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检索条件"丛书名=Lecture Notes in Computer Science"

共 300003 条记录，以下是51-60 订阅

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A Lightweight Approach for User and Keyword Classification in Controversial Topics 16th

A Lightweight Approach for User and Keyword Classification i...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Zareie, Ahmad Bontcheva, Kalina Scarton, Carolina Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

ISBN: (纸本)9783031785375;9783031785382

Classifying the stance of individuals on controversial topics and uncovering their concerns is crucial for social scientists and policymakers. Data from Online Social Networks (OSNs), which serve as a proxy to a representative sample of society, offers an opportunity to classify these stances, discover society's concerns regarding controversial topics, and track the evolution of these concerns over time. Consequently, stance classification in OSNs has garnered significant attention from researchers. However, most existing methods for this task often rely on labelled data and utilise the text of users' posts or the interactions between users, necessitating large volumes of data, considerable processing time, and access to information that is not readily available (e.g. users' followers/followees). This paper proposes a lightweight approach for the stance classification of users and keywords in OSNs, aiming at understanding the collective opinion of individuals and their concerns. Our approach employs a tailored random walk model, requiring just one keyword representing each stance, using solely the keywords in social media posts. Experimental results demonstrate the superior performance of our method compared to the baselines, excelling in stance classification of users and keywords, with a running time that, while not the fastest, remains competitive.

关键词： Users Classification Keyword Classification Stance Detection Random Walk

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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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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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A Transaction-Level Model for Blockchain Privacy 28th

A Transaction-Level Model for Blockchain Privacy

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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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Event Embedding Learning from Social Media Using Graph Topic Model Autoencoder 16th

Event Embedding Learning from Social Media Using Graph Topic...

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16th International Conference on Social Networks Analysis and Mining

作者： Zhang, Yihong Hara, Takahiro Osaka Univ Multimedia Data Engn Lab Grad Sch Informat Sci & Technol Osaka Japan

ISBN: (纸本)9783031785375;9783031785382

Event detection from social media has been researched intensively and applied to many social problems such as disaster monitoring, rumor detection, and product sales prediction. In this paper, we deal with the underlying task of event representation. Existing works use topic modeling or graph embedding to extract events from text corpora, but have not fully utilized the available information. We propose a novel model called Graph Topic Model Autoencoder (GTMA) for improving event representation quality. The model combines non-negative matrix factorization and graph autoencoder to take advantages of the document-word matrix and the word-word co-occurrence graph. The model outputs event embeddings that can be used for topic-based event detection. We test our approach with two real-world social media datasets. Compared to several embedding learning baselines, our model generally achieves better topic quality in event detection evaluation.

关键词： event representation social media mining graph topic model

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A Social Network Based Approach to Analyzing Artistic Influences on American Stand-Up Comedians 16th

A Social Network Based Approach to Analyzing Artistic Influe...

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16th International Conference on Social Networks Analysis and Mining

作者： Date, Sachin VitalEdge Technol Mumbai Maharashtra India

ISBN: (纸本)9783031785535;9783031785542

A considerable amount of scholarly study has been done on the art of "Stand-Up" comedy, particular American Stand-Up, on its history and vaudevillian origins, and its impact on several fields such as teaching, public communication, politics, and culture. However, there is a striking absence of studies that quantitatively examine the origins and patterns of artistic influences on the work of American Stand-Up comedians. In this paper, we attempted to fill that gap. We analyzed artistic influence, an intrinsically unmeasurable variable, using a set of observable proxies. Using these proxies, we modeled artistic influences on American Stand-Up comedians as a social influence network and studied the diffusion of influence through this network. We found that chains of artistic influence on American Stand-Up comedians originated from times much older than the vaudevillian era of the late 1800s to early 1900s. Indeed, they originated from European writers of the late middle ages. We also found that an overwhelming volume (91.23%) of direct artistic influence on American Stand-Up comedians came from individuals who were at most two generations (50 years) older than the influenced comedians, and more than half (55.73%) of the direct influence came from individuals of the same generation. We found the total volume of artistic influence (direct and indirect) on the American Stand-Up comedians followed a pronounced Pareto pattern with 11.40% and 16.67% of influencers contributing respectively 79.64% and 90.06% of the total influence volume. We also found the social influence network of American Stand-Up comedians to exhibit "small-world" network behavior.

关键词： stand-up comedy artistic influence social network small-world network

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WILDWOOD: Southside Chicago Worldbuilding Through Interactive Digital Narrative 17th

WILDWOOD: Southside Chicago Worldbuilding Through Interactiv...

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17th International Conference on Interactive Digital Storytelling

作者： Alexander, E. M. Univ Maryland College Pk MD 20742 USA

ISBN: (纸本)9783031784491;9783031784507

From the archive to the screen, Black digital humanists regularly engage interactive digital media as a tool of social justice-based creative placemaking. In this short paper, Alexander examines interactive digital narrative as a radical worldbuilding platform, particularly for marginalized communities, and outlines a theoretical framework for Black futurist worldbuilding. This framework culminates in WILDWOOD, an afro-solarpunk interactive digital narrative that visions a rewilded future for one Southside Chicago neighborhood. WILDWOOD incorporates elements of tabletop roleplay game storytelling and interactive digital narrative methods, using hypertext fiction and chance as digital worldbuilding tools. The game explores community histories and futures through Black speculative placemaking, offering tools to imagine new visions of Black worlds and space to enact through immersive digital narrative play.

关键词： Afro-solarpunk Black digital humanities Black speculative Chicago Creative placemaking Digital media Greater Englewood Hypertext fiction Interactive narrative Narrative change Social justice Southside Chicago Tabletop role play gaming Twine Twine writing Worldbuilding

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AmGNN: A Framework for Adaptive Processing of Inter-layer Information in Multi-layer Graph 16th

AmGNN: A Framework for Adaptive Processing of Inter-layer In...

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16th International Conference on Social Networks Analysis and Mining

作者： Zhu, Huaisheng Wu, Zongyu Zhao, Tianxiang Wang, Suhang Penn State Univ University Pk PA 16802 USA

ISBN: (纸本)9783031785405;9783031785412

Graphs play a vital role in various applications. Graph Neural Networks (GNNs) excel at capturing topology information by using a message-passing mechanism to enrich node representations with local neighborhood information. Despite their success in modeling single-layer graphs, real-world scenarios often involve multi-layer graphs where nodes can have multiple edges or relationships represented as different layers. Existing methods of multi-layer graph learning struggle to efficiently process inter-layer information, as they mainly focus on preserving similar layers or shared invariant information, which may not be suitable for all situations. We propose a novel framework called Adaptive Multi-layer Graph Neural Networks (AmGNN) to address this challenge. AmGNN learns shared invariant information for nodes that need it and selectively preserves relevant layers' information for nodes not requiring shared invariance. We introduce multi-layer graph contrastive learning to efficiently capture invariant information and learn weights for adaptive processing. Our experiments on real-world multi-layer graphs validate the effectiveness of AmGNN in node classification tasks.

关键词： Multi-layer Graph Node Classification Graph Neural Networks

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Unraveling the Italian and English Telegram Conspiracy Spheres Through Message Forwarding 16th

Unraveling the Italian and English Telegram Conspiracy Spher...

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16th International Conference on Social Networks Analysis and Mining

作者： Alvisi, Lorenzo Tardelli, Serena Tesconi, Maurizio IMT Sch Adv Studies Lucca Italy CNR Inst Informat & Telemat Pisa Italy

ISBN: (纸本)9783031785375;9783031785382

Telegram has grown into a significant platform for news and information sharing, favored for its anonymity and minimal moderation. This openness, however, makes it vulnerable to misinformation and conspiracy theories. In this study, we explore the dynamics of conspiratorial narrative dissemination within Telegram, focusing on Italian and English landscapes. In particular, we leverage the mechanism of message forwarding within Telegram and collect two extensive datasets through snowball strategy. We adopt a network-based approach and build the Italian and English Telegram networks to reveal their respective communities. By employing topic modeling, we uncover distinct narratives and dynamics of misinformation spread. Results highlight differences between Italian and English conspiracy landscapes, with Italian discourse involving assorted conspiracy theories and alternative news sources intertwined with legitimate news sources, whereas English discourse is characterized by a more focused approach on specific narratives. Finally, we show that our methodology exhibits robustness across initial seed selections, suggesting broader applicability. This study contributes to understanding information and misinformation spread on Italian and English Telegram ecosystems through the mechanism of message forwarding.

关键词： Telegram Message forwarding Linked chats Conspiracy Network Communities

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SocFedGPT: Federated GPT-Based Adaptive Content Filtering System Leveraging User Interactions in Social Networks 16th

SocFedGPT: Federated GPT-Based Adaptive Content Filtering Sy...

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16th International Conference on Social Networks Analysis and Mining

作者： Puppala, Sai Hossain, Ismail Alam, Md Jahangir Talukder, Sajedul Southern Illinois Univ Carbondale IL 62901 USA Univ Texas El Paso El Paso TX 79968 USA

ISBN: (纸本)9783031785474;9783031785481

Our study presents a multifaceted approach to enhancing user interaction and content relevance in social media platforms through a federated learning framework. We introduce personalized GPT and Context-based Social Media LLM models, utilizing federated learning for privacy and security. Four client entities receive a base GPT-2 model and locally collected social media data, with federated aggregation ensuring up-to-date model maintenance. Subsequent modules focus on categorizing user posts, computing user persona scores, and identifying relevant posts from friends' lists. A quantifying social engagement approach, coupled with matrix factorization techniques, facilitates personalized content suggestions in real time. Additionally, an adaptive feedback loop and readability score algorithm enhance the quality and relevance of content presented to users. Our system offers a comprehensive solution to content filtering and recommendation, fostering a tailored and engaging social media experience while safeguarding user privacy.

关键词： content filtering GPT content diversity LLM online social network

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