different levels. We illustrate the\u0000implications of these differences for applied research by showing that the\u0000levels closer to the core of the organization display more sectoral homophily\u0000but less triadic closure, and how each level consists of very different group\u0000structures. Our results show that the common practice of solely analyzing\u0000accounts from a single organizational level, grouping together all levels, or\u0000excluding certain levels can lead to a skewed understanding of how\u0000organizations are represented on social media.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263392","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

Inside Alameda Research: A Multi-Token Network Analysis 阿拉米达研究中心内部：多代币网络分析

arXiv - CS - Social and Information Networks Pub Date : 2024-09-17 DOI: arxiv-2409.10949

Célestin Coquidé, Rémy Cazabet, Natkamon Tovanich

引用次数: 0

A Property Encoder for Graph Neural Networks 图神经网络的属性编码器

arXiv - CS - Social and Information Networks Pub Date : 2024-09-17 DOI: arxiv-2409.11554

Anwar Said, Xenofon Koutsoukos

{"title":"A Property Encoder for Graph Neural Networks","authors":"Anwar Said, Xenofon Koutsoukos","doi":"arxiv-2409.11554","DOIUrl":"https://doi.org/arxiv-2409.11554","url":null,"abstract":"Graph machine learning, particularly using graph neural networks,\u0000fundamentally relies on node features. Nevertheless, numerous real-world\u0000systems, such as social and biological networks, often lack node features due\u0000to various reasons, including privacy concerns, incomplete or missing data, and\u0000limitations in data collection. In such scenarios, researchers typically resort\u0000to methods like structural and positional encoding to construct node features.\u0000However, the length of such features is contingent on the maximum value within\u0000the property being encoded, for example, the highest node degree, which can be\u0000exceedingly large in applications like scale-free networks. Furthermore, these\u0000encoding schemes are limited to categorical data and might not be able to\u0000encode metrics returning other type of values. In this paper, we introduce a\u0000novel, universally applicable encoder, termed PropEnc, which constructs\u0000expressive node embedding from any given graph metric. PropEnc leverages\u0000histogram construction combined with reverse index encoding, offering a\u0000flexible method for node features initialization. It supports flexible encoding\u0000in terms of both dimensionality and type of input, demonstrating its\u0000effectiveness across diverse applications. PropEnc allows encoding metrics in\u0000low-dimensional space which effectively avoids the issue of sparsity and\u0000enhances the efficiency of the models. We show that emph{PropEnc} can\u0000construct node features that either exactly replicate one-hot encoding or\u0000closely approximate indices under various settings. Our extensive evaluations\u0000in graph classification setting across multiple social networks that lack node\u0000features support our hypothesis. The empirical results conclusively demonstrate\u0000that PropEnc is both an efficient and effective mechanism for constructing node\u0000features from diverse set of graph metrics.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263398","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

Capturing Differences in Character Representations Between Communities: An Initial Study with Fandom 捕捉不同社群之间角色表现形式的差异：关于粉丝的初步研究

arXiv - CS - Social and Information Networks Pub Date : 2024-09-17 DOI: arxiv-2409.11170

Bianca N. Y. Kang

{"title":"Capturing Differences in Character Representations Between Communities: An Initial Study with Fandom","authors":"Bianca N. Y. Kang","doi":"arxiv-2409.11170","DOIUrl":"https://doi.org/arxiv-2409.11170","url":null,"abstract":"Sociolinguistic theories have highlighted how narratives are often retold,\u0000co-constructed and reconceptualized in collaborative settings. This working\u0000paper focuses on the re-interpretation of characters, an integral part of the\u0000narrative story-world, and attempts to study how this may be computationally\u0000compared between online communities. Using online fandom - a highly communal\u0000phenomenon that has been largely studied qualitatively - as data, computational\u0000methods were applied to explore shifts in character representations between two\u0000communities and the original text. Specifically, text from the Harry Potter\u0000novels, r/HarryPotter subreddit, and fanfiction on Archive of Our Own were\u0000analyzed for changes in character mentions, centrality measures from\u0000co-occurrence networks, and semantic associations. While fandom elevates\u0000secondary characters as found in past work, the two fan communities prioritize\u0000different subsets of characters. Word embedding tests reveal starkly different\u0000associations of the same characters between communities on the gendered\u0000concepts of femininity/masculinity, cruelty, and beauty. Furthermore,\u0000fanfiction descriptions of a male character analyzed between romance pairings\u0000scored higher for feminine-coded characteristics in male-male romance, matching\u0000past qualitative theorizing. The results high-light the potential for\u0000computational methods to assist in capturing the re-conceptualization of\u0000narrative elements across communities and in supporting qualitative research on\u0000fandom.","PeriodicalId":501032,"journal":{"name":"arXiv - CS - Social and Information Networks","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142263434","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

Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs 利用最密集重叠子图在超图神经网络中建立超edge 模型

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10340

Mehrad Soltani, Luis Rueda

{"title":"Hyperedge Modeling in Hypergraph Neural Networks by using Densest Overlapping Subgraphs","authors":"Mehrad Soltani, Luis Rueda","doi":"arxiv-2409.10340","DOIUrl":"https://doi.org/arxiv-2409.10340","url":null,"abstract":"Hypergraphs tackle the limitations of traditional graphs by introducing {em\u0000hyperedges}. While graph edges connect only two nodes, hyperedges connect an\u0000arbitrary number of nodes along their edges. Also, the underlying\u0000message-passing mechanisms in Hypergraph Neural Networks (HGNNs) are in the\u0000form of vertex-hyperedge-vertex, which let HGNNs capture and utilize richer and\u0000more complex structural information than traditional Graph Neural Networks\u0000(GNNs). More recently, the idea of overlapping subgraphs has emerged. These\u0000subgraphs can capture more information about subgroups of vertices without\u0000limiting one vertex belonging to just one group, allowing vertices to belong to\u0000multiple groups or subgraphs. In addition, one of the most important problems\u0000in graph clustering is to find densest overlapping subgraphs (DOS). In this\u0000paper, we propose a solution to the DOS problem via Agglomerative Greedy\u0000Enumeration (DOSAGE) algorithm as a novel approach to enhance the process of\u0000generating the densest overlapping subgraphs and, hence, a robust construction\u0000of the hypergraphs. Experiments on standard benchmarks show that the DOSAGE\u0000algorithm significantly outperforms the HGNNs and six other methods on the node\u0000classification task.","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":"142263439","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

ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation ES-KT-24：包含教育游戏视频和合成文本生成的多模态知识追踪基准数据集

arXiv - CS - Social and Information Networks Pub Date : 2024-09-16 DOI: arxiv-2409.10244

Dohee Kim, Unggi Lee, Sookbun Lee, Jiyeong Bae, Taekyung Ahn, Jaekwon Park, Gunho Lee, Hyeoncheol Kim

{"title":"ES-KT-24: A Multimodal Knowledge Tracing Benchmark Dataset with Educational Game Playing Video and Synthetic Text Generation","authors":"Dohee Kim, Unggi Lee, Sookbun Lee, Jiyeong Bae, Taekyung Ahn, Jaekwon Park, Gunho Lee, Hyeoncheol Kim","doi":"arxiv-2409.10244","DOIUrl":"https://doi.org/arxiv-2409.10244","url":null,"abstract":"This paper introduces ES-KT-24, a novel multimodal Knowledge Tracing (KT)\u0000dataset for intelligent tutoring systems in educational game contexts. Although\u0000KT is crucial in adaptive learning, existing datasets often lack game-based and\u0000multimodal elements. ES-KT-24 addresses these limitations by incorporating\u0000educational game-playing videos, synthetically generated question text, and\u0000detailed game logs. The dataset covers Mathematics, English, Indonesian, and\u0000Malaysian subjects, emphasizing diversity and including non-English content.\u0000The synthetic text component, generated using a large language model,\u0000encompasses 28 distinct knowledge concepts and 182 questions, featuring 15,032\u0000users and 7,782,928 interactions. Our benchmark experiments demonstrate the\u0000dataset's utility for KT research by comparing Deep learning-based KT models\u0000with Language Model-based Knowledge Tracing (LKT) approaches. Notably, LKT\u0000models showed slightly higher performance than traditional DKT models,\u0000highlighting the potential of language model-based approaches in this field.\u0000Furthermore, ES-KT-24 has the potential to significantly advance research in\u0000multimodal KT models and learning analytics. By integrating game-playing videos\u0000and detailed game logs, this dataset offers a unique approach to dissecting\u0000student learning patterns through advanced data analysis and machine-learning\u0000techniques. It has the potential to unearth new insights into the learning\u0000process and inspire further exploration in the field.","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":"142263437","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