Online Social Networks and Media最新文献_第3页

BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning BotSSCL：基于自监督对比学习的社交机器人检测

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-06-04 DOI: 10.1016/j.osnem.2025.100318

Mohammad Majid Akhtar , Navid Shadman Bhuiyan , Rahat Masood , Muhammad Ikram , Salil S. Kanhere

The detection of automated accounts, also known as “social bots”, has been an important concern for online social networks (OSNs). While several methods have been proposed for detecting social bots, significant research gaps remain. First, current models exhibit limitations in detecting sophisticated bots that aim to mimic genuine OSN users. Second, these methods often rely on simplistic profile features, which are susceptible to adversarial manipulation. In addition, these models lack generalizability, resulting in subpar performance when trained on one dataset and tested on another.

To address these challenges, we propose a framework for social Bot detection with Self-Supervised Contrastive Learning (BotSSCL). Our framework leverages contrastive learning to distinguish between social bots and humans in the embedding space to improve linear separability. The high-level representations derived by BotSSCL enhance its resilience to variations in data distribution and ensure generalizability. We evaluate BotSSCL’s robustness against adversarial attempts to manipulate bot accounts to evade detection. Experiments on two datasets featuring sophisticated bots demonstrate that BotSSCL outperforms other supervised, unsupervised, and self-supervised baseline methods. We achieve

\approx 6 %

and

\approx 8 %

higher (F1) performance than SOTA on both datasets. In addition, BotSSCL also achieves 67% F1 when trained on one dataset and tested with another, demonstrating its generalizability under cross-botnet evaluation. Lastly, under adversarial evasion attack, BotSSCL shows increased complexity for the adversary and only allows 4% success to the adversary in evading detection. The code is available at https://github.com/code4thispaper/BotSSCL.

自动账户（也被称为“社交机器人”）的检测一直是在线社交网络（OSNs）关注的一个重要问题。虽然已经提出了几种检测社交机器人的方法，但仍然存在重大的研究空白。首先，目前的模型在检测旨在模仿真正OSN用户的复杂机器人方面表现出局限性。其次，这些方法通常依赖于简单的轮廓特征，容易受到对抗性操纵。此外，这些模型缺乏泛化性，导致在一个数据集上训练和在另一个数据集上测试时性能欠佳。为了解决这些挑战，我们提出了一个基于自监督对比学习（BotSSCL）的社交机器人检测框架。我们的框架利用对比学习来区分嵌入空间中的社交机器人和人类，以提高线性可分性。由BotSSCL衍生的高级表示增强了其对数据分布变化的弹性，并确保了其通用性。我们评估了BotSSCL对操纵bot账户以逃避检测的对抗性尝试的鲁棒性。在两个具有复杂机器人的数据集上的实验表明，BotSSCL优于其他有监督、无监督和自监督基线方法。我们在两个数据集上的性能都比SOTA高≈6%和≈8% （F1）。此外，BotSSCL在一个数据集上训练并在另一个数据集上测试时也达到了67%的F1，证明了其在跨僵尸网络评估下的泛化性。最后，在对抗性逃避攻击下，BotSSCL显示出对手增加的复杂性，并且只允许对手成功逃避检测。代码可在https://github.com/code4thispaper/BotSSCL上获得。

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引用次数: 0

‘Toxic’ memes: A survey of computational perspectives on the detection and explanation of meme toxicities “有毒”模因：对模因毒性检测和解释的计算视角的调查

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-05-30 DOI: 10.1016/j.osnem.2025.100317

Delfina S. Martinez Pandiani , Erik Tjong Kim Sang , Davide Ceolin

Internet memes are multimodal, highly shareable cultural units that condense complex messages into compact forms of communication, making them a powerful vehicle for information spread. Increasingly, they are used to propagate hateful, extremist, or otherwise ‘toxic’ narratives, symbols, and messages. Research on computational methods for meme toxicity analysis has expanded significantly over the past five years. However, existing surveys cover only studies published until 2022, resulting in inconsistent terminology and overlooked trends. This survey bridges that gap by systematically reviewing content-based computational approaches to toxic meme analysis, incorporating key developments up to early 2024. Using the PRISMA methodology, we extend the scope of prior analyses, resulting in a threefold increase in the number of reviewed works. This study makes four key contributions. First, we expand the coverage of computational research on toxic memes, reviewing 158 content-based studies, including 119 newly analyzed papers, and identifying over 30 datasets while examining their labeling methodologies. Second, we address the lack of clear definitions of meme toxicity in computational research by introducing a new taxonomy that categorizes different toxicity types, providing a more structured foundation for future studies. Third, we observe that existing content-based studies implicitly focus on three key dimensions of meme toxicity—target, intent, and conveyance tactics. We formalize this perspective by introducing a structured framework that models how these dimensions are computationally analyzed across studies. Finally, we examine emerging trends and challenges, including advancements in cross-modal reasoning, the integration of expert and cultural knowledge, the increasing demand for automatic toxicity explanations, the challenges of handling meme toxicity in low-resource languages, and the rising role of generative AI in both analyzing and generating ‘toxic’ memes.

网络模因是多模态的、高度可共享的文化单位，它将复杂的信息浓缩成紧凑的交流形式，使其成为信息传播的强大载体。越来越多地，它们被用来传播仇恨、极端主义或其他“有毒”的叙事、符号和信息。在过去的五年中，模因毒性分析的计算方法的研究得到了显著的扩展。然而，现有的调查只涵盖了2022年之前发表的研究，导致术语不一致，趋势被忽视。本调查通过系统地回顾基于内容的有毒模因分析计算方法，结合到2024年初的关键发展，弥合了这一差距。使用PRISMA方法，我们扩展了先前分析的范围，导致审查作品的数量增加了三倍。这项研究做出了四个关键贡献。首先，我们扩大了有毒模因的计算研究范围，回顾了158项基于内容的研究，其中包括119篇新分析的论文，并在检查其标记方法的同时识别了30多个数据集。其次，我们通过引入一种对不同毒性类型进行分类的新分类法，解决了计算研究中模因毒性缺乏明确定义的问题，为未来的研究提供了更结构化的基础。第三，我们观察到现有的基于内容的研究隐含地关注了模因毒性的三个关键维度——目标、意图和传递策略。我们通过引入一个结构化框架来形式化这一观点，该框架对这些维度如何在研究中进行计算分析进行建模。最后，我们研究了新兴趋势和挑战，包括跨模态推理的进步、专家和文化知识的整合、对自动毒性解释的日益增长的需求、在低资源语言中处理模因毒性的挑战，以及生成式人工智能在分析和生成“有毒”模因方面的日益重要的作用。

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引用次数: 0

Message order matters: A robust author profiling approach for social media platforms 消息顺序很重要：针对社交媒体平台的健壮的作者分析方法

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-05-22 DOI: 10.1016/j.osnem.2025.100316

Mehmet Deniz Türkmen , Mucahid Kutlu

As the order of sentences can impact the meaning of texts, transformer models and recurrent neural networks (RNN) also consider the order of the tokens. However, this feature can negatively affect the classification of social media accounts, as users might share messages on entirely different topics in consecutive order. In this study, we explore how to enhance the performance of models that take into account word order for various author profiling tasks on social media. We first draw attention to the transformer models’ input limit and propose a message selection method that also reduces noise caused by irrelevant messages. In addition, we show that arbitrarily concatenating messages can be problematic. Therefore, we propose creating multiple variants of data by shuffling messages, classifying each variant separately, and then aggregating the predictions. In our comprehensive experiments, we focus on age, gender, occupation, and bot detection tasks. We show that the proposed content selection and shuffling-based methods lead to slight improvements in the transformer model’s performance for age and gender detection tasks. However, our approach yields noticeable performance increases for BiLSTM model. Additionally, we observe that the shuffling method serves as an effective means to augment training data, further enhancing models’ performance. Moreover, our shuffling-based approach enhances the models’ resistance to adversarial attacks in gender and occupation detection tasks without compromising their performance in age detection.

由于句子的顺序会影响文本的含义，转换模型和递归神经网络（RNN）也会考虑标记的顺序。然而，这一功能可能会对社交媒体账户的分类产生负面影响，因为用户可能会连续分享完全不同主题的消息。在这项研究中，我们探讨了如何在社交媒体上提高考虑词序的各种作者分析任务的模型的性能。我们首先注意到变压器模型的输入限制，并提出了一种消息选择方法，该方法也降低了无关消息引起的噪声。此外，我们还展示了任意连接消息可能会有问题。因此，我们建议通过洗牌消息创建多个数据变体，分别对每个变体进行分类，然后汇总预测结果。在我们的综合实验中，我们关注年龄、性别、职业和机器人检测任务。我们表明，提出的内容选择和基于洗牌的方法导致变压器模型在年龄和性别检测任务中的性能略有改善。然而，我们的方法为BiLSTM模型带来了显著的性能提升。此外，我们观察到洗牌方法是增强训练数据的有效手段，进一步提高了模型的性能。此外，我们基于洗牌的方法增强了模型在性别和职业检测任务中对对抗性攻击的抵抗力，而不会影响其在年龄检测中的性能。

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引用次数: 0

A first principles approach to trust-based recommendation systems in social networks 社交网络中基于信任的推荐系统的第一原则方法

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-05-17 DOI: 10.1016/j.osnem.2025.100315

Paras Stefanopoulos , Sourin Chatterjee , Ahad N. Zehmakan

This paper explores recommender systems in social networks which leverage information such as item rating, intra-item similarities, and trust graph. We demonstrate that item-rating information is more influential than other information types in a collaborative filtering approach. The trust graph-based approaches were found to be more robust to network adversarial attacks due to hard-to-manipulate trust structures. Intra-item information, although sub-optimal in isolation, enhances the consistency of predictions and lower-end performance when fused with other information forms. Additionally, the Weighted Average framework is introduced, enabling the construction of recommendation systems around any user-to-user similarity metric.

本文探讨了社交网络中的推荐系统，该系统利用诸如项目评级、项目内相似性和信任图等信息。我们证明了项目评价信息在协同过滤方法中比其他信息类型更有影响力。由于难以操纵信任结构，基于信任图的方法对网络对抗性攻击具有更强的鲁棒性。项目内信息虽然单独来看不是最优的，但当与其他信息形式融合时，可以增强预测的一致性和低端性能。此外，还引入了加权平均框架，使推荐系统能够围绕任何用户对用户的相似度度量构建。

引用次数: 0

Is it fake or not? A comprehensive approach for multimodal fake news detection 它是假的还是假的？一种多模式假新闻检测的综合方法

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-05-16 DOI: 10.1016/j.osnem.2025.100314

Davide Antonio Mura , Marco Usai , Andrea Loddo, Manuela Sanguinetti, Luca Zedda, Cecilia Di Ruberto, Maurizio Atzori

In recent years, the proliferation of fake news has posed significant challenges to information integrity and public trust, paving the way for the development of artificial intelligence-based models that can analyze information and determine its veracity. This study comprehensively evaluates the Themis architecture in the context of fake news detection on two distinct public datasets: Fakeddit and ReCoVery. To enhance model performance, we systematically investigate various customizations of Themis, including the integration of Low-Rank Adaptation, diverse data augmentation techniques, and multiple configurations, employing the TinyLlama Large Language Model and CLIP ViT image encoders while tuning key parameters to optimize results. Our findings reveal that while the standard Themis model performed adequately, significant improvements were observed by incorporating LoRA and specific data augmentation strategies, particularly in the ReCoVery dataset. Comparisons with existing literature indicate that Themis achieves competitive performance, especially in the ReCoVery dataset, where it outperforms existing solutions.

近年来，假新闻的泛滥对信息完整性和公众信任构成了重大挑战，为基于人工智能的模型的发展铺平了道路，这些模型可以分析信息并确定其真实性。本研究在两个不同的公共数据集（Fakeddit和ReCoVery）上全面评估了假新闻检测背景下的Themis架构。为了提高模型的性能，我们系统地研究了Themis的各种定制，包括集成低秩自适应、多种数据增强技术和多种配置，采用TinyLlama大型语言模型和CLIP ViT图像编码器，同时调整关键参数以优化结果。我们的研究结果表明，虽然标准Themis模型表现良好，但通过结合LoRA和特定的数据增强策略，特别是在ReCoVery数据集中，可以观察到显著的改进。与现有文献的比较表明，Themis实现了具有竞争力的性能，特别是在恢复数据集中，它优于现有的解决方案。

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引用次数: 0

Political communication and conspiracy theory sharing on twitter 推特上的政治交流和阴谋论分享

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-05-10 DOI: 10.1016/j.osnem.2025.100313

Imane Khaouja , Daniel Toribio-Flórez , Ricky Green , Cassidy Rowden , Chee Siang Ang , Karen M. Douglas

Social media has become an influential channel for political communication, offering broad reach while enabling the proliferation of misinformation and conspiracy theories. These unchecked conspiracy narratives may allow manipulation by malign actors, posing dangers to democratic processes. Despite their intuitive appeal, little research has examined the strategic usage and timing of conspiracy theories in politicians’ social media communication compared to the spread of misinformation and fake news.

This study provides an empirical analysis of how members of the U.S. Congress spread conspiracy theories on Twitter. Leveraging the Twitter Historical API, we collected a corpus of tweets from members of the US Congress between January 2012 and December 2022. We developed a classifier to identify conspiracy theory content within this political discourse. We also analyzed the linguistic characteristics, topics and distribution of conspiracy tweets. To assess classifier performance, we created ground truth data through human annotation in which experts labeled a sample of 2500 politicians’ tweets.

Our findings shed light on several aspects, including the influence of prevailing political power dynamics on the propagation of conspiracy theories and higher user engagement. Moreover, we identified specific psycho-linguistic attributes within the tweets, characterized by the use of words related to power and causation, and outgroup language. Our results provide valuable insights into the motivations compelling influential figures to engage in the dissemination of conspiracy narratives in political discourse.

社交媒体已经成为一个有影响力的政治沟通渠道，在提供广泛影响的同时，也使错误信息和阴谋论得以扩散。这些未经检查的阴谋叙事可能会被恶意行为者操纵，对民主进程构成威胁。尽管阴谋论在直觉上很有吸引力，但与错误信息和假新闻的传播相比，很少有研究调查阴谋论在政客社交媒体传播中的战略使用和时机。本研究对美国国会议员如何在Twitter上传播阴谋论进行了实证分析。利用推特历史API，我们收集了2012年1月至2022年12月期间美国国会议员的推文语料库。我们开发了一个分类器来识别这种政治话语中的阴谋论内容。我们还分析了阴谋推文的语言特征、话题和分布。为了评估分类器的性能，我们通过人工注释创建了基础事实数据，专家在其中标记了2500个政治家的推文样本。我们的研究结果揭示了几个方面，包括主流政治权力动态对阴谋论传播和更高用户参与度的影响。此外，我们确定了推文中特定的心理语言属性，其特征是使用与权力和因果关系以及外群体语言相关的词语。我们的研究结果为有影响力的人物参与政治话语中阴谋叙事传播的动机提供了有价值的见解。

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引用次数: 0

Advancing depression detection on social media platforms through fine-tuned large language models 通过微调的大型语言模型，在社交媒体平台上推进抑郁症检测

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-03-21 DOI: 10.1016/j.osnem.2025.100311

Shahid Munir Shah , Syeda Anshrah Gillani , Mirza Samad Ahmed Baig , Muhammad Aamer Saleem , Muhammad Hamzah Siddiqui

This study investigates the use of Large Language Models (LLMs) for improved depression detection from users’ social media data. Through the use of fine-tuned GPT-3.5 Turbo 1106 and LLaMA2-7B models and a sizable dataset from earlier studies, we were able to identify depressed content in social media posts with a high accuracy of 96.4%. The comparative analysis of the obtained results with the relevant studies in the literature and the base models shows that the proposed fine-tuned LLMs achieved enhanced performance compared to existing state-of-the-art systems and the base models. This demonstrates the robustness of LLM-based fine-tuned systems to be used as potential depression detection systems. The study describes the approach in depth, including the parameters used and the fine-tuning procedure, along with the dataset description and comparative summary of the results, indicating the important implications of the obtained results for the early diagnosis of depression on various social media platforms.

本研究探讨了如何使用大型语言模型（LLMs）从用户的社交媒体数据中改进抑郁检测。通过使用经过微调的 GPT-3.5 Turbo 1106 和 LLaMA2-7B 模型以及早期研究中的大量数据集，我们能够识别出社交媒体帖子中的抑郁内容，准确率高达 96.4%。将所获得的结果与文献中的相关研究和基础模型进行比较分析后发现，与现有的最先进系统和基础模型相比，所提出的微调 LLM 的性能得到了提高。这证明了基于 LLM 的微调系统的鲁棒性，可以用作潜在的抑郁检测系统。本研究深入介绍了该方法，包括使用的参数和微调程序，以及数据集描述和结果对比总结，指出了所获结果对在各种社交媒体平台上早期诊断抑郁症的重要意义。

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引用次数: 0

Community detection in Multimedia Social Networks using an attributed graph model 基于属性图模型的多媒体社交网络社区检测

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-03-20 DOI: 10.1016/j.osnem.2025.100312

Giancarlo Sperlì

In this paper, we design a novel data model for a Multimedia Social Network, that has been modeled as an attribute graph for integrating semantic analysis of multimedia content published by users. It combines features inferred from object detection, image classification, and hashtag analysis in a unified model to characterize a user from different points of view. On top of this model, community detection algorithms have been applied to unveil users’ communities. Hence, we design a framework integrating multimedia features with different community detection approaches (topological, deep learning, representation learning, and game theory-based) to improve detection effectiveness. The proposed framework has been evaluated on a real-world dataset, composed of 4.5 million profiles publishing more than 42 million posts and 1.2 million images, to investigate the impact of different features on both graph-building and community detection tasks. The main findings of the proposed analysis show how combining different sets of features inferred from multimedia content allows to achieve the highest modularity score w.r.t. other configurations although it requires a higher running time for building the underlined network. Specifically, representation and game theory-based algorithms achieve the highest results in terms of Modularity measure by exploiting the semantic and contextual information integrated into the proposed model.

本文设计了一种新的多媒体社交网络数据模型，并将其建模为一个属性图，用于集成用户发布的多媒体内容的语义分析。它将从物体检测、图像分类和标签分析中推断出的特征结合在一个统一的模型中，从不同的角度来描述用户。在这个模型之上，社区检测算法被应用于揭示用户的社区。因此，我们设计了一个将多媒体功能与不同的社区检测方法（拓扑、深度学习、表示学习和基于博弈论）集成的框架，以提高检测效率。提议的框架已经在一个真实世界的数据集上进行了评估，该数据集由450万个个人资料组成，发布了超过4200万个帖子和120万张图片，以调查不同特征对图构建和社区检测任务的影响。所建议的分析的主要发现表明，结合从多媒体内容推断出的不同功能集如何能够实现比其他配置更高的模块化得分，尽管构建下划线网络需要更高的运行时间。具体来说，基于表示和博弈论的算法通过利用集成到提议模型中的语义和上下文信息，在模块化度量方面取得了最高的结果。

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引用次数: 0

Explaining homophily without social selection: The role of transitivity in the formation of homophilic ties 解释没有社会选择的同质性：及物性在同质关系形成中的作用

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-03-17 DOI: 10.1016/j.osnem.2025.100310

Alexander V. Gubanov , Vyacheslav L. Goiko , Ivan V. Kozitsin

This paper investigates if an assortative social network may further amplify its assortativity following two distinct interventions: linking a random unconnected pair in the neighborhood of a vertex chosen by chance (N-protocol) or connecting a randomly selected open triplet (T-protocol). Under a series of assumptions, we derive a closed-form expression that links the expected change in assortativity caused by N-protocol and the current assortativity score. For the binary nodal characteristic or some other settings, this expression turns out to be a simple quadratic dependency, which indicates that networks with assortative mixing should become less assortative following N-protocol. Next, we provide a sufficient condition that N- and T-protocols will cause the same assortativity change. Using numerical experiments with synthetic and empirical networks with various assortativity rates, we demonstrate that our theoretical estimations tend to be quite accurate for topologies with moderate assortativity. However, for topologies with relatively high assortativity levels, the factual values of the assortativity shift tend to be greater than theoretical ones, but still negative. Empirical topologies are also characterized by relatively large divergences between N- and T-protocols, with the latter typically providing a higher assortativity decrease. We carefully explain these findings by analyzing local homophily patterns. We also consider a modified version of T-protocol that connects individuals having no less than a predefined number of common peers. For a corpus of empirical networks, we managed to characterize the threshold values of assortativity and the number of common friends above which the assortativity rate will increase following this intervention.

本文研究了在两种不同的干预措施之后，同类社会网络是否会进一步扩大其同类性：连接一个随机选择的顶点邻域中的一对未连接的顶点（N 协议）或连接一个随机选择的开放三元组（T 协议）。在一系列假设条件下，我们得出了一个封闭式表达式，它将 N 协议引起的同向性预期变化与当前同向性得分联系起来。对于二元节点特征或其他一些设置，这个表达式变成了一个简单的二次依赖关系，这表明在 N 协议之后，具有同类混合的网络的同类性应该会降低。接下来，我们提供了一个充分条件，即 N 协议和 T 协议将导致相同的同类性变化。通过对具有不同同配率的合成网络和经验网络进行数值实验，我们证明，对于具有中等同配率的拓扑结构，我们的理论估计往往相当准确。然而，对于同化水平相对较高的拓扑结构，同化偏移的实际值往往大于理论值，但仍为负值。经验拓扑的另一个特点是 N 协议和 T 协议之间的差异相对较大，后者通常会带来更高的同类性下降。我们通过分析局部同亲模式仔细解释了这些发现。我们还考虑了一种改进版的 T 协议，它能将具有不少于预定数量的共同同伴的个体连接起来。对于经验网络的语料库，我们设法确定了同质性的临界值和共同朋友的数量，超过这些临界值，同质性率就会增加。

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引用次数: 0

Modeling and simulation of interventions’ effect on the spread of toxicity in social media 建模和模拟干预措施对社交媒体中毒性传播的影响

Q1 Social Sciences

Online Social Networks and Media

Pub Date : 2025-03-10 DOI: 10.1016/j.osnem.2025.100309

Emmanuel Addai , Nitin Agarwal , Niloofar Yousefi

The prevalence of toxicity on social media platforms constitutes a significant issue. Gaining insights into the factors that contribute to toxicity is essential for devising effective strategies to mitigate it. In this work, we extend and evaluate fractional control SEIQR (Susceptible, Exposed, Infected, Quarantined, Recovered) epidemiological modeling incorporating government interventions and awareness programs. The model incorporates different infected groups, moderate and high infected users, and is used to investigate the influence by each user on the overall spread of toxicity. We have evaluated the toxic post-free equilibrium point, the reproduction number

(R_{0})

, the existence-uniqueness, and the stability point. We performed the model sensitivity analysis using the Latin Hypercube Sampling-Partial Rank Correlation Coefficient (LHS-PRCC) method. For data fitting analysis, we examined data from COVID-19-related tweets. We examine the intricacies of the proposed numerical scheme, providing a robust framework for analyzing and comprehending online toxicity spread. Simulations were conducted to elucidate the effects of government interventions and public awareness programs on the prevalence and dynamics of online toxicity spread. The study’s primary accomplishment is the model’s reduction of the error rate to 0.0011. This is distinguished by the reduced need to remove users from the network. The model not only improves accuracy but also maintains a larger user base, indicating an efficient, user-centric strategy. The results suggest that both awareness programs and government interventions are crucial for managing and mitigating online toxicity spread. This study will significantly assist network providers and policymakers to identify the infected users, thereby reducing toxic conversations.

社交媒体平台上流行的恶意言论构成了一个重大问题。深入了解导致毒性的因素对于制定有效的策略来减轻毒性至关重要。在这项工作中，我们扩展和评估了部分控制SEIQR（易感、暴露、感染、隔离、恢复）流行病学模型，包括政府干预和意识计划。该模型纳入了不同的感染群体，中度和高度感染用户，并用于研究每个用户对毒性整体传播的影响。我们评估了无毒后平衡点、繁殖数（R0）、存在唯一性和稳定性点。我们使用拉丁超立方抽样-偏秩相关系数（LHS-PRCC）方法进行模型敏感性分析。为了进行数据拟合分析，我们检查了与covid -19相关的推文数据。我们研究了所提出的数值方案的复杂性，为分析和理解在线毒性传播提供了一个强大的框架。我们进行了模拟，以阐明政府干预和公众意识计划对网络毒性传播的流行和动态的影响。该研究的主要成就是该模型将错误率降低到0.0011。其特点是减少了从网络中删除用户的需要。该模型不仅提高了准确性，而且保持了更大的用户群，表明了一种高效的、以用户为中心的策略。结果表明，意识项目和政府干预对于管理和减轻网络毒性传播至关重要。这项研究将极大地帮助网络提供商和政策制定者识别受感染的用户，从而减少有毒对话。

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引用次数: 0