Analyzing topics in social media for improving digital twinning based product development

Digital twinning enables manufacturers to create digital representations of physical entities,thus implementing virtual simulations for product development.Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages,failing to cover the gap between physical and digital spaces.This work mines real-world consumer feedbacks through social media topics,which is significant to product development.We specifically analyze the prevalent time of a product topic,giving an insight into both consumer attention and the widely-discussed time of a product.The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution.Therefore,these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics.To this end,this work combines deep learning and survival analysis to predict the prevalent time of topics.We propose a specialized deep survival model which consists of two modules.The first module enriches input covariates by incorporating latent features of the time-varying text,and the second module fully captures the temporal pattern of a rumor by a recurrent network structure.Moreover,a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction.Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

Profile of Reliability Engineering & System Safety in the 21 years of the 21st century:An informetric-based analysis

The objective of this study is to provide a case study of informetric and bibliometric analysis,by building up a profile for the journal of Reliability Engineering&System Safety in the 21st century,based on the data collected in Web of Science and the tool of VOSViewer.4821 articles published in the journal in 2001-2021 have been adopted as the dataset.The keywords of these articles are analyzed and clustered,the main applications of these studies are identified,and the temporal development trend of the topics are discussed.The most productive countries/regions,institutions and individual researchers for the journal are presented and the collaboration relationships at the national and institutional levels are investigated and visualized.Distribution of author genders is surveyed based on a sample.Then,the citation situation of articles in the journal is analyzed,and the potential impact factors on citations,including number of authors,number of participating institutions and countries/regions,number of references,and topics are studied.Finally,evidence on whether open access can influence citations of articles is provided.Readers of this article are expected to understand more about how bibliometric analysis support journal analysis and development analysis in a certain domain.

Survey on Data Analysis in Social Media:A Practical Application Aspect

Social media has more than three billion users sharing events,comments,and feelings throughout the world.It serves as a critical information source with large volumes,high velocity,and a wide variety of data.The previous studies on information spreading,relationship analyzing,and individual modeling,etc.,have been heavily conducted to explore the tremendous social and commercial values of social media data.This survey studies the previous literature and the existing applications from a practical perspective.We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques,such as topic analysis,time series analysis,sentiment analysis,and network analysis.After that,we present the impacts of such applications in three different areas,including disaster management,healthcare,and business.Finally,we list existing challenges and suggest promising future research directions in terms of data privacy,5 G wireless network,and multilingual support.

When Factorization Meets Heterogeneous Latent Topics： An Interpretable Cross-Site Recommendation Framework

Data sparsity is a well-known challenge in recommender systems. Previous studies alleviate this problem by incorporating the information within the corresponding social media site. In this paper, we solve this challenge by exploring cross-site information. Specifically, we examine： 1） how to effectively and efficiently utilize cross-site ratings and content features to improve recommendation performance and 2） how to make the recommendation interpretable by utilizing content features. We propose a joint model of matrix factorization and latent topic analysis. Heterogeneous content features are modeled by multiple kinds of latent topics. In addition, the combination of matrix factorization and latent topics makes the recommendation result interpretable. Therefore, the above two issues are simultaneously solved. Through a real-world data.set, where user behaviors in three social media sites are collected, we demonstrate that the proposed model is effective in improving recommendation performance and interpreting the rationale of ratings.

TopicBubbler:An interactive visual analytics system for cross-level fine-grained exploration of social media data

How to explore fine-grained but meaningful information from the massive amount of social media data is critical but challenging.To address this challenge,we propose the TopicBubbler,a visual analytics system that supports the cross-level fine-grained exploration of social media data.To achieve the goal of cross-level fine-grained exploration,we propose a new workflow.Under the procedure of the workflow,we construct the fine-grained exploration view through the design of bubble-based word clouds.Each bubble contains two rings that can display information through different levels,and recommends six keywords computed by different algorithms.The view supports users collecting information at different levels and to perform fine-grained selection and exploration across different levels based on keyword recommendations.To enable the users to explore the temporal information and the hierarchical structure,we also construct the Temporal View and Hierarchical View,which satisfy users to view the cross-level dynamic trends and the overview hierarchical structure.In addition,we use the storyline metaphor to enable users to consolidate the fragmented information extracted across levels and topics and ultimately present it as a complete story.Case studies from real-world data confirm the capability of the TopicBubbler from different perspectives,including event mining across levels and topics,and fine-grained mining of specific topics to capture events hidden beneath the surface.