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Fighting False Information from Propagation Process: A Survey

ACM COMPUTING SURVEYS 2023年第10期55卷 1-38页

作者： Sun, Ling Rao, Yuan Wu, Lianwei Zhang, Xiangbo Lan, Yuqian Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian 710049 Shaanxi Peoples R China

The recent serious cases of spreading false information have posed a significant threat to the social stability and even national security, urgently requiring all circles to respond adequately. Therefore, this survey illustrates how to fight against false information from its propagation process by (1) exploring the drivers of information infectivity from the content, media, user, structural, and temporal dimensions;(2) describing the propagation modeling approaches from macro (global), meso (community), and micro (individual) levels;and (3) discussing the governance strategies from both technical and application aspects. The potential data sources and the future directions of fighting are also given, hoping to facilitate more comprehensive solutions.

关键词： False information socialmedia information infectivity propagation modeling propagation governance

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Category-Controlled Encoder-Decoder for Fake News Detection

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IEEE TRANSACTIONS ON KNOWLEDGE AND data ENGINEERING 2023年第2期35卷 1242-1257页

作者： Wu, Lianwei Rao, Yuan Zhang, Cong Zhao, Yongqiang Nazir, Ambreen Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data Pr Xian 710054 Shaanxi Peoples R China Xi An Jiao Tong Univ Shaanxi Joint Key Lab Artifact Intelligence Sublab Xian 710054 Shaanxi Peoples R China Xian Huanyu Satellite Control & Data Applicat Co L Xian 710065 Shaanxi Peoples R China

The existing data-driven approaches typically capture credibility-indicative representations from relevant articles for fake news detection, such as skeptical and conflicting opinions. However, these methods still have several drawbacks: 1) Due to the difficulty of collecting fake news, the capacity of the existing datasets is relatively small;and 2) there is considerable unverified news that lacks conflicting voices in relevant articles, which makes it difficult for the existing methods to identify their credibility. Especially, the differences between true and fake news are not limited to whether there are conflict features in their relevant articles, but also include more extensive hidden differences at the linguistic level, such as the perspectives of emotional expression (like extreme emotion in fake news), writing style (like the shocking title in clickbait), etc., the existing methods are difficult to fully capture these differences. To capture more general and wide-ranging differences between true and fake news, in this paper, directly from the different categories of news itself, we propose a Category-controlled Encoder-Decoder model (CED) to generate examples with category-differentiated features and extend the dataset capacity to achieve data enhancement effect, thus enhancing fake news detection. Specifically, to make the generated examples enrich more news features, we develop news-guided encoder to guide relevant articles to generate news-semantic context representations. To drive the generated examples to contain more category-differentiated features, we devise category-controlled decoder which relies on pattern-shared unit to respectively capture intra-category shared features within true or fake news, and employs restriction unit to force the two types of shared features to be more different for highlighting inter-category differentiated features. The experimental results on three datasets demonstrate the superiority of CED.

关键词： Feature extraction Semantics Decoding social networking (online) Task analysis data models Recurrent neural networks Encoder decoder fake news detection social media analysis natural language processing

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Joint learning of video scene detection and annotation via multi-modal adaptive context network

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期249卷

作者： Xu, Yifei Pan, Litong Sang, Weiguang Luo, Hailun Li, Li Wei, Pingping Zhu, Li Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mfg Syst Engn Xian 710049 Peoples R China Alltuu Inc Hangzhou 310000 Zhejiang Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian 710054 Shaanxi Peoples R China Xian Key Lab Social Intelligence & Complex Data Pr Xian 710054 Shaanxi Peoples R China

The tasks of scene detection and annotation have gained impressive attention for understanding video content. The main challenges lie in mitigating the error propagation of shot detection, recognizing cuts and gradual transitions, fusing hierarchical multi -modal cues, and solving these two tasks simultaneously. To address these challenges, we propose the Multi -modal Adaptive Context Network (MACN) to jointly learn scene detection and annotation from a window partitioning perspective. As a shared task -agnostic part, we perform Windowbased Cross -modal Representation (WCR) to distill complex semantic correlations from multi -modal sources for each window. Considering the long-term temporal dependency of variable -length scenes, we further develop Adaptive Context -aware Representation (ACR) to improve the performance for specific tasks. Different from previous works, scene detection is formulated as locating the starting window and its associated location offset and transition duration. Meanwhile, we assemble two multi -label sub -classifiers in different levels to predict the labels for each scene candidate. Experimental comparisons to state-of-the-art algorithms on the TAVS and ClipShots indicate that the proposed method yields promising performance in both tasks. Our code and test sample videos are released at MACN.

关键词： Scene detection Scene annotation Cross-modal representation Video understanding

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HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection 37

HG-SL: Jointly Learning of Global and Local User Spreading B...

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37th AAAI Conference on Artificial intelligence (AAAI) / 35th Conference on Innovative Applications of Artificial intelligence / 13th Symposium on Educational Advances in Artificial intelligence

作者： Sun, Ling Rao, Yuan Lan, Yuqian Xia, Bingcan Li, Yangyang Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian Peoples R China Natl Engn Lab Risk Percept & Prevent Beijing Peoples R China

ISBN: (纸本)9781577358800

Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, but this is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identity, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading behaviors, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status in behavior learning process to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.

关键词： Fake detection

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MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction 36

MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Net...

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36th AAAI Conference on Artificial intelligence / 34th Conference on Innovative Applications of Artificial intelligence / 12th Symposium on Educational Advances in Artificial intelligence

作者： Sun, Ling Rao, Yuan Zhang, Xiangbo Lan, Yuqian Yu, Shuanghe Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data P Xian Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian Peoples R China

ISBN: (纸本)9781577358763

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture users' preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memoryenhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

关键词： Embeddings

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A dual-stream spatial-temporal detector for action recognition 4

A dual-stream spatial-temporal detector for action recogniti...

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4th International Conference on Computer Vision and Pattern Analysis, ICCPA 2024

作者： Wei, Pingping Li, Jiale Liu, Peiran Li, Li Xu, Yifei Wang, Ling State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiao-Tong University Xi’an710049 China School of Software Xi’an Jiao-Tong University Shaanxi Xi’an710049 China Shaanxi Joint Key Laboratory for Artifact Intelligence Shaanxi 710054 China Xi’an Key Laboratory for Social Intelligence and Complex Data Processing Shaanxi 710054 China Alltuu Inc. Zhejiang Hangzhou310000 China

ISBN: (纸本)9781510682528

In response to the issue that existing human action recognition models can not make full use of complementary information from different modalities, this thesis proposes a multi-path attention module MA to form the MA-GCN model. modalities, this thesis proposes a dual-stream human action recognition model SRHAR that fuses skeleton data and RGB data. This model utilizes LAF proposed in this thesis to fuse skeleton features and RGB features. The introduction of skeleton modality enables the RGB modality to obtain the RGB features. The introduction of skeleton modality enables the RGB modality to obtain complementary information, resulting in more accurate prediction results. This algorithm focuses more on recognition accuracy and has a slower recognition speed, but achieves leading performance in terms of accuracy on public datasets. © 2024 SPIE.

关键词： Musculoskeletal system

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Graph-based KB and Text Fusion Interaction Network for Open Domain Question Answering

Graph-based KB and Text Fusion Interaction Network for Open ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Ding, Yi Rao, Yuan Yang, Fan Xi An Jiao Tong Univ Sch Software Engn Lab Social Intelligence & Complex Data Proc Xian Peoples R China

ISBN: (纸本)9780738133669

The incompleteness of the knowledge base (KB) limits the performance of open domain question answering (QA).Represent the incomplete KB with graph attention network (GAT) and complement the incomplete KB by extra text achieves great success to boost the QA system when the KB is incomplete. In this paper, we propose a Graph-based KB and Text Fusion Interaction Network (GTFIN) to improve the performance of the incomplete QA system by utilizing the KB and text *** GTFIN, to reduce the influence of the query-unrelated noisy information of GAT on final answer prediction, we first design a global-normalization graph attention network (GGAT) by determining the query-related edge weights from the global perspective, and then a coarse-to-fine text reader (CFReader) is proposed to both exploit the relation information and obtain the entity mention representation in the text to enhance the incomplete KB. We further incorporate a bi-attention mechanism to enhance the interaction between question and entity representation which could find more query-related entities for final answer prediction. On the widely used KBQA benchmark WebQSP, our model achieves state-of-the-art performance.

关键词： open domain question answering incomplete knowledge base coarse-to-fine text reader bi-attention

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Graph Explicit Attention Network Based on Predefined Strategy 12

Graph Explicit Attention Network Based on Predefined Strateg...

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12th International Conference on Identification, Information and Knowledge in the internet of Things, IIKI 2021

作者： Zhang, Xiangbo Rao, Yuan Sun, Ling Lab of Social Intelligence and Complexity Data Processing School of Software Engineering Xi'an Jiaotong University Shaanxi Xi'an710049 China Shannxi Joint Key Laboratory for Artifact Intelligence Sub-Lab of Xi'an Jiaotong University China Research Institute of Xi'an Jiaotong University Shenzhen China

Graph attention network (GAT) has achieved great success in graph representation learning in recent years. However, the lower Physical properties of GAT in training process severely affects the application of attention mechanism in graph domain. Particularly, the attention coefficients and the multi-head mechanism are calculated or introduced in the process of model training, which obviously increases the storage complexity. In this paper, we present a graph explicit attention network (GEAT), a novel graph attention architecture that leverage predefined strategy to calculate attention coefficients, which combine global structural and node feature information. In this way, GEAT can effectively reduce the storage complexity, improve the training efficiency and alleviate the over-smoothing problem of attention scores. Experiments on benchmark datasets — Core, Citeseer and Pubmed — demonstrate that our model outperforms the state-of-the-art methods with a clear margin in challenging classifcation tasks, while being computationally efficient. © 2022 The Authors. Published by Elsevier B.V.

关键词： Attention mechanism Deep Learning Graph neural network

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A Multi-semantics Classification Method Based on Deep Learning for Incredible Messages on social Media

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Chinese Journal of Electronics 2019年第4期28卷 754-763页

作者： WU Lianwei RAO Yuan YU Hualei WANG Yiming AMBREEN Nazir Lab of Social Intelligence and Complex Data Processing School of Software Engineering Xi'an Jiaotong University

How to classify incredible messages has attracted great attention from academic and industry nowadays. The recent work mainly focuses on one type of incredible messages(a.k.a rumors or fake news) and achieves some success to detect them. The existing problem is that incredible messages have different types on social media, and rumors or fake news cannot represent all incredible messages. Based on this, in the paper, we divide messages on social media into five types based on three dimensions of information evaluation metrics. And a novel method is proposed based on deep learning for classifying the five types of incredible messages on social *** specifically, we use attention mechanism to obtain deep text semantic features and strengthen emotional semantics features, meanwhile, construct universal metadata as auxiliary features, concatenating them for incredible messages classification. A series of experiments on two representative real-world datasets demonstrate that the proposed method outperforms the state-of-the-art methods.

关键词： Information credibility evaluation Rumor detection social media Text classification

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Research on Model and Algorithm of Multiview and Multilabel Classification Based on Nearest-Neighbor Model

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MATHEMATICAL PROBLEMS IN ENGINEERING 2022年第1期2022卷

作者： Zhang, Junyi Rao, Yuan Xi An Jiao Tong Univ Sch Software Engn Xian Key Lab Social Intelligence & Complex Data Pr Xian Peoples R China Shaanxi Joint Key Lab Artifact Intelligence Xian Peoples R China

In the field of machine learning, multilabel learning gradually evolved from the traditional text classification problem. In this study, a multiview multilabel via optimal classifier chain (MVMLOCC) algorithm based on the nearest-neighbor model is proposed. The algorithm model establishes a multilabel chain classifier for each view of the dataset and predicts unknown data samples by dynamically adjusting the weights of chain learners. When an unknown example is input, multiple chain classifier models are multiplied by corresponding weights to obtain the final tag set situation. The model makes full use of the relevance between multiple tags and the complementarity and integrity between multiple perspectives and can achieve better learning results. The final experimental results show that our proposed model can be applied to different multilabel classification tasks and has achieved excellent performance under different evaluation indicators. This study mainly studies the multiview and multilabel classification method and uses active learning technology to solve the problem of high label components in data collection.

关键词： Classification (of information)

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