IEEE Transactions on Computational Social Systems最新文献

{"title":"The Face of Deception: The Impact of AI-Generated Photos on Malicious Social Bots","authors":"Maxim Kolomeets;Han Wu;Lei Shi;Aad van Moorsel","doi":"10.1109/TCSS.2024.3461328","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3461328","url":null,"abstract":"In this research, we investigate the influence of utilizing artificial intelligence (AI)-generated photographs on malicious bots that engage in disinformation, fraud, reputation manipulation, and other types of malicious activity on social networks. Our research aims to compare the performance metrics of social bots that employ AI photos with those that use other types of photographs. To accomplish this, we analyzed a dataset with 13 748 measurements of 11 423 bots from the VK social network and identified 73 cases where bots employed generative adversarial network (GAN)-photos and 84 cases where bots employed diffusion or transformers photos. We conducted a qualitative comparison of these bots using metrics such as price, survival rate, quality, speed, and human trust. Our study findings indicate that bots that use AI-photos exhibit less danger and lower levels of sophistication compared to other types: AI-enhanced bots are less expensive, less popular on exchange platforms, of inferior quality, less likely to be operated by humans, and, as a consequence, faster and more susceptible to being blocked by social networks. We also did not observe any significant difference between GAN-based and diffusion/transformers-based bots, indicating that diffusion/transformers models did not contribute to increased bot sophistication compared to GAN models. Our contributions include a proposed methodology for evaluating the impact of photos on bot sophistication, along with a publicly available dataset for other researchers to study and analyze bots. Our research findings suggest a contradiction to theoretical expectations: in practice, bots using AI-generated photos pose less danger.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1080-1091"},"PeriodicalIF":4.5,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discriminative Adversarial Network Based on Spatial–Temporal–Graph Fusion for Motor Imagery Recognition","authors":"Qingshan She;Tie Chen;Feng Fang;Yunyuan Gao;Yingchun Zhang","doi":"10.1109/TCSS.2024.3462823","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3462823","url":null,"abstract":"Motor imagery (MI)-based electroencephalography (EEG) stands as a prominent paradigm in the brain–computer interface (BCI) field, which is frequently applied in neural rehabilitation and gaming due to its accessibility and reliability. Despite extensive research dedicated to MI EEG classification algorithms, a notable deficiency still remains: their performance is often optimal only in subject-specific or dataset-specific scenarios, which undermines their generalization capability, hence restricting BCI systems' practical utility in real-world contexts. To address this limitation, this study introduces a cutting-edge approach: a discriminative adversarial network based on spatial–temporal–graph fusion (STG-DAN). This innovation aims to learn features that are not only class-discriminative but also domain-invariant. Specifically, the feature extraction module guarantees the feature discriminativeness by amalgamating spatial–temporal and graph-related features, while the domain alignment module focuses on both global domain and local subdomain. The two modules are incorporated into one adversarial learning framework to facilitate the acquisition of domain-invariant features. Evaluations on two publicly accessible datasets, BCI competition IV 2a and OpenBMI, affirm the superiority of our proposed model (averaged accuracy = 62.94% and 73.01% for the two datasets in cross-subject circumstance, respectively). In cross-dataset circumstances, it also outperforms several state-of-the-art algorithms, attesting to the potency of STG-DAN.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"972-983"},"PeriodicalIF":4.5,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMCRL: EM-Enhanced Negative Sampling Strategy for Contrastive Representation Learning","authors":"Kun Zhang;Guangyi Lv;Le Wu;Richang Hong;Meng Wang","doi":"10.1109/TCSS.2024.3454056","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3454056","url":null,"abstract":"As one representative framework of self-supervised learning (SSL), contrastive learning (CL) has drawn enormous attention in the representation learning area. By pulling together a “positive” example and an anchor, as well as pushing away many “negative” examples from the anchor, CL is able to generate high-quality representations for the data of different modalities. Therefore, the qualities of selected positive and negative examples are critical for the performance of CL-based models. However, due to the assumption of label unavailability, most existing work follows the paradigm of contrastive instance discrimination, which treats each input instance as an individual category. Therefore, they focused more on positive example generation and designed plenty of data augmentation strategies. For negative examples, they just leverage the in-batch negative sampling strategy. We argue that this negative sampling strategy will easily select false negatives and inhibit the capability of CL, which we also believe is one of the reasons why a large size of negatives is needed in CL. Apart from using annotated labels, we try to tackle this problem in an unsupervised manner. We propose to integrate expectation maximization (EM) into the selection of negative examples and develop a novel <italic>EM-enhanced negative sampling strategy</i> (<italic>EMCRL</i>) to distinguish false negatives from true ones for CL performance improvement. Specifically, <italic>EMCRL</i> employs EM to estimate the distribution of ground-truth relations between each sample and corresponding in-batch negatives and then optimizes model parameters with the estimations. Considering the sensitivity of EM algorithm to the parameter initialization, we propose to add a random flip into the distribution estimation to enhance the robustness of the learning process. Extensive experiments over several advanced models on sentence representation and image representation tasks demonstrate the effectiveness of <italic>EMCRL</i>. Our method is easy to implement, and the code is publicly available at <uri>https://github.com/zhangkunzk/EMCRL_pytorch</uri>.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1256-1267"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144185858","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Group Influence Maximization in Social Networks Using Deep Reinforcement Learning","authors":"Smita Ghosh;Tiantian Chen;Weili Wu","doi":"10.1109/TCSS.2024.3459853","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3459853","url":null,"abstract":"In contemporary society, groups are pivotal in shaping decisions and actions. The consensus of a majority of members on specific topics often guides the collective decision-making in groups. Group influence maximization (GIM) aims to select <inline-formula><tex-math>$k$</tex-math></inline-formula> seed users in a network to maximize the number of eventually activated groups. A group is said to be activated if <inline-formula><tex-math>$beta$</tex-math></inline-formula> percent of users in this group are activated. This study delves into the strategic selection of seed users in social networks to maximize the spread of a topic, thereby activating the highest number of groups. The GIM problem, inherently NP-hard when computing the influence spread from a selected set of nodes, has traditionally faced obstacles in ensuring theoretical robustness, time efficiency, and adaptability in large and complex network environments. To overcome these challenges, we introduce a robust framework called GIMDRL that addresses the GIM problem in social networks using deep reinforcement learning (DRL). Our approach integrates node embeddings from multiple graph neural networks, thereby utilizing diverse information for effective network analysis. This integration plays a crucial role in optimizing the parameter learning process. Extensive experiments are conducted on real-world and synthetic datasets to assess the performance of our proposed framework. The results of these experiments indicate that our approach significantly outperforms existing methods in GIM, even when trained on sampled graphs. This highlights our model's strong capacity for generalization in varying network scenarios.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"573-585"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SpaTeD: Sparsity-Aware Tensor Decomposition-Based Representation Learning Framework for Phishing Scams Detection","authors":"Medhasree Ghosh;Raju Halder;Joydeep Chandra","doi":"10.1109/TCSS.2024.3462552","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3462552","url":null,"abstract":"In recent years, the consequences of phishing scams on Ethereum have adversely affected the stability of the cryptocurrency environment. Numerous incidents have been reported that have resulted in a substantial loss of cryptocurrency. The existing literature in this area primarily leverages traditional feature engineering or network representation learning to recover crucial information from transaction records to identify suspected users. However, these methods mainly rely on handcrafted feature engineering or conventional node representation learning from a static network while ignoring the network dynamism and inherent temporal sparsity in the user behavior that results in underperformance after an extended period. This article proposes a novel sparsity-aware tensor decomposition-based architecture: <italic>SpaTeD</i>, which retrieves efficient user representation utilizing the evolving transaction and structural information and subsequently mitigates the temporal sparsity problem. Our model is evaluated on a real-world Ethereum phishing scam dataset and reports a significant performance improvement over the baselines (96% <italic>recall</i> and 96% <italic>F1-score</i>). We have conducted an extensive set of experiments to verify the temporal robustness of the model. Additionally, we have provided the ablation study to demonstrate the contribution of each component of the framework.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"320-334"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143106587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deformable Blur Sensing and Regression Analysis ReID Feature Fusion for Multitarget Multicamera Tracking Systems in Highway Scenarios","authors":"Sixian Chan;Shenghao Ni;Bin Guo;Jie Hu;Tinglong Tang;Xiaolong Zhou;Pengyi Hao","doi":"10.1109/TCSS.2024.3454321","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3454321","url":null,"abstract":"In highway scenarios, the rapid motion of vehicles can cause deformation and blur in camera footage, significantly affecting the accuracy of vehicle detection and re-identification (ReID) in multitarget multicamera tracking (MTMCT) systems. To address this issue, this article develops the deformable and blur sensing and regression analysis ReID feature fusion MTMCT system (DSRF). First, a deformable and blur sensing detection module (DFB) in DSRF is designed to overcome the limitations of cameras in capturing fast-moving objects, thereby accurately detecting vehicles moving at high speeds on highways. Then, a regression-based ReID feature fusion algorithm (RARF) in DSRF is proposed, which enhances ReID features by modeling the relationship between vehicle motion and its features, thereby better associating the detected vehicles in consecutive frames into trajectories and establishing intertrajectory relationships. Finally, extensive experiments are conducted on the highway surveillance traffic (HST) dataset developed by our team and the public dataset (CityFlow). Promising results are achieved, validating the effectiveness of our proposed method.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 2","pages":"738-748"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143783349","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Subspace-Based Method for Facial Image Editing","authors":"Nan Yang;MengChu Zhou;Xin Luan;Liang Qi;Yandong Tang;Zhi Han","doi":"10.1109/TCSS.2024.3447692","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3447692","url":null,"abstract":"In the realm of computational social systems, the ability to edit facial attributes accurately plays a crucial role in enhancing user experience on social media platforms and virtual environments. However, we face significant challenges in isolated attribute manipulation and balancing the tradeoff between editing fidelity and facial identity preservation. Here, this article presents a novel approach to constructing an orthogonal decomposition subspace, enabling precise editing control over individual attributes with minimal impact on others and maintaining identity consistency. We introduce an adaptive weight modulation (AWM) method and a maximum slope truncation (MST) formula. The AWM method, founded on a sufficient convergent criterion, performs singular value decomposition to yield subspace parameters that preserve rich facial knowledge within the generative model, facilitating high-quality facial generation with reduced parameterization. This empowers meaningful semantic interpretation of attributes, supporting diverse editing tasks such as pose, age, and eyewear adjustments. The MST formula rigorously defines the editing bounds to effectively navigate the tradeoff between editing depth and identity retention. We also propose a guideline for deciphering the specific meanings of unsupervised semantics, potentially advancing interpretability in social behavioral studies. An accompanying web application, available at <uri>https://github.com/mickoluan/GreenLimeSia</uri>, has been developed, granting users the freedom to perform tailored facial edits. Extensive experimental results show we pave the way for more personalized and authentic interactions within computational social platforms.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 1","pages":"185-197"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling Temporal Interaction for Dynamic Sentiment Analysis on Social Network","authors":"Anping Zhao;Saiqi Tian;Yu Yu","doi":"10.1109/TCSS.2024.3457897","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3457897","url":null,"abstract":"With the evolution of the network over time, dynamic sentiment is ubiquitous in the real social network. Learning the temporal social interactions representation and modeling a dynamic socio-sentiment analysis model is important to understand the network data and necessary for accurately analyzing and prediction. In this work, we design a temporal social network representation model for dynamic sentiment analysis by capturing the temporal interaction information in the evolutionary social network. Specifically, the temporal social network embedding method is employed to learn dynamic representations of node and node's interaction relationships from the evolutionary network by preserving both the explicit structural proximity information and implicit multiview association information. The joint temporal heterogeneous social network embeddings are learned by fusing the different dimensional representation at their temporal granularity, which can be used to naturally support sentiment analysis on social network in a dynamic way. The results demonstrate that the raised approach reports better performance than the baseline methods. The results indicate the importance of incorporating temporal dependencies in social network for dynamic sentiment analysis. It also indicates the effectiveness of the proposed approach for learning meaningful dynamic network representations to improve sentiment analysis performance.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"12 3","pages":"1233-1242"},"PeriodicalIF":4.5,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Computational Social Systems Information for Authors","authors":"","doi":"10.1109/TCSS.2024.3457715","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3457715","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"C4-C4"},"PeriodicalIF":4.5,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704071","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Aids on Mental Health: Revolutionizing Care in the Digital Age","authors":"Shiqiu Meng;Jie Shi;Bin Hu","doi":"10.1109/TCSS.2024.3458128","DOIUrl":"https://doi.org/10.1109/TCSS.2024.3458128","url":null,"abstract":"","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":"11 5","pages":"5559-5576"},"PeriodicalIF":4.5,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10704034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}