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Weaponizing the Wall: The Role of Sponsored News in Spreading Propaganda on Facebook 16th

Weaponizing the Wall: The Role of Sponsored News in Spreadin...

引用

16th International Conference on Social Networks Analysis and Mining

作者： Singh, Daman Deep Chauhan, Gaurav Nguyen, Minh-Kha Goga, Oana Chakraborty, Abhijnan Indian Inst Technol Delhi New Delhi India Univ Grenoble Alpes Grenoble France Inst Polytech Paris CNRS INRIA Paris France Indian Inst Technol Kharagpur Kharagpur W Bengal India

ISBN: (纸本)9783031785405;9783031785412

A large fraction of people today consume most of their news online, and social media platforms like Facebook play a significant role in directing traffic to news articles. While news organizations often use Facebook advertising to drive traffic to their websites, this practice can inadvertently lead to biases in what articles users get exposed to, or worse, could be used as a mechanism for manipulation. In this work, we examine the impact of sponsored news on Facebook on the dissemination of propaganda. Propaganda is a method of persuasion that is frequently employed to advance some sort of goal, such as a personal, political, or business objective. By analyzing 17 Million+ Facebook posts and 6 Million sponsored advertisements gathered over 182 days, we observe that advertisers of all kinds, including politicians, media houses, and commercial corporations, publish thousands of ads/boosted posts every day on Facebook. However, Facebook does not include advertisements from news organizations in their public ad archive, even when these ads address political and social issues. This exemption places news organizations in a unique position where they can publish paid political opinions without any transparency requirements. The risk is that news organizations or other third-party interest groups can selectively promote news articles that support their agenda, giving these ads an appearance of legitimacy because they link to established news sites. In this paper, we explore how such sponsored news on Facebook can be a powerful tool for spreading propaganda. Through this work, we hope to raise awareness among users about the potential biases in sponsored news and the need to critically evaluate the information they see on Facebook.

关键词： Facebook Ads Propaganda Detection Sponsored News

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On Mining Dynamic Graphs for k Shortest Paths 16th

On Mining Dynamic Graphs for k Shortest Paths

引用

16th International Conference on Social Networks Analysis and Mining

作者： D'Ascenzo, Andrea D'Emidio, Mattia Luiss Univ Rome Italy Univ Aquila Laquila Italy

ISBN: (纸本)9783031785405;9783031785412

Mining graphs, upon query, for k shortest paths between vertex pairs is a prominent primitive to support several analytics tasks on complex networked datasets. The state-of-the-art method to implement this primitive is KPLL, a framework that provides very fast query answering, even for large inputs and volumes of queries, by pre-computing and exploiting an appropriate index of the graph. However, if the graph's topology undergoes changes over time, such index might become obsolete and thus yield incorrect query results. Re-building the index from scratch, upon every modification, induces unsustainable time overheads, incompatible with applications using k shortest paths for analytics purposes. Motivated by this limitation, in this paper, we introduce DECKPLL, the first dynamic algorithm to maintain a KPLL index under decremental modifications. We assess the effectiveness and scalability of our algorithm through extensive experimentation and show it updates KPLL indices orders of magnitude faster than the re-computation from scratch, while preserving its compactness and query performance. We also combine DECKPLL with INCKPLL, the only known dynamic algorithm to maintain a KPLL index under incremental modifications, and hence showcase, on real-world datasets, the first method to support fast extraction of k shortest paths from graphs that evolve by arbitrary topological changes.

关键词： Graph Algorithms Dynamic Networks Algorithm Engineering Experimental Algorithmics

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You Must Be a Trump Supporter: Political Identity Projections on the Social Web 16th

You Must Be a Trump Supporter: Political Identity Projection...

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16th International Conference on Social Networks Analysis and Mining

作者： Mittal, Shubh Chawla, Tisha KhudaBukhsh, Ashiqur R. Vellore Inst Technol Vellore 632014 Tamil Nadu India Rochester Inst Technol Rochester NY 14623 USA

ISBN: (纸本)9783031785405;9783031785412

This paper contains offensive content. This paper assesses the extent of political polarization in the United States by demonstrating the phenomenon of political identity projection, where individuals attribute political affiliations to others based on political discourse. This aspect of political behavior, often found in interactions between authors on various social media platforms, remains relatively unexplored. To address this gap, our research utilizes a comprehensive dataset of comments on YouTube news videos from three prominent US cable news networks (Fox News, CNN, and MSNBC) to interpret expressions of political polarization. First, we assess the accuracy of LLMs in identifying political identity projections, exploring the potential biases these models may incorporate. Second, we conduct a user engagement analysis that highlights interaction patterns and their implications for understanding political identity projections across different news outlets.

关键词： Political Identity Projections Large Language Models Social Web

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Emotional Believability of Non-playable Game Characters - Animations of Anger, Sadness and Happiness 17th

Emotional Believability of Non-playable Game Characters - An...

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17th International Conference on Interactive Digital Storytelling

作者： Johansson, Therese Eladhari, Mirjam Palosaari Stockholm Univ Stockholm Sweden

ISBN: (纸本)9783031784491;9783031784507

This study investigates the relationship between the perceived believability of NPC animations and their portrayal of three core emotional expressions: anger, sadness and happiness in well-known, recently released, character-focused games. This study is explorative aiming to identify subjects for further research. We studied tree games: Genshin Impact, Baldur's Gate 3 and Animal Crossing: New Horizons (ACNH). A content analysis was conducted to compare the animations with human emotional expressions to identify patterns. Based on this, a questionnaire with nine videos from the games was designed, to assess players' ability to recognize emotions, and rate their believability, garnering 159 responses. Our findings show that ACNH, which exaggerate its animations, achieved the highest believability score with 4.91 out of 5 and a 100% identification rate of anger. The study's findings supports previous research regarding exaggerated acting as one of the most effective ways to convey emotions. This study underscores the importance of emotional expression via animation in creating expressive, yet recognizable, animations.

关键词： Animation Emotions Believability Games NPC

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HyperSMOTE-MC: Enhancing Multiclass Bot Detection on X Through Hypergraph-Based Resampling 16th

HyperSMOTE-MC: Enhancing Multiclass Bot Detection on X Throu...

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16th International Conference on Social Networks Analysis and Mining

作者： AlKulaib, Lulwah Lu, Chang-Tien Sabah AlSalem Univ City Kuwait Univ Dept Comp Sci Kuwait Kuwait Virginia Tech Dept Comp Sci Falls Church VA 22043 USA

ISBN: (纸本)9783031785535;9783031785542

The complexity and variety of bot behaviors on social media platforms like X (formerly Twitter) demand advanced detection methods that can handle multiclass imbalances effectively. Existing binary classification methods often fall short in accurately identifying and distinguishing between various bot types and genuine users, leading to biased and incomplete detection. To address these challenges, we introduce HyperSMOTE-MC, a novel hypergraph-based approach specifically designed for multiclass bot detection. By constructing a hypergraph where users are represented as nodes and their interactions as hyper-edges, HyperSMOTE-MC captures the multifaceted relationships among users. This method employs synthetic minority oversampling to balance the dataset, ensuring fair representation of all bot classes. Additionally, HyperSMOTE-MC integrates a Hypergraph Convolutional Network (HGCN) to leverage these complex interactions for improved classification performance. Evaluated on the TwiBot-20 dataset, HyperSMOTE-MC demonstrates superior accuracy, precision, recall, F1 score, and AUC-ROC compared to baseline methods, showcasing its robustness and effectiveness in handling multiclass bot detection across various domains.

关键词： hypergraph hypergraph learning bot detection class imbalance node classification

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PRAGyan - Connecting the Dots in Tweets 16th

PRAGyan - Connecting the Dots in Tweets

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16th International Conference on Social Networks Analysis and Mining

作者： Ravi, Rahul Ginde, Gouri Rokne, Jon Univ Calgary Calgary AB T2N 1N4 Canada

ISBN: (纸本)9783031785474;9783031785481

Understanding the underlying reasons behind events and statements is crucial for businesses, policymakers, and researchers. In this research the data available from social media platform is analyzed, aiming to extract these reasons. An integration of Knowledge Graphs (KGs) with Large Language Models (LLMs) is used to perform causal analysis of tweets dataset. The LLM aided analysis techniques often lack depth in uncovering the causes driving observed effects. By leveraging KGs and LLMs, which encode rich semantic relationships and temporal information, this study aims to uncover the complex interplay of factors influencing causal dynamics and compare the results obtained using GPT-3.5 Turbo. We employ a Retrieval-Augmented Generation (RAG) model, utilizing a KG stored in a Neo4j (a.k.a PRAGyan) data format, to retrieve relevant context for causal reasoning. Our approach demonstrates that the KG-enhanced LLM RAG can provide improved results when compared to the baseline LLM (GPT-3.5 Turbo) model as the source corpus increases in size. Our qualitative analysis highlights the advantages of combining KGs with LLMs for improved interpretability and actionable insights, facilitating informed decision-making across various domains. Whereas, quantitative analysis using metrics such as BLEU and cosine similarity show that our approach outperforms the baseline by 10%.

关键词： Knowledge Graphs (KG) Natural Language Processing (NLP) Causal Analysis Large Language Models (LLM) Relation Extraction Retrieval-Augmented Generation (RAG) Embeddings

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Balancing Efficiency and Quality in LLM-Based Entity Resolution on Structured Data 16th

Balancing Efficiency and Quality in LLM-Based Entity Resolut...

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16th International Conference on Social Networks Analysis and Mining

作者： Nananukul, Navapat Kekriwal, Mayank Univ Southern Calif Inst Informat Sci Los Angeles CA 90292 USA

ISBN: (纸本)9783031785474;9783031785481

Entity Resolution (ER) is the problem of automatically determining when two or more entities refer to the same underlying entity. ER has been researched for over fifty years across multiple domains (including healthcare, e-commerce, and census data). In graph-based applications, such as deduplicating identities across (or even within) social media platforms, as well as knowledge graphs, ER can be particularly important. Traditionally, ER was a difficult problem both within Artificial Intelligence (AI) and in databases, owing to the quadratic O(n(2)) complexity of comparing n entities to each other, given one or more graphs with n total nodes. However, recent emergence of large language models (LLMs) allow us to address the challenges of ER as an AI problem, but a clear framework for applying LLMs in a cost-effective way remains an open issue. In this paper, we present such a framework and validate it through early experiments on real-world ER benchmarks. The framework is LLM-agnostic and is premised on assumptions that resemble pragmatic real-world requirements.

关键词： Entity resolution identity matching knowledge graphs blocking efficiency large language models

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Shoal: Improving DAG-BFT Latency and Robustness 28th

Shoal: Improving DAG-BFT Latency and Robustness

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28th International Conference on Financial Cryptography and Data Security

作者： Spiegelman, Alexander Arun, Balaji Gelashvili, Rati Li, Zekun Aptos Labs Palo Alto CA 94301 USA

ISBN: (纸本)9783031786754;9783031786761

The Narwhal system is a state-of-the-art Byzantine fault-tolerant (BFT) scalable architecture that involves constructing a directed acyclic graph (DAG) of messages among a set of validators in a Blockchain network. Bullshark is a zero-overhead consensus protocol on top of the Narwhal's DAG that can order over 100k transactions per second. Unfortunately, the high throughput of Bullshark comes with a latency price due to the DAG construction, increasing the latency compared to the state-of-the-art leader-based BFT consensus protocols. We introduce Shoal, a protocol-agnostic framework for enhancing Narwhal-based consensus. By incorporating leader reputation and pipelining support for the first time in DAG-BFT, Shoal significantly reduces latency. Moreover, the combination of properties of the DAG construction and the leader reputation mechanism enables the elimination of timeouts in all but extremely uncommon scenarios in practice, a property we name "prevalent responsiveness" (it strictly subsumes the established and often desired "optimistic responsiveness" property for BFT protocols). We integrated Shoal instantiated with Bullshark in an open-source Blockchain project and provide experimental evaluations demonstrating up to 40% latency reduction in the failure-free executions, and up-to 80% reduction in executions with failures against the vanilla Bullshark implementation.

关键词： Directed graphs

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FairNet: A Genetic Framework to Reduce Marginalization in Social Networks 16th

FairNet: A Genetic Framework to Reduce Marginalization in So...

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16th International Conference on Social Networks Analysis and Mining

作者： Mazzoni, Federico Failla, Andrea Rossetti, Giulio Univ Pisa Dept Comp Sci I-56127 Pisa Italy CNR ISTI Natl Res Council I-56127 Pisa Italy

ISBN: (纸本)9783031785405;9783031785412

Discrimination in social networks often assumes the form of marginalization against nodes with specific features, e.g., segregation of/against minorities. In this work, we propose a metric that proxies social discrimination based on salient node features in a social network. Under the assumption that in a fair social system, all individuals should be enclosed in similar social circles representing the network in its entirety, our metric assigns a marginalization score to each node in the network, identifying if they are marginalized by similar nodes (e.g., a man marginalized by other men), by different nodes (e.g., a man marginalized by women), or not marginalized at all (i.e., the node has a fair neighborhood). Moreover, we introduce FAIRNET, a two-fold framework that aims to reduce network marginalization in partially- and fully-attributed networks by employing genetic algorithms. We evaluate our framework on networks emerging from online social interactions and find that the two components of FAIRNET are able to consistently reduce marginalization.

关键词： Fairness Genetic Algorithm Marginalization Bias Mitigation

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Towards Practical Doubly-Efficient Private Information Retrieval 28th

Towards Practical Doubly-Efficient Private Information Retri...

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28th International Conference on Financial Cryptography and Data Security

作者： Okada, Hiroki Player, Rachel Pohmann, Simon Weinert, Christian Royal Holloway Univ London London England KDDI Res Fujimino Japan Univ Tokyo Bunkyo Japan

ISBN: (纸本)9783031786785;9783031786792

Private information retrieval (PIR) protocols allow clients to access database entries without revealing the queried indices. They have many real-world applications, including privately querying patent-, compromised credential-, and contact databases. While existing PIR protocols that have been implemented perform reasonably well in practice, they all have suboptimal asymptotic complexities. A line of work has explored so-called doubly-efficient PIR (DEPIR), which refers to single-server PIR protocols with optimal asymptotic complexities. Recently, Lin, Mook, and Wichs (STOC 2023) presented the first protocol that completely satisfies the DEPIR constraints and can be rigorously proven secure. Unfortunately, their proposal is purely theoretical in nature. It is even speculated that such protocols are completely impractical, and hence no implementation of any DEPIR protocol exists. In this work, we challenge this assumption. We propose several optimizations for the protocol of Lin, Mook, and Wichs that improve both asymptotic and concrete running times, as well as storage requirements, by orders of magnitude. Furthermore, we implement the resulting protocol and show that for batch queries it can outperform state-of-the-art protocols.

关键词： Structured Query Language

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