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From Prison Gang to Transnational Criminal Organization. A social Network analysis of the Tren de Aragua 16th

From Prison Gang to Transnational Criminal Organization. A S...

引用

16th international conference on social networks analysis and mining

作者： Homann, Laura Alvaro-Montes, Oscar Univ Massachusetts Lowell Lowell MA 01854 USA

ISBN: (纸本)9783031785474;9783031785481

This research paper explores the evolution of the Venezuelan prison gang Tren de Aragua. This criminal group has emerged as one of the most influential entities in Latin America, exerting control that extends beyond prison walls into broader societal, business, and political spheres. This paper utilizes social Network analysis (SNA) to dissect the intricate relationships and networks surrounding the Tren de Aragua. We introduce the concept of "converged governance," which challenges traditional views of the separations between state and non-state actors. This theoretical framework proposes that the clandestine links between these state and non-state entities allow for covert collaborations that maintain internal cohesion while promoting extensive criminal activities. Our study not only provides a detailed map of the Tren de Aragua's network structure through SNA but also emphasizes the prison's role as a critical territory where state and non-state systems intersect. This offers a more systemic insight into the dynamics of criminal governance and its impact on national and regional security. By investigating the conditions under which prison gangs like the Tren de Aragua assume control over state facilities and evolve into transnational criminal organizations, this paper significantly enhances our understanding of systemic challenges by highlighting the complex, often symbiotic relationships between criminal organizations and state actors.

关键词： Tren de Aragua Criminal governance social Network analysis

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Non-binary Gender Expression in Online Interactions 16th

Non-binary Gender Expression in Online Interactions

引用

16th international conference on social networks analysis and mining

作者： Dorn, Rebecca Mokhberian, Negar Jiang, Julie Abramson, Jeremy Morstatter, Fred Lerman, Kristina Univ Southern Calif Informat Sci Inst Marina Del Rey CA 90292 USA

ISBN: (纸本)9783031785375;9783031785382

Trigger Warning: Profane Language, Slurs. The presence of openly non-binary gender individuals on social networks is growing. However, the relationship between gender, activity, and language in online interactions has not been extensively explored. Lack of understanding surrounding this interaction can result in the disparate treatment of non-binary gender individuals on online platforms. We investigate patterns of gender-based behavior identity on Twitter, focusing on gender expression as represented by users' expression of pronouns from eight different pronoun groups. We find that non-binary gender groups tend to receive substantially less attention in the form of likes and followers compared to binary groups. Additionally, non-binary users send and receive tweets with higher toxicity scores than other groups. This study identifies differences in the language and online activity of users with non-binary gender identity, and highlights a need for further evaluation of potential disparate treatment by algorithms used by online platforms.

关键词： Big Data applications social computing Text analysis

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Enhancing School Success Prediction with FRC and Merged GNN 16th

Enhancing School Success Prediction with FRC and Merged GNN

引用

16th international conference on social networks analysis and mining

作者： Aktas, Melike Yildiz Khatri, Aadyant Almutairi, Mariam Alkulaib, Lulwah Lu, Chang-Tien Virginia Tech Dept Comp Sci Falls Church VA 22043 USA Kuwait Univ Dept Comp Sci Kuwait Kuwait

ISBN: (纸本)9783031785474;9783031785481

This study presents an innovative framework for predicting school success, leveraging Large Language Models (LLMs) to define ground truth labels based on comprehensive school information with Factor-Reasoning-Classification (FRC) prompting. In this article, we conceptualize school data as a complex social network and create two different graphs where schools are defined as nodes. Similarity Graph captures school similarities, integrating factors such as graduation rates, ACT scores, socioeconomic conditions, crime rates, and community resources. Geographic Proximity Graph models spatial relationships among schools using geographical coordinates. We define Merged GNN that enhances prediction accuracy by incorporating both similarity-based and spatial proximity-based information. Our approach leverages Graph Neural networks (GNN) to predict the most probable labels the LLM model identifies. Experimental results on the school success dataset not only demonstrate the superior predictive performance of our methodology over baseline models but also highlight the importance of integrating diverse sources of information for accurate prediction and analysis.

关键词： Data labeling School success prediction Graph neural network Classification Spatial analysis

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Analyzing Characteristics of Nontrivial Information Diffusion via Implicit Links on social Media 16th

Analyzing Characteristics of Nontrivial Information Diffusio...

引用

16th international conference on social networks analysis and mining

作者： Tamura, Yuto Tsugawa, Sho Watabe, Kohei Univ Tsukuba Tsukuba Ibaraki Japan Saitama Univ Saitama Japan

ISBN: (纸本)9783031785474;9783031785481

On social media, information spreads primarily through explicit relationships, such as follow connections, among users. However, information diffusion isn't limited to explicit relationships alone. Users may come across posts from others via external websites or searches and subsequently repost them. The characteristics of such information diffusion, which bypass explicit social relationships, have not been thoroughly examined to date. In this paper, we examine the characteristics of nontrivial information diffusion facilitated by implicit-links among social media users. We define information diffusion that occurs without explicit social relationships as dissemination through implicit links. Utilizing three Twitter datasets, we investigate how users engage in information diffusion through implicit-links. Our findings suggest that users situated farther from the source are more inclined to involve in information diffusion via implicit-links, contrasting with those in closer proximity. This implies that implicit-links originate from sources beyond direct social connections among users. Additionally, we observe that while implicit-links have a weaker influence on diffusion size compared to explicit-links, they tend to distribute information across diverse communities distinct from the source user.

关键词： social networks social media Information diffusion Implicit links

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Making social Platforms Accessible: Emotion-Aware Speech Generation with Integrated Text analysis 16th

Making Social Platforms Accessible: Emotion-Aware Speech Gen...

引用

16th international conference on social networks analysis and mining

作者： De, Suparna Bostan, Ionut Sastry, Nishanth Univ Surrey Sch Comp Sci & Elect Engn Guildford Surrey England Nquiringminds Univ Pkwy Southampton SO16 7PT Hants England

ISBN: (纸本)9783031785535;9783031785542

Recent studies have outlined the accessibility challenges faced by blind or visually impaired, and less-literate people, in interacting with social networks, in-spite of facilitating technologies such as monotone text-to-speech (TTS) screen readers and audio narration of visual elements such as emojis. Emotional speech generation traditionally relies on human input of the expected emotion together with the text to synthesise, with additional challenges around data simplification (causing information loss) and duration inaccuracy, leading to lack of expressive emotional rendering. In real-life communications, the duration of phonemes can vary since the same sentence might be spoken in a variety of ways depending on the speakers' emotional states or accents (referred to as the one-to-many problem of text to speech generation). As a result, an advanced voice synthesis system is required to account for this unpredictability. We propose an end-to-end context-aware Text-to-Speech (TTS) synthesis system that derives the conveyed emotion from text input and synthesises audio that focuses on emotions and speaker features for natural and expressive speech, integrating advanced natural language processing (NLP) and speech synthesis techniques for real-time applications. Our system also showcases competitive inference time performance when benchmarked against the state-of-the-art TTS models, making it suitable for real-time accessibility applications.

关键词： Text-to-Speech Emotion embeddings Sentiment detection

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Masking the Bias: From Echo Chambers to Large Scale Aspect-Based Sentiment analysis 16th

Masking the Bias: From Echo Chambers to Large Scale Aspect-B...

引用

16th international conference on social networks analysis and mining

作者： Lee, Yeonjung Cetinkaya, Yusuf Mucahit Kulah, Emre Toroslu, Hakki Davulcu, Hasan Arizona State Univ Tempe AZ 85287 USA Middle East Tech Univ Ankara Turkiye

ISBN: (纸本)9783031785375;9783031785382

Aspect-based sentiment analysis (ABSA) is a natural language processing (NLP) task, ascribing precise sentiment linkages to specific entities and issues in text data. This paper addresses critical shortcomings in current ABSA methods, particularly the issues of limited aspects, training set biases, and lack of comprehensive stance-coded datasets. First, we develop a scalable MaskedABSA approach that masks aspect terms in training sentences to enable unbiased sentiment inference from the context alone. We show that the proposed method surpasses the state-of-the-art solutions in accuracy for the aspect term sentiment classification task, as verified by the SemEval datasets. Furthermore, we tackle the perennial challenges of limited training resources and the prohibitive costs of manual annotation in ABSA dataset creation by introducing an innovative weak supervision technique capitalizing on the inherent community clustering properties found within social media datasets. We utilize community detection algorithms to partition a share network into polarized groups with homogeneous adversarial stances, allowing large-scale aspect-based sentiment analysis dataset curation without labor intensive manual labeling. Our methodology is also validated using a real-world polarized dataset comprising diverse aspects and stances to showcase its efficacy and scalability.

关键词： aspect-based sentiment analysis social networks weakly labeled data social media

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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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Insights About Radicalism on the Digital Era: A Sociological Approach 16th

Insights About Radicalism on the Digital Era: A Sociological...

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16th international conference on social networks analysis and mining

作者： Ferro, Victoria Cavalini, Athus Goveia, Fabio Malini, Fabio Univ Fed Espirito Santo Postgrad Program Commun & Terr Vitoria ES Brazil Univ Fed Espirito Santo Postgrad Program Comp Sci Vitoria ES Brazil Univ Fed Espirito Santo Dept Commun Vitoria ES Brazil

ISBN: (纸本)9783031785474;9783031785481

The pervasiveness of new information flows and integration models stemming from the platformization of social life, which has been driven by the widespread adoption of novel communication technologies and digital social networks, has facilitated collective actions in terms of mobilization, reach, organization, and the emergence of new communication tools. Alongside the centrality of the internet in the dynamics of social life, extremist movements, which were already present in society, have gained new means to perpetuate and organize themselves. Telegram, as a digital platform with content moderation policies that are lenient compared to others, has become an open stage for the concentration of extremist users and groups. In view of this, the platform in question has also become a research object for studies on online radicalization, making it possible to monitor radical ideologies and the social consequences observed in the promotion of violence and hate speech. Seeking to identify the immaterial bases and social dynamics that compose extremist groups in Brazil, an analysis of data extracted from Telegram between May 1st, 2023, and August 30th, 2023, was conducted. For this purpose, a hybrid approach was adopted, combining big data techniques and sociological analyses. The results of this work shed light on how the opinions encouraged and expressed in radical digital communities contribute to the promotion of violence outside digital media platforms.

关键词： Radicalism Platformization social networks

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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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Dynamic Inter-organizational Communication Network in a Post-merger Integration 16th

Dynamic Inter-organizational Communication Network in a Post...

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16th international conference on social networks analysis and mining

作者： Benzinger, Michael Ghawi, Raji Zenk, Lukas Pfeffer, Juergen Tech Univ Munich Arcisstr 21 D-80333 Munich Germany Danube Univ Krems Dr Karl Dorrek Str 30 A-3500 Krems Austria

ISBN: (纸本)9783031785405;9783031785412

During a Post-Merger Integration, the communication structure of an organization changes profoundly. Effective communication and personal interaction are crucial for the success of Mergers and Acquisitions, as many such endeavours fail to achieve their targets. Mergers and Acquisitions involve the combination of two organizations to create a new entity. Creating personal networks across organizational boundaries can build bridges between several organization entities to create a cohesive, adjustable and resilient holistic organization and leverage synergies. However, little actual network data on communication and network structure during Post-Merger Integration is available. Our results are based on two merged organizational units with approximately 3,800 employees in total and a collective sum of around 250,000 email connections from an actual Post-Merger Integration data set. We show that various communication networks stabilize over time in Post-Merger Integration and, that homophily is partially reflected in the informal social network. We also find that homophily occurs on a department level but somewhat less on an organizational level and that some critical departments communicate less than expected. Our results indicate that the formal merger is reflected in the communication and ego-network structure. We expect that our findings will add insights into Post-Merger Integration research and how networks are adapting, since the findings are based on actual organizational network data.

关键词： Dynamic networks Network Stabilization Communication Homophily Mergers & Acquisitions Post-Merger Integration

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