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Use of online social networks (OSNs) undoubtedly brings the world closer. OSNs like Twitter provide a space for expressing one’s opinions in a public platform. This great potential is misused by the creation of bot accounts, which spread fake news and manipulate opinions. Hence, distinguishing genuine human accounts from bot accounts has become a pressing issue for researchers. In this paper, we propose a framework based on deep learning to classify Twitter accounts as either ‘human’ or ‘bot.’ We use the information from user profile metadata of the Twitter account like description, follower count and tweet count. We name the framework ‘DeeProBot,’ which stands for Deep Profile-based Bot detection framework. The raw text from the description field of the Twitter account is also considered a feature for training the model by embedding the raw text using pre-trained Global Vectors (GLoVe) for word representation. Using only the user profile-based features considerably reduces the feature engineering overhead compared with that of user timeline-based features like user tweets and retweets. DeeProBot handles mixed types of features including numerical, binary, and text data, making the model hybrid. The network is designed with long short-term memory (LSTM) units and dense layers to accept and process the mixed input types. The proposed model is evaluated on a collection of publicly available labeled datasets. We have designed the model to make it generalizable across different datasets. The model is evaluated using two ways: testing on a hold-out set of the same dataset; and training with one dataset and testing with a different dataset. With these experiments, the proposed model achieved AUC as high as 0.97 with a selected set of features.

Personalized search plays an important role in satisfying users’ information needs owing to its ability to build user profiles based on users’ search histories. Most of the existing personalized methods built dynamic user profiles by emphasizing query-related historical behaviors rather than treating each historical behavior equally. Sometimes, the ambiguity and short nature of the query make it difficult to understand the potential query intent exactly, and the query-centric user profiles built in these cases will be biased and inaccurate. In this work, we propose to leverage candidate documents, which contain richer information than the short query text, to help understand the query intent more accurately and improve the quality of user profiles afterward. Specifically, we intend to better understand the query intent through candidate documents, so that more relevant user behaviors from history can be selected to build more accurate user profiles. Moreover, by analyzing the differences between candidate documents, we can better control the degree of personalization on the ranking of results. This controlled personalization approach is also expected to further improve the stability of personalized search as blind personalization may harm the ranking results. We conduct extensive experiments on two datasets, and the results show that our model significantly outperforms competitive baselines, which confirms the benefit of utilizing candidate documents for personalized web search.

Mining user data and constructing web user profiles of older adults from the perspective of elderly services is conducive to understanding their behavioral habits, needs, and usage preferences on the web, which provides more targeted elderly care services. In this paper, IGA-SOMK +  + , which is a novel clustering method for constructing web user profiles of older adults, is proposed based on the China Family Panel Studies (CFPS) survey data, which include 6596 older adults aged greater than 60 years. The selected data aspects include basic information, work situation, health situation, living habits, and web use services. To describe the web user profiles of older adults, a hybrid method based on improved genetic algorithm (IGA) feature selection, self-organizing feature maps (SOM), and K-means +  + is proposed. Data on older adults’ web use behaviors are first processed, and IGA is used for feature selection based on the adaptive crossover and mutation probabilities. SOM is then used to determine the initial center vectors of K-means +  + for further clustering, which is referred to as SOMK +  + (SOM-K-means + +). The results of IGA-SOMK +  + are compared with those of the state-of-the-art methods, including the K-means, mini batch K-means, Agnes, K-modes, FCM, K-means +  + , SOMK +  + , and IHPSO-KM. In addition, the significance and robustness of IGA-SOMK +  + are analyzed. The experimental results show that the IGA feature selection reduces the influence of the redundant feature factors and improves the performance of the clustering algorithm. SOMK +  + overcomes the sensitivity of K-means to initial cluster centers. Moreover, IGA-SOMK +  + has the best clustering effect among the compared algorithms in terms of silhouette coefficient (SC), calinski-harabaz (CH) index, and davies-bouldin (DB) metrics. For example, it increases the SC from 0.280 to 0.629. Finally, by analyzing the results, the user group of older adults is segmented to perform the deep mining of CFPS data, which verifies the feasibility of the user profile model. This paper summarizes the basic situation of the current web access of older adults in China in terms of web use services, as well as the importance of the web in their lives and in the information channels. It also provides suggestions for the current problems of older adults in accessing the web.

Online social networks, as platforms for personal expression, have evolved into complex networks integrating political and social dimensions. This evolution has shifted the focus of network governance from addressing hacking activities to mitigating unpredictable social behaviors, such as the malicious manipulation of public opinion, the doxing of ordinary users, and cyberbullying. However, the sparsity of data and the concealed nature of user behavior pose significant challenges to existing network reconnaissance technologies. In this study, we focus on constructing user profiles on online social network platforms by extracting features to build deep user profiles based on behavioral patterns. Drawing inspiration from the 5Cs principle of credit evaluation, we refine it into a 3Cs principle tailored for user profiling on social network platforms and associate it with user behavioral patterns. To further analyze user behavior, a high-dimensional feature extraction method is proposed using an improved stacking ensemble learning model. Based on experimental data analysis, the most suitable base algorithms for high-dimensional feature extraction are identified. Experimental results demonstrate that the integration of high-dimensional features improved the behavior prediction accuracy of the profiling model by 9.26% on balanced datasets and enhanced the AUC (area under the curve) metric by 3.69% on imbalanced datasets. The proposed method effectively increases the depth and generalization performance of user profiling.

The user’s intent to seek online information has been an active area of research in user profiling. User profiling considers user characteristics, behaviors, activities, and preferences to sketch user intentions, interests, and motivations. Determining user characteristics can help capture implicit and explicit preferences and intentions for effective user-centric and customized content presentation. The user’s complete online experience in seeking information is a blend of activities such as searching, verifying, and sharing it on social platforms. However, a combination of multiple behaviors in profiling users has yet to be considered. This research takes a novel approach and explores user intent types based on multidimensional online behavior in information acquisition. This research explores information search, verification, and dissemination behavior and identifies diverse types of users based on their online engagement using machine learning. The research proposes a generic user profile template that explains the user characteristics based on the internet experience and uses it as ground truth for data annotation. User feedback is based on online behavior and practices collected by using a survey method. The participants include both males and females from different occupation sectors and different ages. The data collected is subject to feature engineering, and the significant features are presented to unsupervised machine learning methods to identify user intent classes or profiles and their characteristics. Different techniques are evaluated, and the K-Mean clustering method successfully generates five user groups observing different user characteristics with an average silhouette of 0.36 and a distortion score of 1136. Feature average is computed to identify user intent type characteristics. The user intent classes are then further generalized to create a user intent template with an Inter-Rater Reliability of 75%. This research successfully extracts different user types based on their preferences in online content, platforms, criteria, and frequency. The study also validates the proposed template on user feedback data through Inter-Rater Agreement process using an external human rater.

User profile clustering is the process of grouping users on social media sites based on common characteristics identified in their profile data, such as demographics, interests, and interactions. Profile clustering allows users to engage in targeted marketing, skill-matching, and collaborative networking by grouping them based on similar attributes, interests, or professional criteria. However, one key drawback of user profile clustering is its sensitivity to noisy and missing data, high-dimensional feature spaces, poor semantic understanding, and complexity limitations. To overcome these issues, a novel Hybrid Aquila Quantum Sooty tern optimization for clustering (AQST-ClustNet) approach based on user profiles (UP) has been proposed in this paper. The user profile data is preprocessed using NLP techniques involving data stemming, handling of missing data or values, removal of stop words, and data extraction for eliminating inappropriate data. A Hybrid Aquila Quantum Sooty Tern Optimization (HAQSTO) algorithm is employed for clustering the user profile into healthcare professionals, marketing professionals, software developers, and educators. The efficiency of the developed method is assessed employing various metrics, including Calinski–Harabasz score (CHS), Silhouette score (SHS), and Davies–Bouldin score (DBS). The proposed model achieves less runtime of 45 s, whereas the existing techniques, such as MCEMS, DBSTexC, and TSMIUSC-Miner, achieve runtimes of 70 s, 79 s, and 60 s. Using the effective dual-stage feature extraction and clustering approach, the complexity of clustering and a high-dimensional feature space is effectively reduced.

In the context of the digital age, traditional reading promotion models are no longer able to meet the diverse needs of modern users. In order to cultivate reading habits among college students and improve their reading rate, this study constructed a semantic user profile model for reading promotion, which deeply analyzed users' reading preferences, behavioral characteristics, and differences in needs and achieved precise reading promotion services. This study integrated sentiment analysis techniques to extract users' emotional tendencies toward reading materials, enabling a more refined understanding of user preferences and engagement. At the same time, this study further explored the innovative model of libraries collaborating with student clubs to improve the quality of library services and enhance effective reading promotion.

Since today's television can receive more and more programs, and televisions are often viewed by groups of people, such as a family or a student dormitory, this paper proposes a TV program recommendation strategy for multiple viewers based on user profile merging. This paper first introduces three alternative strategies to achieve program recommendation for multiple television viewers, discusses, and analyzes their advantages and disadvantages respectively, and then chooses the strategy based on user profile merging as our solution. The selected strategy first merges all user profiles to construct a common user profile, and then uses a recommendation approach to generate a common program recommendation list for the group according to the merged user profile. This paper then describes in detail the user profile merging scheme, the key technology of the strategy, which is based on total distance minimization. The evaluation results proved that the merging result can appropriately reflect the preferences of the majority of members within the group, and the proposed recommendation strategy is effective for multiple viewers watching TV together. [PUBLICATION ABSTRACT]

Purpose The purpose of this paper is to understand the similarities and differences between the Twitter users who tweeted on journal articles in psychology and political science disciplines. Design/methodology/approach The data were collected from Web of Science, Altmetric.com, and Twitter. A total of 91,826 tweets with 22,541 distinct Twitter user profiles for psychology discipline and 29,958 tweets with 10,478 distinct Twitter user profiles for political science discipline were used for analysis. The demographics analysis includes gender, geographic location, individual or organization user, academic or non-academic background, and psychology/political science domain knowledge background. A machine learning approach using support vector machine (SVM) was used for user classification based on the Twitter user profile information. Latent Dirichlet allocation (LDA) topic modeling was used to discover the topics that the users discussed from the tweets. Findings Results showed that the demographics of Twitter users who tweeted on psychology and political science are significantly different. Tweets on journal articles in psychology reflected more the impact of scientific research finding on the general public and attracted more attention from the general public than the ones in political science. Disciplinary difference in term of user demographics exists, and thus it is important to take the discipline into consideration for future altmetrics studies. Originality/value From this study, researchers or research organizations may have a better idea on who their audiences are, and hence more effective strategies can be taken by researchers or organizations to reach a wider audience and enhance their influence.

In the past two decades, recommender systems have been successfully applied in many e-commerce companies. One of the promising techniques to generate personalized recommendations is collaborative filtering. However, it suffers from sparsity problem. Alleviating this problem, cross-domain recommender systems came into existence in which transfer learning mechanism is applied to exploit the knowledge from other related domains. While applying transfer learning, some information should overlap between source and target domains. Several attempts have been made to enhance the performance of collaborative filtering with the help of other related domains in cross-domain recommender systems framework. Although exploiting the knowledge from other domains is still challenging and open problem in recommender systems. In this paper, we propose a method namely User Profile as a Bridge in Cross-domain Recommender Systems (UP-CDRSs) for transferring knowledge between domains through user profile. Firstly, we build a user profile using demographical information of a user, explicit ratings and content information of user-rated items. Thereafter, the probabilistic graphical model is employed to learn latent factors of users and items in both domains by maximizing posterior probability. At last prediction on unrated item is estimated by an inner product of corresponding latent factors of users and items. Validating of our proposed UP-CDRSs method, we conduct series of experiments on various sparsity levels using cross-domain dataset. The results demonstrate that our proposed method substantially outperforms other without and with transfer learning methods in terms of accuracy.