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Inside the echo chamber: Linguistic underpinnings of misinformation on Twitter arXiv.cs.SI Pub Date : 2024-04-24 Xinyu Wang, Jiayi Li, Sarah Rajtmajer
Social media users drive the spread of misinformation online by sharing posts that include erroneous information or commenting on controversial topics with unsubstantiated arguments often in earnest. Work on echo chambers has suggested that users' perspectives are reinforced through repeated interactions with like-minded peers, promoted by homophily and bias in information diffusion. Building on long-standing
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Are There Echo Chambers in the US News Ecosystem? Evidence From Twitter/X arXiv.cs.SI Pub Date : 2024-04-24 Wen Yang
This study investigates echo chambers in social networks through an analysis of Twitter news accounts. Utilizing bias labels from the AllSides website, we construct a dataset representing six dimensions of news bias. Through manual extraction of follower/following relationships, we analyze interactions among 65 active Twitter news accounts. Despite the relatively small size of the network node data
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DyGCL: Dynamic Graph Contrastive Learning For Event Prediction arXiv.cs.SI Pub Date : 2024-04-24 Muhammed Ifte Khairul Islam, Khaled Mohammed Saifuddin, Tanvir Hossain, Esra Akbas
Predicting events such as political protests, flu epidemics, and criminal activities is crucial to proactively taking necessary measures and implementing required responses to address emerging challenges. Capturing contextual information from textual data for event forecasting poses significant challenges due to the intricate structure of the documents and the evolving nature of events. Recently, dynamic
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SMI-5: Five Dimensions of Social Media Interaction for Platform (De)Centralization arXiv.cs.SI Pub Date : 2024-04-23 Lynnette Hui Xian Ng, Samantha C. Phillips, Kathleen M. Carley
Web 3.0 focuses on the decentralization of the internet and creating a system of interconnected and independent computers for improved privacy and security. We extend the idea of the decentralization of the web to the social media space: whereby we ask: in the context of the social media space, what does "decentralization" mean? Does decentralization of social media affect user interactions? We put
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Hidden in Plain Sight: Exploring the Intersections of Mental Health, Eating Disorders, and Content Moderation on TikTok arXiv.cs.SI Pub Date : 2024-04-23 Charles Bickham, Kia Kazemi-Nia, Luca Luceri, Kristina Lerman, Emilio Ferrara
Social media platforms actively moderate content glorifying harmful behaviors like eating disorders, which include anorexia and bulimia. However, users have adapted to evade moderation by using coded hashtags. Our study investigates the prevalence of moderation evaders on the popular social media platform TikTok and contrasts their use and emotional valence with mainstream hashtags. We notice that
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Dynamicity-aware Social Bot Detection with Dynamic Graph Transformers arXiv.cs.SI Pub Date : 2024-04-23 Buyun He, Yingguang Yang, Qi Wu, Hao Liu, Renyu Yang, Hao Peng, Xiang Wang, Yong Liao, Pengyuan Zhou
Detecting social bots has evolved into a pivotal yet intricate task, aimed at combating the dissemination of misinformation and preserving the authenticity of online interactions. While earlier graph-based approaches, which leverage topological structure of social networks, yielded notable outcomes, they overlooked the inherent dynamicity of social networks -- In reality, they largely depicted the
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Social Media and Artificial Intelligence for Sustainable Cities and Societies: A Water Quality Analysis Use-case arXiv.cs.SI Pub Date : 2024-04-23 Muhammad Asif Auyb, Muhammad Tayyab Zamir, Imran Khan, Hannia Naseem, Nasir Ahmad, Kashif Ahmad
This paper focuses on a very important societal challenge of water quality analysis. Being one of the key factors in the economic and social development of society, the provision of water and ensuring its quality has always remained one of the top priorities of public authorities. To ensure the quality of water, different methods for monitoring and assessing the water networks, such as offline and
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Source Localization for Cross Network Information Diffusion arXiv.cs.SI Pub Date : 2024-04-23 Chen Ling, Tanmoy Chowdhury, Jie Ji, Sirui Li, Andreas Züfle, Liang Zhao
Source localization aims to locate information diffusion sources only given the diffusion observation, which has attracted extensive attention in the past few years. Existing methods are mostly tailored for single networks and may not be generalized to handle more complex networks like cross-networks. Cross-network is defined as two interconnected networks, where one network's functionality depends
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What Makes A Video Radicalizing? Identifying Sources of Influence in QAnon Videos arXiv.cs.SI Pub Date : 2024-04-22 Lin Ai, Yu-Wen Chen, Yuwen Yu, Seoyoung Kweon, Julia Hirschberg, Sarah Ita Levitan
In recent years, radicalization is being increasingly attempted on video-sharing platforms. Previous studies have been proposed to identify online radicalization using generic social context analysis, without taking into account comprehensive viewer traits and how those can affect viewers' perception of radicalizing content. To address the challenge, we examine QAnon, a conspiracy-based radicalizing
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Graph Machine Learning in the Era of Large Language Models (LLMs) arXiv.cs.SI Pub Date : 2024-04-23 Wenqi Fan, Shijie Wang, Jiani Huang, Zhikai Chen, Yu Song, Wenzhuo Tang, Haitao Mao, Hui Liu, Xiaorui Liu, Dawei Yin, Qing Li
Graphs play an important role in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. With the advent of deep learning, Graph Neural Networks (GNNs) have emerged as a cornerstone in Graph Machine Learning (Graph ML), facilitating the representation and processing of graph structures. Recently, LLMs have demonstrated unprecedented capabilities
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Teaching Network Traffic Matrices in an Interactive Game Environment arXiv.cs.SI Pub Date : 2024-04-23 Chasen Milner, Hayden Jananthan, Jeremy Kepner, Vijay Gadepally, Michael Jones, Peter Michaleas, Ritesh Patel, Sandeep Pisharody, Gabriel Wachman, Alex Pentland
The Internet has become a critical domain for modern society that requires ongoing efforts for its improvement and protection. Network traffic matrices are a powerful tool for understanding and analyzing networks and are broadly taught in online graph theory educational resources. Network traffic matrix concepts are rarely available in online computer network and cybersecurity educational resources
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A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation arXiv.cs.SI Pub Date : 2024-04-22 Qikai Yang, Panfeng Li, Xinyu Shen, Zhicheng Ding, Wenjing Zhou, Yi Nian, Xinhe Xu
In the ever-evolving landscape of social network advertising, the volume and accuracy of data play a critical role in the performance of predictive models. However, the development of robust predictive algorithms is often hampered by the limited size and potential bias present in real-world datasets. This study presents and explores a generative augmentation framework of social network advertising
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Unsupervised Social Bot Detection via Structural Information Theory arXiv.cs.SI Pub Date : 2024-04-21 Hao Peng, Jingyun Zhang, Xiang Huang, Zhifeng Hao, Angsheng Li, Zhengtao Yu, Philip S. Yu
Research on social bot detection plays a crucial role in maintaining the order and reliability of information dissemination while increasing trust in social interactions. The current mainstream social bot detection models rely on black-box neural network technology, e.g., Graph Neural Network, Transformer, etc., which lacks interpretability. In this work, we present UnDBot, a novel unsupervised, interpretable
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Demystify Adult Learning: A Social Network and Large Language Model Assisted Approach arXiv.cs.SI Pub Date : 2024-04-20 Fang Liu, Bosheng Ding, Chong Guan, Zhang Wei, Dusit Niyato, Justina Tan
Adult learning is increasingly recognized as a crucial way for personal development and societal progress. It however is challenging, and adult learners face unique challenges such as balancing education with other life responsibilities. Collecting feedback from adult learners is effective in understanding their concerns and improving learning experiences, and social networks provide a rich source
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Bubble reachers and uncivil discourse in polarized online public sphere arXiv.cs.SI Pub Date : 2024-04-19 Jordan K Kobellarz, Milos Brocic, Daniel Silver, Thiago H Silva
Early optimism saw possibilities for social media to renew democratic discourse, marked by hopes for individuals from diverse backgrounds to find opportunities to learn from and interact with others different from themselves. This optimism quickly waned as social media seemed to breed ideological homophily marked by "filter bubble" or "echo chambers." A typical response to the sense of fragmentation
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Crowdsourcing public attitudes toward local services through the lens of Google Maps reviews: An urban density-based perspective arXiv.cs.SI Pub Date : 2024-04-19 Lingyao Li, Songhua Hu, Atiyya Shaw, Libby Hemphill
Understanding how urban density impacts public perceptions of urban service is important for informing livable, accessible, and equitable urban planning. Conventional methods such as surveys are limited by their sampling scope, time efficiency, and expense. On the other hand, crowdsourcing through online platforms presents an opportunity for decision-makers to tap into a user-generated source of information
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AI-Generated Faces in the Real World: A Large-Scale Case Study of Twitter Profile Images arXiv.cs.SI Pub Date : 2024-04-22 Jonas Ricker, Dennis Assenmacher, Thorsten Holz, Asja Fischer, Erwin Quiring
Recent advances in the field of generative artificial intelligence (AI) have blurred the lines between authentic and machine-generated content, making it almost impossible for humans to distinguish between such media. One notable consequence is the use of AI-generated images for fake profiles on social media. While several types of disinformation campaigns and similar incidents have been reported in
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FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning arXiv.cs.SI Pub Date : 2024-04-22 Yinlin Zhu, Xunkai Li, Zhengyu Wu, Di Wu, Miao Hu, Rong-Hua Li
Subgraph federated learning (subgraph-FL) is a new distributed paradigm that facilitates the collaborative training of graph neural networks (GNNs) by multi-client subgraphs. Unfortunately, a significant challenge of subgraph-FL arises from subgraph heterogeneity, which stems from node and topology variation, causing the impaired performance of the global GNN. Despite various studies, they have not
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A Grassroots Architecture to Supplant Global Digital Platforms by a Global Digital Democracy arXiv.cs.SI Pub Date : 2024-04-20 Ehud Shapiro
We present an architectural alternative to global digital platforms termed grassroots, designed to serve the social, economic, civic, and political needs of local digital communities, as well as their federation. Grassroots platforms may offer local communities an alternative to global digital platforms while operating solely on the smartphones of their members, forsaking any global resources other
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Private Agent-Based Modeling arXiv.cs.SI Pub Date : 2024-04-19 Ayush Chopra, Arnau Quera-Bofarull, Nurullah Giray-Kuru, Michael Wooldridge, Ramesh Raskar
The practical utility of agent-based models in decision-making relies on their capacity to accurately replicate populations while seamlessly integrating real-world data streams. Yet, the incorporation of such data poses significant challenges due to privacy concerns. To address this issue, we introduce a paradigm for private agent-based modeling wherein the simulation, calibration, and analysis of
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Stance Detection on Social Media with Fine-Tuned Large Language Models arXiv.cs.SI Pub Date : 2024-04-18 İlker Gül, Rémi Lebret, Karl Aberer
Stance detection, a key task in natural language processing, determines an author's viewpoint based on textual analysis. This study evaluates the evolution of stance detection methods, transitioning from early machine learning approaches to the groundbreaking BERT model, and eventually to modern Large Language Models (LLMs) such as ChatGPT, LLaMa-2, and Mistral-7B. While ChatGPT's closed-source nature
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Effective Individual Fairest Community Search over Heterogeneous Information Networks arXiv.cs.SI Pub Date : 2024-04-18 Taige Zhao, Jianxin Li, Ningning Cui, Wei Luo
Community search over heterogeneous information networks has been applied to wide domains, such as activity organization and team formation. From these scenarios, the members of a group with the same treatment often have different levels of activity and workloads, which causes unfairness in the treatment between active members and inactive members (called individual unfairness). However, existing works
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Multi-view Graph Structural Representation Learning via Graph Coarsening arXiv.cs.SI Pub Date : 2024-04-18 Xiaorui Qi, Qijie Bai, Yanlong Wen, Haiwei Zhang, Xiaojie Yuan
Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lacks explicit representations of edges and structures. One natural question is, can we treat graph structures node-like as a whole to learn high-level features
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X-posing Free Speech: Examining the Impact of Moderation Relaxation on Online Social Networks arXiv.cs.SI Pub Date : 2024-04-17 Arvindh Arun, Saurav Chhatani, Jisun An, Ponnurangam Kumaraguru
We investigate the impact of free speech and the relaxation of moderation on online social media platforms using Elon Musk's takeover of Twitter as a case study. By curating a dataset of over 10 million tweets, our study employs a novel framework combining content and network analysis. Our findings reveal a significant increase in the distribution of certain forms of hate content, particularly targeting
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Complex hypergraph analysis of Australian MPs' professional connections, 1947-2019 arXiv.cs.SI Pub Date : 2024-04-17 Eve Cheng, Danny Cocks, Patrick Leslie
We propose a suit of methods to analyse the professional networks of MPs, showing how to analyse weak-tie connections between legislators and the connections between background charactersitic attributes. Applied to a novel dataset on the backgrounds of Australian MPs in the Australian Labor Party and the Liberal Party of Australia (1947-2019), we show that our approach can help to describe and explain
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The Future of Research on Social Technologies: CCC Workshop Visioning Report arXiv.cs.SI Pub Date : 2024-04-16 Motahhare Eslami, Eric Gilbert, Sarita Schoenebeck, Eric P. S. Baumer, Eshwar Chandrasekharan, Michelle De Mooy, Karrie Karahalios, David Karger, Tressie McMillan Cottom, Andrés Monroy-Hernández, Loren Terveen, John Wihbey
Social technologies are the systems, interfaces, features, infrastructures, and architectures that allow people to interact with each other online. These technologies dramatically shape the fabric of our everyday lives, from the information we consume to the people we interact with to the foundations of our culture and politics. While the benefits of social technologies are well documented, the harms
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Intelligent Message Behavioral Identification System arXiv.cs.SI Pub Date : 2024-04-14 Yuvaraju Chinnam, Bosubabu Sambana
On social media platforms, the act of predicting reposting is seen as a challenging issue related to Short Message Services (SMS). This study examines the issue of predicting picture reposting in SMS and forecasts users' behavior in sharing photographs on Twitter. Several research vary. The paper introduces a network called Image Retweet Modeling (IRM) that models heterogeneous image retransmission
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CORE: Data Augmentation for Link Prediction via Information Bottleneck arXiv.cs.SI Pub Date : 2024-04-17 Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often compromised due to the presence of noisy or spurious information in graphs and the inherent incompleteness of graph data. To address these challenges, we draw inspiration from the Information Bottleneck principle and propose
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Monitoring Critical Infrastructure Facilities During Disasters Using Large Language Models arXiv.cs.SI Pub Date : 2024-04-18 Abdul Wahab Ziaullah, Ferda Ofli, Muhammad Imran
Critical Infrastructure Facilities (CIFs), such as healthcare and transportation facilities, are vital for the functioning of a community, especially during large-scale emergencies. In this paper, we explore a potential application of Large Language Models (LLMs) to monitor the status of CIFs affected by natural disasters through information disseminated in social media networks. To this end, we analyze
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Community detection and anomaly prediction in dynamic networks arXiv.cs.SI Pub Date : 2024-04-16 Hadiseh Safdari, Caterina De Bacco
Anomaly detection is an essential task in the analysis of dynamic networks, as it can provide early warning of potential threats or abnormal behavior. We present a principled approach to detect anomalies in dynamic networks that integrates community structure as a foundational model for regular behavior. Our model identifies anomalies as irregular edges while capturing structural changes. Leveraging
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Stampede Alert Clustering Algorithmic System Based on Tiny-Scale Strengthened DETR arXiv.cs.SI Pub Date : 2024-04-16 Mingze Sun, Yiqing Wang, Zhenyi Zhao
A novel crowd stampede detection and prediction algorithm based on Deformable DETR is proposed to address the challenges of detecting a large number of small targets and target occlusion in crowded airport and train station environments. In terms of model design, the algorithm incorporates a multi-scale feature fusion module to enlarge the receptive field and enhance the detection capability of small
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Cross-Language Evolution of Divergent Collective Memory Around the Arab Spring arXiv.cs.SI Pub Date : 2024-04-16 H. Laurie Jones, Brian C. Keegan
The Arab Spring was a historic set of protests beginning in 2011 that toppled governments and led to major conflicts. Collective memories of events like these can vary significantly across social contexts in response to political, cultural, and linguistic factors. While Wikipedia plays an important role in documenting both historic and current events, little attention has been given to how Wikipedia
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Rethinking the Graph Polynomial Filter via Positive and Negative Coupling Analysis arXiv.cs.SI Pub Date : 2024-04-16 Haodong Wen, Bodong Du, Ruixun Liu, Deyu Meng, Xiangyong Cao
Recently, the optimization of polynomial filters within Spectral Graph Neural Networks (GNNs) has emerged as a prominent research focus. Existing spectral GNNs mainly emphasize polynomial properties in filter design, introducing computational overhead and neglecting the integration of crucial graph structure information. We argue that incorporating graph information into basis construction can enhance
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Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy arXiv.cs.SI Pub Date : 2024-04-16 Tunazzina Islam, Dan Goldwasser
The widespread use of social media has led to a surge in popularity for automated methods of analyzing public opinion. Supervised methods are adept at text categorization, yet the dynamic nature of social media discussions poses a continual challenge for these techniques due to the constant shifting of the focus. On the other hand, traditional unsupervised methods for extracting themes from public
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Two-Stage Stance Labeling: User-Hashtag Heuristics with Graph Neural Networks arXiv.cs.SI Pub Date : 2024-04-16 Joshua Melton, Shannon Reid, Gabriel Terejanu, Siddharth Krishnan
The high volume and rapid evolution of content on social media present major challenges for studying the stance of social media users. In this work, we develop a two stage stance labeling method that utilizes the user-hashtag bipartite graph and the user-user interaction graph. In the first stage, a simple and efficient heuristic for stance labeling uses the user-hashtag bipartite graph to iteratively
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Survey on Embedding Models for Knowledge Graph and its Applications arXiv.cs.SI Pub Date : 2024-04-14 Manita Pote
Knowledge Graph (KG) is a graph based data structure to represent facts of the world where nodes represent real world entities or abstract concept and edges represent relation between the entities. Graph as representation for knowledge has several drawbacks like data sparsity, computational complexity and manual feature engineering. Knowledge Graph embedding tackles the drawback by representing entities
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Beam Management in Low Earth Orbit Satellite Communication With Handover Frequency Control and Satellite-Terrestrial Spectrum Sharing arXiv.cs.SI Pub Date : 2024-04-13 Yaohua Sun, Jianfeng Zhu, Mugen Peng
To achieve ubiquitous wireless connectivity, low earth orbit (LEO) satellite networks have drawn much attention. However, effective beam management is challenging due to time-varying cell load, high dynamic network topology, and complex interference situations. In this paper, under inter-satellite handover frequency and satellite-terrestrial/inter-beam interference constraints, we formulate a practical
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Timing Advance Estimation in Low Earth Orbit Satellite Networks arXiv.cs.SI Pub Date : 2024-04-13 Jianfeng Zhu, Yaohua Sun, Mugen Peng
Low earth orbit (LEO) satellite communication based on 3GPP standard is seen as a promising solution to rolling out communication services in areas without terrestrial base stations. However, due to the fast movement of satellites and large beam footprint size, the existing 5G timing advance (TA) estimation mechanism cannot be directly applied when global navigation satellite system is unavailable
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Beam Management in Low Earth Orbit Satellite Networks with Random Traffic Arrival and Time-varying Topology arXiv.cs.SI Pub Date : 2024-04-13 Jianfeng Zhu, Yaohua Sun, Mugen Peng
Low earth orbit (LEO) satellite communication networks have been considered as promising solutions to providing high data rate and seamless coverage, where satellite beam management plays a key role. However, due to the limitation of beam resource, dynamic network topology, beam spectrum reuse, time-varying traffic arrival and service continuity requirement, it is challenging to effectively allocate
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Misinformation Resilient Search Rankings with Webgraph-based Interventions arXiv.cs.SI Pub Date : 2024-04-13 Peter Carragher, Evan M. Williams, Kathleen M. Carley
The proliferation of unreliable news domains on the internet has had wide-reaching negative impacts on society. We introduce and evaluate interventions aimed at reducing traffic to unreliable news domains from search engines while maintaining traffic to reliable domains. We build these interventions on the principles of fairness (penalize sites for what is in their control), generality (label/fact-check
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BOND: Bootstrapping From-Scratch Name Disambiguation with Multi-task Promoting arXiv.cs.SI Pub Date : 2024-04-12 Yuqing Cheng, Bo Chen, Fanjin Zhang, Jie Tang
From-scratch name disambiguation is an essential task for establishing a reliable foundation for academic platforms. It involves partitioning documents authored by identically named individuals into groups representing distinct real-life experts. Canonically, the process is divided into two decoupled tasks: locally estimating the pairwise similarities between documents followed by globally grouping
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Interest Maximization in Social Networks arXiv.cs.SI Pub Date : 2024-04-12 Rahul Kumar Gautam, Anjeneya Swami Kare, S. Durga Bhavani
Nowadays, organizations use viral marketing strategies to promote their products through social networks. It is expensive to directly send the product promotional information to all the users in the network. In this context, Kempe et al. \cite{kempe2003maximizing} introduced the Influence Maximization (IM) problem, which identifies $k$ most influential nodes (spreader nodes), such that the maximum
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Efficient GPU Implementation of Static and Incrementally Expanding DF-P PageRank for Dynamic Graphs arXiv.cs.SI Pub Date : 2024-04-12 Subhajit Sahu
PageRank is a widely used centrality measure that "ranks" vertices in a graph by considering the connections and their importance. In this report, we first introduce one of the most efficient GPU implementations of Static PageRank, which recomputes PageRank scores from scratch. It uses a synchronous pull-based atomics-free PageRank computation, with the low and high in-degree vertices being partitioned
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Relational Prompt-based Pre-trained Language Models for Social Event Detection arXiv.cs.SI Pub Date : 2024-04-12 Pu Li, Xiaoyan Yu, Hao Peng, Yantuan Xian, Linqin Wang, Li Sun, Jingyun Zhang, Philip S. Yu
Social Event Detection (SED) aims to identify significant events from social streams, and has a wide application ranging from public opinion analysis to risk management. In recent years, Graph Neural Network (GNN) based solutions have achieved state-of-the-art performance. However, GNN-based methods often struggle with noisy and missing edges between messages, affecting the quality of learned message
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Toxic Synergy Between Hate Speech and Fake News Exposure arXiv.cs.SI Pub Date : 2024-04-11 Munjung Kim, Tuğrulcan Elmas, Filippo Menczer
Hate speech on social media is a pressing concern. Understanding the factors associated with hate speech may help mitigate it. Here we explore the association between hate speech and exposure to fake news by studying the correlation between exposure to news from low-credibility sources through following connections and the use of hate speech on Twitter. Using news source credibility labels and a dataset
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Auditing health-related recommendations in social media: A Case Study of Abortion on YouTube arXiv.cs.SI Pub Date : 2024-04-11 Mohammed Lahsaini, Mohamed Lechiakh, Alexandre Maurer
Recommendation algorithms (RS) used by social media, like YouTube, significantly shape our information consumption across various domains, especially in healthcare. Hence, algorithmic auditing becomes crucial to uncover their potential bias and misinformation, particularly in the context of controversial topics like abortion. We introduce a simple yet effective sock puppet auditing approach to investigate
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Analyzing Toxicity in Deep Conversations: A Reddit Case Study arXiv.cs.SI Pub Date : 2024-04-11 Vigneshwaran Shankaran, Rajesh Sharma
Online social media has become increasingly popular in recent years due to its ease of access and ability to connect with others. One of social media's main draws is its anonymity, allowing users to share their thoughts and opinions without fear of judgment or retribution. This anonymity has also made social media prone to harmful content, which requires moderation to ensure responsible and productive
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Illicit Promotion on Twitter arXiv.cs.SI Pub Date : 2024-04-11 Hongyu Wang, Ying Li, Ronghong Huang, Xianghang Mi
In this paper, we present an extensive study of the promotion of illicit goods and services on Twitter, a popular online social network(OSN). This study is made possible through the design and implementation of multiple novel tools for detecting and analyzing illicit promotion activities as well as their underlying campaigns. As the results, we observe that illicit promotion is prevalent on Twitter
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Mitigating Cascading Effects in Large Adversarial Graph Environments arXiv.cs.SI Pub Date : 2024-04-12 James D. Cunningham, Conrad S. Tucker
A significant amount of society's infrastructure can be modeled using graph structures, from electric and communication grids, to traffic networks, to social networks. Each of these domains are also susceptible to the cascading spread of negative impacts, whether this be overloaded devices in the power grid or the reach of a social media post containing misinformation. The potential harm of a cascade
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Embedding Economic Incentives in Social Networks Shape the Diffusion of Digital Technological Innovation arXiv.cs.SI Pub Date : 2024-04-10 Zhe Li, Tianfang Zhao, Hongjun Zhu
The digital innovation accompanied by explicit economic incentives have fundamentally changed the process of innovation diffusion. As a representative of digital innovation, NFTs provide a decentralized and secure way to authenticate and trade digital assets, offering the potential for new revenue streams in the digital space. However, current researches about NFTs mainly focus on their transaction
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Echo Chambers in the Age of Algorithms: An Audit of Twitter's Friend Recommender System arXiv.cs.SI Pub Date : 2024-04-09 Kayla Duskin, Joseph S. Schafer, Jevin D. West, Emma S. Spiro
The presence of political misinformation and ideological echo chambers on social media platforms is concerning given the important role that these sites play in the public's exposure to news and current events. Algorithmic systems employed on these platforms are presumed to play a role in these phenomena, but little is known about their mechanisms and effects. In this work, we conduct an algorithmic
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Modeling the Dynamic Process of Inventions for Reducing Knowledge Search Costs arXiv.cs.SI Pub Date : 2024-04-08 Haiying Ren, Yuanyuan Song, Rui Peng
A knowledge search is a key process for inventions. However, there is inadequate quantitative modeling of dynamic knowledge search processes and associated search costs. In this study, agent-based and complex network methodologies were proposed to quantitatively describe the dynamic process of knowledge search for actual inventions. Prior knowledge networks (PKNs), the search space of historical patents
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Computational Propaganda Theory and Bot Detection System: Critical Literature Review arXiv.cs.SI Pub Date : 2024-04-08 Manita Pote
According to the classical definition, propaganda is the management of collective attitudes by manipulation of significant symbols. However this definition has changed to computational propaganda, the way manipulation takes place in digital medium. Computational propaganda is the use of algorithms, automation and human curation to purposefully distribute misleading information over social media networks
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Graph Vertex Embeddings: Distance, Regularization and Community Detection arXiv.cs.SI Pub Date : 2024-04-09 Radosław Nowak, Adam Małkowski, Daniel Cieślak, Piotr Sokół, Paweł Wawrzyński
Graph embeddings have emerged as a powerful tool for representing complex network structures in a low-dimensional space, enabling the use of efficient methods that employ the metric structure in the embedding space as a proxy for the topological structure of the data. In this paper, we explore several aspects that affect the quality of a vertex embedding of graph-structured data. To this effect, we
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Model Selection with Model Zoo via Graph Learning arXiv.cs.SI Pub Date : 2024-04-05 Ziyu Li, Hilco van der Wilk, Danning Zhan, Megha Khosla, Alessandro Bozzon, Rihan Hai
Pre-trained deep learning (DL) models are increasingly accessible in public repositories, i.e., model zoos. Given a new prediction task, finding the best model to fine-tune can be computationally intensive and costly, especially when the number of pre-trained models is large. Selecting the right pre-trained models is crucial, yet complicated by the diversity of models from various model families (like
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BanglaAutoKG: Automatic Bangla Knowledge Graph Construction with Semantic Neural Graph Filtering arXiv.cs.SI Pub Date : 2024-04-04 Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Dong-Kyu Chae
Knowledge Graphs (KGs) have proven essential in information processing and reasoning applications because they link related entities and give context-rich information, supporting efficient information retrieval and knowledge discovery; presenting information flow in a very effective manner. Despite being widely used globally, Bangla is relatively underrepresented in KGs due to a lack of comprehensive
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Knowledge Graph Representation for Political Information Sources arXiv.cs.SI Pub Date : 2024-04-04 Tinatin Osmonova, Alexey Tikhonov, Ivan P. Yamshchikov
With the rise of computational social science, many scholars utilize data analysis and natural language processing tools to analyze social media, news articles, and other accessible data sources for examining political and social discourse. Particularly, the study of the emergence of echo-chambers due to the dissemination of specific information has become a topic of interest in mixed methods research
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Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks arXiv.cs.SI Pub Date : 2024-04-04 Arjun Subramonian, Jian Kang, Yizhou Sun
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree nodes on node classification tasks. This degree bias can reinforce social marginalization by, e.g., sidelining authors of lowly-cited papers when predicting paper topics in citation networks. While researchers have proposed numerous hypotheses for why GNN degree bias occurs, we find via a survey of 38 degree bias
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Modeling social interaction dynamics using temporal graph networks arXiv.cs.SI Pub Date : 2024-04-05 J. Taery Kim, Archit Naik, Isuru Jayarathne, Sehoon Ha, Jouh Yeong Chew
Integrating intelligent systems, such as robots, into dynamic group settings poses challenges due to the mutual influence of human behaviors and internal states. A robust representation of social interaction dynamics is essential for effective human-robot collaboration. Existing approaches often narrow their focus to facial expressions or speech, overlooking the broader context. We propose employing
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Why do people think liberals drink lattes? How social media afforded self-presentation can shape subjective social sorting arXiv.cs.SI Pub Date : 2024-04-02 Samantha C. Phillips, Kathleen M. Carley, Kenneth Joseph
Social sorting, the alignment of social identities, affiliations, and/or preferences with partisan groups, can increase in-party attachment and decrease out-party tolerance. We propose that self-presentation afforded by social media profiles fosters subjective social sorting by shaping perceptions of alignments between non-political and political identifiers. Unlike previous work, we evaluate social