User Behavior Characteristics Research Articles

A survey on social network’s anomalous behavior detection

AbstractThe onset of Web 3.0 has catalyzed the rapid advancement of social networking, transforming platforms into essential elements deeply embedded within the fabric of daily life. Researchers have proposed several methods for detecting anomalous behaviors in various scenarios. This article provides a comprehensive review of current research and the latest developments in anomalous behavior detection within social networks. We present a hierarchical three-layer categorization scheme based on the distinct characteristics of base-level detection technologies and various datasets. First, anomaly detection based on user behavioral characteristics can intuitively reflect deviations in individual behavior. However, it may overlook the overall network structure’s impact. Second, detecting anomalies within a network’s topological structure highlights structural significance, but may overlook the subtle nuances of individual behavior. Finally, the coordinated fusion method, which blends individual behavioral characteristics and the network’s topological structure, addresses the multifaceted nature of anomalies, yielding a more thorough and accurate anomaly detection strategy. This paper provides an overview and assesses the performance of three anomaly detection methods. Furthermore, we explore the challenges associated with social network anomaly detection and the potential pathways for further research.

Energy big data abnormal cluster detection method based on redundant convolution codec.

Due to the scattered distribution and poor clustering of abnormal clusters in energy big data, the ability to detect anomalies is poor. Therefore, a high-energy data anomaly clustering detection method based on redundant convolutional encoding is proposed. Quantitative analysis of the coupling characteristics of electrical thermal gas optical time series for multi energy users based on Copula function, and incorporating quantitative values into multi energy feature indicators to extract the energy consumption behavior characteristics of multi energy users. Utilize redundant convolutional codecs to recombine and structurally encode abnormal features of energy big data, and capture multi energy coupling time features using coupling time capsule layers. Then, coupling time features are synthesized through fully connected linear regression layers to generate anomalous clustering feature components, and the energy time series data is then transformed into feature values of the time series in three-dimensional space. Based on this, a comprehensive energy system and massive multi energy user energy big data anomaly clustering analysis are carried out to determine the optimal number of multi energy users. Then, based on linear layers, the electricity heat gas light load characteristic map of multi energy users is transformed into one-dimensional form, and an energy big data anomaly clustering detection model is constructed to complete anomaly detection. The simulation results show that the proposed method has excellent feature clustering performance, detection accuracy above 98.7%, fast convergence speed, and an error rate below 0.1, which has reliable application value.

DeFi Survival Analysis: Insights Into the Emerging Decentralized Financial Ecosystem

We propose a survival analysis approach for discovering and characterizing user behavior and risks for lending protocols in decentralized finance (DeFi). We demonstrate how to gather and prepare DeFi transaction data for survival analysis. We illustrate our approach using transactions in Aave, one of the largest lending protocols. We develop a DeFi survival analysis pipeline that first prepares transaction data for survival analysis through the selection of different index events (or transactions) and associated outcome events. Then we apply survival analysis statistical and visualization methods modified for competing risks when appropriate, such as Kaplan–Meier survival curves, cumulative incidence functions, Cox hazard regression, and Fine-Gray models for sub-distribution hazards to gain insights into usage patterns and risks within the protocol. We show how, by varying the index and outcome events as well as covariates, we can use DeFi survival analysis to answer questions like “How does loan size affect the repayment schedule of the loan?”; “How does loan size affect the likelihood that an account gets liquidated?”; “How does user behavior vary between Aave markets?”; “How has user behavior in Aave varied from quarter to quarter?” The proposed DeFi survival analysis can easily be generalized to other DeFi lending protocols. By defining appropriate index and outcome events, DeFi survival analysis can be applied to any cryptocurrency protocol with transactions.

FDGNN: Feature-Aware Disentangled Graph Neural Network for Recommendation

Collaborative filtering (CF) is dedicated to learning the representations of users and items based on interactive data. Regrettably, the lack of fine-grained modeling of interactive motivation makes the model less interpretable. A feasible solution is to combine the disentangling idea with the graph neural network (GNN) and capture different types of interaction relationships by using a message propagation mechanism on the graph of user–item interaction. However, this process typically relies on the disentangling of users’ hidden intents, ignoring the significance of item features to user engagement. This fact leads to the inadequate interpretability of existing models. To make up for the deficiency, this article proposes a new feature-aware disentangled GNN (FDGNN) for the recommendation. By learning the relationship between user behavior and important features of items, the model aims to achieve better recommendation performance and model interpretability. In the end, we first realize the feature partition based on mutual information and then design an attention-based graph disentangling model to realize the fine-grained disentangling of user intents. In addition, to further ensure the independence of the disentangled intents, we augment the model with disagreement regularization. Through multilayer embedding propagation, FDGNN can display a capture CF effect in feature semantics. The interpretability and efficiency of our proposed approach are demonstrated by numerous pertinent experiments.

Push Method of Online Learning Resources Based on User Behavior Characteristics

Push Method of Online Learning Resources Based on User Behavior Characteristics

Research on Algorithm-Driven International Chinese Online Language and Culture Dissemination Mechanisms

Abstract With the development of digitalization and Internet technology, algorithm-driven international Chinese online language and culture dissemination mechanisms are becoming increasingly important and a key area of cultural exchange in globalization. This study explores the international Chinese online language and culture communication mechanism under algorithm-driven. By analyzing social network data and adopting quantitative methods such as the SEIR model, the study reveals the effects of network structure, user behavior and cultural content characteristics on communication. The study results show that in the algorithm-optimized network environment, the propagation speed of information is increased by about 50%, and the propagation efficiency of its central node is increased by nearly 70%. Simulations using the SEIR model show that the dissemination coverage of Chinese culture can be increased by more than 40% in a short period with the intervention of the algorithm. In addition, the survey data shows that about 60% of the respondents consider social media the main channel to learn about Chinese culture. The study also found that content with a high degree of interactivity is more likely to be disseminated, with an average interaction rate 30% higher than that of moderate content. Algorithm-driven Chinese cultural communication has higher speed and breadth, but it also needs to pay attention to the depth and diversity of cultural content.

Exploring Graphic Design and its Interactivity in the Context of Cognitive Psychology

Abstract Cognitive psychology can help better understand human thinking and behavior, and its application in design can increase the infectious force of design. In this paper, because the brain will generate ERP signals when cognitive stimuli stimulate it, the group sparse decomposition method obtains ERP from EEG signals, and uses the BAP of brain regions to construct the interaction activity pattern of brain regions, and then combines with the interaction genetic algorithm to search for the optimal solution for the data in graphic design by using hierarchical degradation method for processing. The EEG data and interaction behavior characteristics of users were explored through chromatic stimulus response experiments and interaction behavior analysis. It was found that the accuracy of users’ responses to orange, green and purple were all higher than 60%, and P400 in EEG responded to the stimuli most significantly, with P-values of 0.0291, 0.0462, and 0.0355 at P5, P6, and Pz, respectively, with significant effects. The average number of interactions of 46 users in the graphic design enjoyment was 31.47, with the highest success rate of interaction reaching 99.81%, and the average duration of interaction experience for all subjects was 10.26 minutes. Graphic design based on cognitive psychology can capture people’s inner emotions and make better design solutions.

Big Data User Behaviour Prediction Model Incorporating Deep Learning

Abstract With the improvement of the quality of human life, various industries utilize deep learning technology to meet the needs of users. In this paper, after exploring the deep temporal model and deep forest algorithm (DF) model in extracting the characteristics of user behavior features, we propose a user behavior prediction model based on CNN-LSTM and add a front embedding layer as well as a feature fusion layer to improve it and increase the prediction accuracy. The weighted average method is used to integrate CNN-LSTM with DF for algorithmic model fusion, aiming to improve the robustness and stability of the model and achieve accurate predictions of user behavior. In the empirical analysis, the correct rate of the fusion model proposed in this paper exceeds the proper rate of several other models by 11.75-15.73%, and it can reach 11.2% recall at k=20. Meanwhile, the lower the user behavior level is, the higher the prediction accuracy of the CNNLSTM+DF algorithm is, which can reach up to 95.20%. The lower the average relative error and average absolute error are, which can reach 13.45% and 3.92min respectively, which verifies the validity of the fusion model proposed in this paper, and provides a reference for the research in the related fields.

Insider Threat Detection Based on Deep Clustering of Multi-Source Behavioral Events

With the continuous advancement of enterprise digitization, insider threats have become one of the primary cybersecurity concerns for organizations. Therefore, it is of great significance to develop an effective insider threat detection mechanism to ensure the security of enterprises. Most methods rely on artificial feature engineering and input the extracted user behavior features into a clustering-based unsupervised machine learning model for insider threat detection. However, feature extraction is independent of clustering-based unsupervised machine learning. As a result, user behavior features are not the most appropriate for clustering-based unsupervised machine learning, and thus, they reduce the insider threat detection accuracy. This paper proposes an insider threat detection method based on the deep clustering of multi-source behavioral events. On the one hand, the proposed method constructs an end-to-end deep clustering network and automatically learns the user behavior feature expression from multi-source behavioral event sequences. On the other hand, a deep clustering objective function is presented to jointly optimize the learning of feature representations and the clustering task for insider threat detection. This optimization can adjust the optimal user behavior features for the clustering model to improve the insider threat detection accuracy. The experimental results show that the proposed end-to-end insider threat detection model can accurately identify insider threats based on abnormal multi-source user behaviors in enterprise networks.

CNN-Bi-LSTM Model for MOOC Forum Post Classification

The discussion forum of the massive open online course (MOOC) is a platform for students to communicate with teachers, teaching assistants, and platform managers. It is one of the important factors related to course quality. A reasonable classification of discussion posts in the forum will help students better communicate and solve problems, so as to improve the quality of teaching. Aiming at the classification of discussion forum posts, this paper proposes a text classification model integrating convolutional neural networks (CNN) and bidirectional long-short-term memory (Bi-LSTM). Firstly, the user types and behavior characteristics are analyzed to build the taxonomy. The taxonomy includes three categories: course related, teacher related and platform related. Then, a text classification model is constructed based on CNN and Bi-LSTM. In order to verify the effectiveness of the proposed model, it is applied to the classification of 19285 discussion posts from the MOOC platform of icourse163.org. The overall classification accuracy of the proposed model is 93.6%, which is 12%, 10%, and 8% higher than traditional machine learning methods, CNN and Bi-LSTM, respectively. The model is used for automatic text classification in MOOC discussion forum, which can provide effective help and support for learners, teachers and platform managers, and improve the automation level of MOOC platform.

Extraction and Prediction of User Communication Behaviors From DNS Query Logs Based on Nonnegative Tensor Factorization

Owing to the critical role of the domain name system (DNS), its query log data are utilized for various network monitoring purposes. With the diversification of network services, these data have become increasingly complex, making mining useful information challenging. DNS query log data can be considered as the superposition of two types of communication patterns: groups of domains accessed simultaneously (e.g., ad servers and content delivery network (CDN) servers) and time-series access patterns based on user behavior characteristics (e.g., access trends during the night). However, previous studies have not focused on extracting both access patterns hidden in the data. This study proposes a method that extracts both patterns of accessed domains and temporal access patterns as user communication behaviors from DNS query log data and predicts future accesses based on these patterns. The proposed method first aggregates similar fully qualified domain names (FQDNs) associated with the same service. We then present temporal regularized nonnegative tensor factorization (TR-NTF) that extracts both access patterns from a third-order tensor expressing DNS query log data and enables prediction. We evaluate the proposed method using synthetic and actual data and demonstrate that it successfully extracts hidden communication patterns and achieves sufficient prediction accuracy.

Full-process electric vehicles battery state of health estimation based on Informer novel model

Accurately estimating state of health (SOH) of the power battery, as the core component of electric vehicles (EVs), is of great significance to the safety of EVs and the sustainable development of energy. Given the difficulty in measuring the capacity of lithium-ion batteries (LIBs) during vehicle operation and the linearization of prediction caused by not considering the phenomenon of capacity regeneration, a novel SOH estimation framework based on Informer and the entire charging and discharging process is proposed, which can be applied to the entire life cycle of power batteries in actual complex scenarios. In this study, the charge-discharge capacity is reanalyzed based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to obtain a nonlinear representation of the capacity. User behavior characteristics based on frequency and hourly meteorological data were extracted as health indicators (HIs) of LIBs. To integrate charge-discharge and meteorological data at different time scales, multivariate statistical data were resampled. The global dependency between HIs and SOH of the model input was learned by the Informer network. The results show that Informer networks outperform the cyclic structure-based model in prediction accuracy and robustness across different data distributions, and have great promise for state estimation.

Incorporating user behavior flow for user risk assessment

PurposeInternet marketing underground industry users refer to people who use technology means to simulate a large number of real consumer behaviors to obtain marketing activities rewards illegally, which leads to increased cost of enterprises and reduced effect of marketing. Therefore, this paper aims to construct a user risk assessment model to identify potential underground industry users to protect the interests of real consumers and reduce the marketing costs of enterprises.Design/methodology/approachMethod feature extraction is based on two aspects. The first aspect is based on traditional statistical characteristics, using density-based spatial clustering of applications with noise clustering method to obtain user-dense regions. According to the total number of users in the region, the corresponding risk level of the receiving address is assigned. So that high-quality address information can be extracted. The second aspect is based on the time period during which users participate in activities, using frequent item set mining to find multiple users with similar operations within the same time period. Extract the behavior flow chart according to the user participation, so that the model can mine the deep relationship between the participating behavior and the underground industry users.FindingsBased on the real underground industry user data set, the features of the data set are extracted by the proposed method. The features are experimentally verified by different models such as random forest, fully-connected layer network, SVM and XGBOST, and the proposed method is comprehensively evaluated. Experimental results show that in the best case, our method can improve the F1-score of traditional models by 55.37%.Originality/valueThis paper investigates the relative importance of static information and dynamic behavior characteristics of users in predicting underground industry users, and whether the absence of features of these categories affects the prediction results. This investigation can go a long way in aiding further research on this subject and found the features which improved the accuracy of predicting underground industry users.

Moviebox: A Movie Recommendation System

Abstract: In the vast world of cinema, numerous critically acclaimed movies often go unnoticed, despite their significant artistic and cultural value. Recognizing this disparity, our research endeavors to bridge the gap by developing a comprehensive movie recommendation system that highlights these "Out of the box" films. In the initial phase, we meticulously collected a bespoke dataset by scraping data from IMDb, encompassing a wide range of movies from various genres and regions. In the subsequent phase, we constructed an advanced algorithm utilizing content-based filtering techniques. This algorithm analyzes both user behavior and movie features to provide personalized recommendations that align with users' unique preferences. By embracing this approach, our research aims to enhance the discoverability and appreciation of lesser-known yet meaningful movies, empowering users to explore a diverse array of cinematic experiences.

Sensor-based implicit authentication through learning user physiological and behavioral characteristics

Nowadays, a large amount of users’privacy data is stored in mobile intelligent devices, and authentication systems have become an important defense to protect that data. The traditional explicit authentication, however, is under significant threat from a great deal of side channel attacks, such as those based on computer vision and touch screen attacks. Users’authentication keys are facing a high risk of leakage, whereas implicit authentication is one of the effective methods to resist various side channel attacks. Nevertheless, existing user implicit authentication approaches still have weaknesses in security and usability, which increase users’authentication burdens and pose potential safety issues. To address this problem, our paper proposes a user implicit authentication approach based on physiological and behavioral characteristics, using the most commonly performed actions in daily life to pre-authenticate users. In our method, multiple sensors are utilized to extract physiological and behavioral characteristics of users, and the sensor data is fused by aligning the extracted characteristic data and using the Kalman filter to reduce its noise. Users’authentication results can be determined by comparing the similarities between their reference and authentication samples using the siamese neural network. The experimental results show that our approach improves the performance of implicit authentication while ensuring its security and usability.

UsbVisdaNet: User Behavior Visual Distillation and Attention Network for Multimodal Sentiment Classification.

In sentiment analysis, biased user reviews can have a detrimental impact on a company's evaluation. Therefore, identifying such users can be highly beneficial as their reviews are not based on reality but on their characteristics rooted in their psychology. Furthermore, biased users may be seen as instigators of other prejudiced information on social media. Thus, proposing a method to help detect polarized opinions in product reviews would offer significant advantages. This paper proposes a new method for sentiment classification of multimodal data, which is called UsbVisdaNet (User Behavior Visual Distillation and Attention Network). The method aims to identify biased user reviews by analyzing their psychological behaviors. It can identify both positive and negative users and improves sentiment classification results that may be skewed due to subjective biases in user opinions by leveraging user behavior information. Through ablation and comparison experiments, the effectiveness of UsbVisdaNet is demonstrated, achieving superior sentiment classification performance on the Yelp multimodal dataset. Our research pioneers the integration of user behavior features, text features, and image features at multiple hierarchical levels within this domain.

Nicotine Intake in Adult Pod E-cigarette Users: Impact of User and Device Characteristics.

This study examined user behavior, e-cigarette dependence, and device characteristics on nicotine intake among users of pod-mod e-cigarettes. In 2019-2020, people who use pod-mods in the San Francisco Bay Area completed questionnaires and provided a urine sample for analysis of total nicotine equivalents (TNE). The relationship between TNE and e-cigarette use, e-cigarette brands, e-liquid nicotine strength, e-cigarette dependence, and urine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL), as a measure of combustible cigarette exposure, were examined. Of 100 participants (64% male, 71% in the 18-34 age group, 45% white), 53 used JUUL primarily, 12 used Puff Bar primarily, and 35 used other brands, including Suorin; 48 participants reported current cigarette smoking. Participants most often reported use of e-liquid with 4.5%-6.0% nicotine (68%), fruit (35%), tobacco (28%), and menthol or mint flavors (26%), used e-cigarettes on 25.5 (SD = 6.3) days a month, 10.2 (SD = 14.2) times a day, and 40% used 1-2 pods/cartridges per week. In bivariate analysis, urinary TNE was higher with greater frequency (days used) and intensity (number of pods used) of e-cigarette use, e-cigarette dependence, and combustible cigarette use. In multivariable analysis, days of e-cigarette use in the last 30 days, number of pods used per week, and NNAL levels were significantly associated with TNE. There was no significant impact of e-liquid nicotine strength on TNE. Nicotine intake among people who used pod-mod e-cigarettes increased with e-cigarette consumption and e-cigarette dependence, but not with e-liquid nicotine strength. Our findings may inform whether FDA adopts a nicotine standard for e-cigarettes. The study examined how device and user characteristics influence nicotine intake among pod-mod e-cigarette users. Nicotine intake increased with frequency (days of e-cigarette use in past 30 days) intensity of use (number of pods used per day) and e-cigarette dependence but not with the flavor or nicotine concentration of the e-liquids. Regulation of nicotine concentration of e-liquids is unlikely to influence nicotine exposure among adult experienced pod-mod users.

Research on prediction of e-commerce repurchase behavior based on multiple fusion models

With the advent of the Internet era, online shopping has become an integral part of peoples life. In order to perform precision marketing, more and more e-commerce platforms are trying to predict users repurchase behaviors by collecting massive user behavior data. Although the traditional single-model prediction method is mature, it is still difficult to improve the accuracy of prediction. Based on the real user behavior data of Tmall, this paper focuses on comparing and exploring the help of different algorithm fusion methods to improve the model prediction effect. The under-sampling method is introduced for sample equalization processing. User behavior features are constructed from three aspects which are user, merchant and user-merchant interaction. Taking AUC value as evaluation method, Soft-Voting and Stacking model fusion methods are used to integrate logistics regression, KNN, XGBoost and RandomForest. And the prediction results is produced based on stratified 5-fold cross-validation. The experimental results show that the fusion model can effectively improve the prediction effect, and the AUC value is raised by 0.2%~4% compared with the single model. The AUC value of Soft-Voting increases by approximately 0.4% after it is weighted.

Emotion Analysis and Opinion Monitoring of Social Network Users Under Deep Convolutional Neural Network

With the development of the internet, the user behavior and emotional characteristics behind social networks have attracted scholars' attention. Meanwhile, identifying user emotion can promote the development of mobile communication technology and network intelligence industrialization. Based on this, this work explores the emotions of social network users and discusses the public comments on the speeches through the speeches of social network users. After 100 times of training, F1 of the BiLSTM algorithm can reach 97.32%, and after 100 times of training, its function loss can be reduced to 1.33%, which can reduce the impact of function loss on emotion recognition. The exploration is of great significance for analyzing the emotional behavior of social network users and provides a reference for the intelligent and systematic development of internet social model as well as the information management.

Evolution analysis of community members for dynamic bitcoin transaction network

The collective behaviors of community members in dynamic bitcoin transaction network are significant to understand the evolutionary characteristics of communities for bitcoin transaction network. In this paper, we empirically investigate the behavior evolution of new nodes forming communities for the bitcoin transaction network. First, we divide the bitcoin transaction network into multiple time segments, and detect community on each time segment. Then, according to the set similarity method, we mark the community with maximal similarity [Formula: see text] at adjacent timestamps as the new community. Finally, we propose an evolution index to illustrate the evolution trend of new nodes forming communities, and introduce the reshuffle model to compare with it. The results show that there are obvious differences in the early stage, and new traders tend to join new communities. However, after August 2011, the trends of before and after reorganization are very similar, which indicates that in bitcoin trading, the behaviors of new traders forming communities become random. Our work may be helpful for the understanding of user behavior characteristics in bitcoin trading, and provide a new perspective for the research of bitcoin transaction network.