Article
Automation & Control Systems
Rosa Sicilia, Mario Merone, Roberto Valenti, Paolo Soda
Summary: The past decades have seen a radical change in the way information spreads, leading to increased importance of rumour detection. Current research focuses mainly on automatic rumour detection at the conversation level, with limited efforts directed towards single post (micro-level) analysis. This study introduces a novel feature selection approach that significantly enhances accuracy in rumour detection.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2021)
Article
Computer Science, Information Systems
Fatimah Alhayan, Diane Pennington, Sarra Ayouni
Summary: This study examines the engagement and interaction of different dementia communities on Twitter. By using machine learning and social network analysis, the study identifies influential communities and explores the presence of suspected social bots.
ONLINE INFORMATION REVIEW
(2023)
Article
Business
Mohsan Ali, Mehdi Hassan, Kashif Kifayat, Jin Young Kim, Saqib Hakak, Muhammad Khurram Khan
Summary: Social cybermedia has revolutionized information sharing and propagation, creating virtual communities that can disseminate both positive and negative content. The spread of hate content within these communities poses a serious threat to social order and stability, necessitating the development of methods for classification and identification of influential individuals. This study utilizes deep learning and graph-based approaches to effectively identify and monitor hate content and communities on Twitter. The proposed LSTM-GRU model achieves an impressive accuracy of 98.14% in classifying hate content, while the Girvan-Newman algorithm successfully detects influential individuals and intraclass communities. This model has significant implications for the prevention and mitigation of societal unrest.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Computer Science, Artificial Intelligence
Isa Inuwa-Dutse, Mark Liptrott, Ioannis Korkontzelos
Summary: This study introduces a new multi-level clustering technique to identify related communities in social networks by leveraging structural and textual information to identify microcosms. Experimental evaluation demonstrates the effectiveness of the approach and offers a new dimension for research, aiding in a better understanding of community evolution and behavior on Twitter.
Review
Computer Science, Information Systems
Norah Alotaibi, Delel Rhouma
Summary: This article provides an overview of the characteristics and challenges of community detection in dynamic social networks, and compares state-of-the-art methods. Researchers can use this survey to find the best methods and choose relevant future directions.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Geography
Wayne Williamson
Summary: This study focuses on the use of Twitter at academic conferences, analysing Twitter data through social network analysis techniques and online surveys to examine subgroups and the roles of highly connected users in the network.
GEOGRAPHICAL RESEARCH
(2021)
Article
Computer Science, Information Systems
Dazhao Xu, Yunliang Chen, Ningning Cui, Jianxin Li
Summary: This paper focuses on the problem of detecting communities in location-based social networks (LBSN), and proposes a method called Multi-dimensional Similarity Information Fusion for Community Detection (MFCD). By utilizing the hidden knowledge in LBSN, multiple kinds of knowledge-aware similarities are defined and a set of evaluation metrics specifically for LBSN are established. Experimental results demonstrate the excellent performance of the proposed method.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jie Chen, Nan Song, Yansen Su, Shu Zhao, Yanping Zhang
Summary: This study designed a novel approach to analyze the sentiment orientation of users in social networks, improving sentiment analysis performance by integrating user interactions and opinion data.
INFORMATION SCIENCES
(2022)
Article
Computer Science, Artificial Intelligence
Hongtao Liu, Jiahao Wei, Tianyi Xu
Summary: In this paper, a new community detection method called CPGC is proposed, which combines the community perspective and graph convolution network to address the challenges of overlapping communities in attributed networks. CPGC achieves state-of-the-art results in nonoverlapping or overlapping communities, as demonstrated by experiments on various real-world networks.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Health Care Sciences & Services
Diana Ramirez-Cifuentes, Ana Freire, Ricardo Baeza-Yates, Nadia Sanz Lamora, Aida Alvarez, Alexandre Gonzalez-Rodriguez, Meritxell Lozano Rochel, Roger Llobet Vives, Diego Alejandro Velazquez, Josep Maria Gonfaus, Jordi Gonzalez
Summary: This study characterized Spanish-speaking users showing anorexia signs on Twitter by analyzing behavioral, demographical, relational, and multimodal data. Significant differences were found between users at each stage of the recovery process and control groups, indicating variations in posting patterns and behaviors among users with AN.
JOURNAL OF MEDICAL INTERNET RESEARCH
(2021)
Article
Computer Science, Information Systems
Shunjie Yuan, Hefeng Zeng, Ziyang Zuo, Chao Wang
Summary: This study proposes an overlapping community detection method CDMG based on Graph Convolutional Networks (GCN) to maximize the Markov stability of community structure. Extensive experiments demonstrate the superiority of CDMG compared to other established community detection algorithms. The performance of CDMG can be further improved by utilizing the optimal Markov time, which is found using a trichotomy-based method based on the influence of Markov time on CDMG performance.
COMPUTER COMMUNICATIONS
(2023)
Article
Computer Science, Artificial Intelligence
Marco De Luca, Anna Rita Fasolino, Antonino Ferraro, Vincenzo Moscato, Giancarlo Sperli, Porfirio Tramontana
Summary: In this paper, a novel heterogeneous graph-based model is proposed to capture and handle the complex and strongly-correlated information of a software Developer Social Network (DSN) for analytic tasks. The problem of automatically discovering communities of software developers sharing interests for similar projects is addressed using Social Network Analysis (SNA) findings, and graph embedding techniques are utilized to overcome the large graph size. The proposed approach is evaluated against state-of-the-art approaches in terms of efficiency and effectiveness using the GitHub dataset.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Information Systems
Zhe Chen, Aixin Sun, Xiaokui Xiao
Summary: The article introduces an incremental community detection framework inc-AGGMMR, which maps attributes into the network by constructing an augmented graph and balances the contribution between attribute and topological information through weight adjustment.
ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA
(2021)
Article
Computer Science, Information Systems
Guixun Luo, Zhiyuan Zhang, Sumeng Diao
Summary: User interaction plays a vital role in network public opinion formation, but the physical phenomena of individual interaction are difficult to describe and explain mathematically. By studying micro individuals and their interaction process, we found that friends with higher preference have a higher frequency of interaction compared to friends with lower preference. We established a mathematical model to describe user interaction behavior and analyzed its distribution and time characteristics through theoretical analysis, simulation experiments, and empirical analysis of Weibo data.
INFORMATION SCIENCES
(2022)
Article
Social Issues
Karin Moshkovitz, Tsahi Hayat
Summary: This study examined the relationship between personality, Twitter activity, number of followers, and social capital, finding correlations between Twitter use frequency, extroversion, and perceived online social capital. It also showed that extroverts gain more social capital through online followings and offline sociability. These results support the 'Rich get Richer' model in the context of social networking sites.
TECHNOLOGY IN SOCIETY
(2021)
Article
Psychiatry
Simone N. Vigod, Ebrahim Bagheri, Fattane Zarrinkalam, Hilary K. Brown, Muhammad Mamdani, Joel G. Ray
JOURNAL OF PSYCHOSOMATIC RESEARCH
(2018)
Article
Computer Science, Software Engineering
Mandi Bashari, Ebrahim Bagheri, Weichang Du
JOURNAL OF SYSTEMS AND SOFTWARE
(2018)
Article
Computer Science, Artificial Intelligence
Maryam Khodabakhsh, Mohsen Kahani, Ebrahim Bagheri, Zeinab Noorian
KNOWLEDGE-BASED SYSTEMS
(2018)
Article
Computer Science, Information Systems
J. Kent Poots, Ebrahim Bagheri
Article
Computer Science, Information Systems
Duc-Thuan Vo, Ebrahim Bagheri
INFORMATION PROCESSING & MANAGEMENT
(2019)
Article
Computer Science, Information Systems
Fatemeh Lashkari, Ebrahim Bagheri, Ali A. Ghorbani
INFORMATION PROCESSING & MANAGEMENT
(2019)
Article
Computer Science, Information Systems
Seyed Amin Mirlohi Falavarjani, Fattane Zarrinkalam, Jelena Jovanovic, Ebrahim Bagheri, Ali A. Ghorbani
INFORMATION PROCESSING & MANAGEMENT
(2019)
Article
Computer Science, Information Systems
Hawre Hosseini, Mehran Mansouri, Ebrahim Bagheri
Summary: This paper focuses on identifying whether textual social content includes implicit mentions of knowledge graph entities or not, forming a two-class classification problem. The authors adopt the systemic functional linguistic framework and introduce two classes of features, syntagmatic and paradigmatic features, for implicit entity recognition. Through experiments, they demonstrate the utility of these features for the task and provide a detailed error analysis.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Computer Science, Information Systems
Maryam Khodabakhsh, Ebrahim Bagheri
Summary: This paper proposes a multi-task learning approach, called Multi-task Query Performance Prediction Framework (M-QPPF), which simultaneously performs ad hoc retrieval and query performance prediction tasks. The authors use a shared BERT layer to learn embedding representations for query-document pairs, and include separate layers to capture task-specific characteristics. Experimental results show that the method improves up to 18.8% on the MS MARCO dataset, with a Pearson correlation of 0.604 in the query performance prediction task, outperforming any state-of-the-art baseline.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Cybernetics
Amin Mirlohi, Jalehsadat Mahdavimoghaddam, Jelena Jovanovic, Feras N. Al-Obeidat, Mehdi Khani, Ali A. Ghorbani, Ebrahim Bagheri
Summary: This study explores the impact of social contagion on social alignment and finds that a user's decision to socially align or distance from social topics and sentiments influences the social alignment decisions of their connections on the social network. It further shows that population heterogeneity significantly impacts such social alignment decisions.
IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS
(2022)
Proceedings Paper
Computer Science, Information Systems
Fattane Zarrinkalam, Hossein Fani, Ebrahim Bagheri
PROCEEDINGS OF THE 42ND INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '19)
(2019)
Proceedings Paper
Computer Science, Information Systems
Fattane Zarrinkalam, Hossein Fani, Ebrahim Bagheri
KDD'19: PROCEEDINGS OF THE 25TH ACM SIGKDD INTERNATIONAL CONFERENCCE ON KNOWLEDGE DISCOVERY AND DATA MINING
(2019)
Article
Computer Science, Artificial Intelligence
Hossein Fani, Ebrahim Bagheri, Fattane Zarrinkalam, Xin Zhao, Weichang Du
COMPUTATIONAL INTELLIGENCE
(2018)
Article
Computer Science, Information Systems
Sang-Bing Tsai, Xusen Cheng, Yanwu Yang, Jason Xiong, Alex Zarifis
Summary: This article structurally concludes the methods proposed and evidenced to develop digital entrepreneurship from a socio-technical perspective. The technology itself and the process of utilization should be carefully considered. From a social perspective, fulfilling the needs of customers in social interaction and nurturing characteristics and social skills for the digital work environment are crucial.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xiaochang Fang, Hongchen Wu, Jing Jing, Yihong Meng, Bing Yu, Hongzhu Yu, Huaxiang Zhang
Summary: This study proposes a novel fake news detection framework, utilizing news semantic environment perception (NSEP) to identify fake news content. The framework consists of steps such as dividing the semantic environment into macro and micro levels, applying graph convolutional networks, and utilizing multihead attention. Empirical experiments show that the NSEP framework achieves high accuracy in detecting Chinese fake news, outperforming other baseline methods and highlighting the importance of both micro and macro semantic environments in early detection of fake news.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xudong Sun, Alladoumbaye Ngueilbaye, Kaijing Luo, Yongda Cai, Dingming Wu, Joshua Zhexue Huang
Summary: This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm to address the data scalability and flexibility issues in basket analysis in the big data era. Experiment results demonstrate that the ScaDistFIM algorithm is more efficient compared to the Spark FP-Growth algorithm.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Boxu Guan, Xinhua Zhu, Shangbo Yuan
Summary: This paper aims to improve the interpretability of machine reading comprehension models by utilizing the pre-trained T5 model for evidence inference. They propose an interpretable reading comprehension model based on T5, which is trained on a more accurate evidence corpus and can infer precise interpretations for answers. Experimental results show that their model outperforms the baseline BERT model on the SQuAD1.1 task.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Yanhao Wang, Baohua Zhang, Weikang Liu, Jiahao Cai, Huaping Zhang
Summary: In this study, we propose a data augmentation-based semantic text matching model called STMAP. By using Gaussian noise and noise mask signal for data augmentation, as well as employing an adaptive optimization network for training target optimization, our model achieves good performance in few-shot learning and semantic deviation problems.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Jiahao Yang, Shuo Feng, Wenkai Zhang, Ming Zhang, Jun Zhou, Pengyuan Zhang
Summary: To pursue profit from stock markets, researchers utilize deep learning methods to forecast asset price movements. However, there are two issues in current research, the discrepancy between forecasting results and profits, and heavy reliance on prior knowledge. To address these issues, researchers propose a novel optimization objective and modeling method, and conduct experiments to validate their approach.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Heng Zhang, Chengzhi Zhang, Yuzhuo Wang
Summary: This study provides an accurate analysis of technology development in the field of Natural Language Processing (NLP) from an entity-centric perspective. The findings indicate an increase in the average number of entities per paper, with pre-trained language models becoming mainstream and the impact of Wikipedia dataset and BLEU metric continuing to rise. There has been a surge in popularity for new high-impact technologies in recent years, with researchers accepting them at an unprecedented speed.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Davide Buscaldi, Danilo Dessi, Enrico Motta, Marco Murgia, Francesco Osborne, Diego Reforgiato Recupero
Summary: In scientific papers, citing other articles is a common practice to support claims and provide evidence. This paper proposes two automatic methods using Transformer models to address citation placement, and achieves significant improvements in experiments.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Baozhuang Niu, Lingfeng Wang, Xinhu Yu, Beibei Feng
Summary: This paper examines whether the incumbent brand should adopt digital technology to forecast demand and adjust order decisions in the face of soaring demand for medical supply caused by frequent outbreaks of regional COVID-19 epidemic. The study finds that digital transformation can lead to a triple-win situation among the incumbent brand, social welfare, and consumer surplus, as well as bring benefits to the manufacturer. Furthermore, the research provides insights for firms' digital entrepreneurship decisions through theoretical optimization and data processing/policy simulation.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Xueyang Qin, Lishang Li, Fei Hao, Meiling Ge, Guangyao Pang
Summary: Image-text retrieval is important in connecting vision and language. This paper proposes a method that utilizes prior knowledge to enhance feature representations and optimize network training for better retrieval results.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Review
Computer Science, Information Systems
Gang Ren, Lei Diao, Fanjia Guo, Taeho Hong
Summary: This paper proposes a novel approach for predicting the helpfulness of reviews by utilizing both textual and image features. The proposed method considers the correlation between features through self-attention and co-attention mechanisms, and fuses multi-modal features for prediction. Experimental results demonstrate the superior performance of the proposed method compared to benchmark methods.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhongquan Jian, Jiajian Li, Qingqiang Wu, Junfeng Yao
Summary: Aspect-Level Sentiment Classification (ALSC) is a crucial challenge in Natural Language Processing (NLP). Most existing methods fail to consider the correlations between different instances, leading to a lack of global viewpoint. To address this issue, we propose a Retrieval Contrastive Learning (RCL) framework that extracts intrinsic knowledge across instances for improved instance representation. Experimental results demonstrate that training ALSC models with RCL leads to substantial performance improvements.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Ying Hu, Yanping Chen, Ruizhang Huang, Yongbin Qin, Qinghua Zheng
Summary: Biomedical relation extraction aims to extract the interactive relations between biomedical entities in a sentence. This study proposes a hierarchical convolutional model to address the semantic overlapping and data imbalance problems. The model encodes both local contextual features and global semantic dependencies, enhancing the discriminability of the neural network for biomedical relation extraction.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Zhou Yang, Yucai Pang, Xuehong Li, Qian Li, Shihong Wei, Rong Wang, Yunpeng Xiao
Summary: This study proposes a rumor detection model based on topic audiolization, which transforms the topic space into audio-like signals. Experimental results show that the model achieves significant performance improvements in rumor identification.
INFORMATION PROCESSING & MANAGEMENT
(2024)
Article
Computer Science, Information Systems
Alistair Moffat
Summary: This paper proposes the buying power metric for assessing the quality of product rankings on e-commerce sites. It discusses the relationship between the buying power metric and user reactions, and introduces an alternative product ranking effectiveness metric.
INFORMATION PROCESSING & MANAGEMENT
(2024)