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Article
Publication date: 24 April 2020

Xiufeng Cheng, Ziming Zhang, Yue Yang and Zhonghua Yan

Social coding platforms (SCPs) have been adopted by scores of developers in building, testing and managing their codes collaboratively. Accordingly, this type of platform (site…

485

Abstract

Purpose

Social coding platforms (SCPs) have been adopted by scores of developers in building, testing and managing their codes collaboratively. Accordingly, this type of platform (site) enables collaboration between enterprises and universities (c-EU) at a lower cost in the form of online team-building projects (repositories). This paper investigates the open collaboration patterns between these two parties on GitHub by measuring their online behaviours. The purpose of this investigation is to identify the most attractive collaboration features that enterprises can offer to increase university students' participation intentions.

Design/methodology/approach

The research process is divided into four steps. First, the authors crawled for numerical data for each interactive repository feature created by employees of Alibaba on GitHub and identified the student accounts associated with these repositories. Second, a categorisation schema of feature classification was proposed on a behavioural basis. Third, the authors clustered the aforementioned repositories based on feature data and recognised four types of repositories (popular, formal, normal and obsolete) to represent four open collaboration patterns. The effects of the four repository types on university students' collaboration behaviour were measured using a multiple linear regression model. An ANOVA test was implemented to examine the robustness of research results. Finally, the authors proposed some practical suggestions to enhance collaboration between both sides of SCPs.

Findings

Several counterintuitive but reasonable findings were revealed, for example, those based on the “star” repository feature. The actual coding contribution of the repositories had a negative correlation with student attention. This result indicates that students were inclined to imitate rather than innovate.

Originality/value

This research explores the open collaboration patterns between enterprises and universities on GitHub and their impact on student coding behaviour. According to the research analysis, both parties benefit from open collaboration on SCPs, and the allocation or customisation of online repository features may affect students' participation in coding. This research brings a new perspective to the measurement of users' collaboration behaviour with output rates on SCPs.

Details

Internet Research, vol. 30 no. 4
Type: Research Article
ISSN: 1066-2243

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Article
Publication date: 8 September 2022

Ziming Zeng, Tingting Li, Jingjing Sun, Shouqiang Sun and Yu Zhang

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the…

343

Abstract

Purpose

The proliferation of bots in social networks has profoundly affected the interactions of legitimate users. Detecting and rejecting these unwelcome bots has become part of the collective Internet agenda. Unfortunately, as bot creators use more sophisticated approaches to avoid being discovered, it has become increasingly difficult to distinguish social bots from legitimate users. Therefore, this paper proposes a novel social bot detection mechanism to adapt to new and different kinds of bots.

Design/methodology/approach

This paper proposes a research framework to enhance the generalization of social bot detection from two dimensions: feature extraction and detection approaches. First, 36 features are extracted from four views for social bot detection. Then, this paper analyzes the feature contribution in different kinds of social bots, and the features with stronger generalization are proposed. Finally, this paper introduces outlier detection approaches to enhance the ever-changing social bot detection.

Findings

The experimental results show that the more important features can be more effectively generalized to different social bot detection tasks. Compared with the traditional binary-class classifier, the proposed outlier detection approaches can better adapt to the ever-changing social bots with a performance of 89.23 per cent measured using the F1 score.

Originality/value

Based on the visual interpretation of the feature contribution, the features with stronger generalization in different detection tasks are found. The outlier detection approaches are first introduced to enhance the detection of ever-changing social bots.

Details

Data Technologies and Applications, vol. 57 no. 2
Type: Research Article
ISSN: 2514-9288

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Article
Publication date: 29 August 2023

Qingqing Li, Ziming Zeng, Shouqiang Sun, Chen Cheng and Yingqi Zeng

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant…

447

Abstract

Purpose

The paper aims to construct a spatiotemporal situational awareness framework to sense the evolutionary situation of public opinion in social media, thus assisting relevant departments in formulating public opinion control measures for specific time and space contexts.

Design/methodology/approach

The spatiotemporal situational awareness framework comprises situational element extraction, situational understanding and situational projection. In situational element extraction, the data on the COVID-19 vaccine, including spatiotemporal tags and text contents, is extracted. In situational understanding, the bidirectional encoder representation from transformers – latent dirichlet allocation (BERT-LDA) and bidirectional encoder representation from transformers – bidirectional long short-term memory (BERT-BiLSTM) are used to discover the topics and emotional labels hidden in opinion texts. In situational projection, the situational evolution characteristics and patterns of online public opinion are uncovered from the perspective of time and space through multiple visualisation techniques.

Findings

From the temporal perspective, the evolution of online public opinion is closely related to the developmental dynamics of offline events. In comparison, public views and attitudes are more complex and diversified during the outbreak and diffusion periods. From the spatial perspective, the netizens in hotspot areas with higher discussion volume are more rational and prefer to track the whole process of event development, while the ones in coldspot areas with less discussion volume pay more attention to the expression of personal emotions. From the perspective of intertwined spatiotemporal, there are differences in the focus of attention and emotional state of netizens in different regions and time stages, caused by the specific situations they are in.

Originality/value

The situational awareness framework can shed light on the dynamic evolution of online public opinion from a multidimensional perspective, including temporal, spatial and spatiotemporal perspectives. It enables decision-makers to grasp the psychology and behavioural patterns of the public in different regions and time stages and provide targeted public opinion guidance measures and offline event governance strategies.

Details

The Electronic Library , vol. 41 no. 5
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 15 September 2022

Ziming Liu, Rui Hu and Xiaojun Bi

The prevalence of digital reading and the widespread use of social media among young people demands systematic exploration of the effects of social media addiction on students'…

1813

Abstract

Purpose

The prevalence of digital reading and the widespread use of social media among young people demands systematic exploration of the effects of social media addiction on students' reading practice. This paper aims to explore the effects of social media addiction on reading preferences, in-depth reading and sustained attention.

Design/methodology/approach

Survey and analysis methods are employed.

Findings

For many, social media provides an ideal platform of connection and expression; however, prolonged social media use holds the danger of becoming a behavioral addiction that threatens to undermine one's reading practice. Social media use tends to have a more significant impact on leisure reading than on academic reading. Obsessive engagement with social media hurts reading concentration and in-depth reading. While a majority (70.4%) of those surveyed believed that chronic social media use carries more harm than good on their learning, only half (50.1%) agreed or strongly agreed that today's students are too indulged in social media and need forceful control of it.

Originality/value

Implications of the effects of social media on reading practice are discussed, and directions for future research are suggested. It is likely that social media will continue to be seductive, attracting new generations of young people. Future research should explore prevention strategies.

Details

Journal of Documentation, vol. 79 no. 3
Type: Research Article
ISSN: 0022-0418

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Article
Publication date: 7 February 2025

Qingqing Li, Ziming Zeng, Shouqiang Sun and Tingting Li

Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the…

11

Abstract

Purpose

Aspect category-based sentiment analysis (ACSA) has been widely used in consumer preference mining and marketing strategy formulation. However, existing studies ignore the variability in features and the intrinsic correlation among diverse aspect categories in ACSA tasks. To address these problems, this paper aims to propose a novel integrated framework.

Design/methodology/approach

The integrated framework consists of three modules: text feature extraction and fusion, adaptive feature selection and category-aware decision fusion. First, text features from global and local views are extracted and fused to comprehensively capture the potential information in the different dimensions of the review text. Then, an adaptive feature selection strategy is devised for each aspect category to determine the optimal feature set. Finally, considering the intrinsic associations between aspect categories, a category-aware decision fusion strategy is constructed to enhance the performance of ACSA tasks.

Findings

Comparative experimental results demonstrate that the integrated framework can effectively detect aspect categories and their corresponding sentiment polarities from review texts, achieving a macroaveraged F1 score (Fmacro) of 72.38% and a weighted F1 score (F1) of 79.39%, with absolute gains of 2.93% to 27.36% and 4.35% to 20.36%, respectively, compared to the baselines.

Originality/value

This framework can simultaneously detect aspect categories and corresponding sentiment polarities from review texts, thereby assisting e-commerce enterprises in gaining insights into consumer preferences, prioritizing product improvements, and adjusting marketing strategies.

Details

The Electronic Library, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 22 June 2023

Jingjing Sun, Ziming Zeng, Tingting Li and Shouqiang Sun

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic…

307

Abstract

Purpose

The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

Design/methodology/approach

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

Findings

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

Originality/value

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

Details

Library Hi Tech, vol. 42 no. 6
Type: Research Article
ISSN: 0737-8831

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Article
Publication date: 17 May 2021

Ziming Zeng, Yu Shi, Lavinia Florentina Pieptea and Junhua Ding

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects…

313

Abstract

Purpose

Aspects extracted from the user’s historical records are widely used to define user’s fine-grained preferences for building interpretable recommendation systems. As the aspects were extracted from the historical records, the aspects that represent user’s negative preferences cannot be identified because of their absence from the records. However, these latent aspects are also as important as those aspects representing user’s positive preferences for building a recommendation system. This paper aims to identify the user’s positive preferences and negative preferences for building an interpretable recommendation.

Design/methodology/approach

First, high-frequency tags are selected as aspects to describe user preferences in aspect-level. Second, user positive and negative preferences are calculated according to the positive and negative preference model, and the interaction between similar aspects is adopted to address the aspect sparsity problem. Finally, an experiment is designed to evaluate the effectiveness of the model. The code and the experiment data link is: https://github.com/shiyu108/Recommendation-system

Findings

Experimental results show the proposed approach outperformed the state-of-the-art methods in widely used public data sets. These latent aspects are also as important as those aspects representing the user’s positive preferences for building a recommendation system.

Originality/value

This paper provides a new approach that identifies and uses not only users’ positive preferences but also negative preferences, which can capture user preference precisely. Besides, the proposed model provides good interpretability.

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Article
Publication date: 16 February 2022

Ziming Zeng, Shouqiang Sun, Jingjing Sun, Jie Yin and Yueyan Shen

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users…

292

Abstract

Purpose

Dunhuang murals are rich in cultural and artistic value. The purpose of this paper is to construct a novel mobile visual search (MVS) framework for Dunhuang murals, enabling users to efficiently search for similar, relevant and diversified images.

Design/methodology/approach

The convolutional neural network (CNN) model is fine-tuned in the data set of Dunhuang murals. Image features are extracted through the fine-tuned CNN model, and the similarities between different candidate images and the query image are calculated by the dot product. Then, the candidate images are sorted by similarity, and semantic labels are extracted from the most similar image. Ontology semantic distance (OSD) is proposed to match relevant images using semantic labels. Furthermore, the improved DivScore is introduced to diversify search results.

Findings

The results illustrate that the fine-tuned ResNet152 is the best choice to search for similar images at the visual feature level, and OSD is the effective method to search for the relevant images at the semantic level. After re-ranking based on DivScore, the diversification of search results is improved.

Originality/value

This study collects and builds the Dunhuang mural data set and proposes an effective MVS framework for Dunhuang murals to protect and inherit Dunhuang cultural heritage. Similar, relevant and diversified Dunhuang murals are searched to meet different demands.

Details

The Electronic Library , vol. 40 no. 3
Type: Research Article
ISSN: 0264-0473

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Article
Publication date: 18 May 2021

Zhirui Wang, Yezhuo Li, Bo Su, Lei Jiang, Ziming Zhao and Yan-An Yao

The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable…

244

Abstract

Purpose

The purpose of this paper is to introduce a tetrahedral mobile robot with only revolute joints (TMRR). By using rotation actuators, the mechanism of the robot gains favorable working space and eliminates the engineering difficulties caused by the multilevel extension compared with liner actuators. Furthermore, the rolling locomotion is improved to reduce displacement error based on dynamics analysis.

Design/methodology/approach

The main body of deforming mechanism with a tetrahedral exterior shape is composed of four vertexes and six RRR chains. The mobile robot can achieve the rolling locomotion and reach any position on the ground by orderly driving the rotation actuators. The global kinematics of the mobile modes are analyzed. Dynamics analysis of the robot falling process is carried out during the rolling locomotion, and the rolling locomotion is improved by reducing the collision impulse along with the moving direction.

Findings

Based on global kinematics analysis of TMRR, the robot can realize the continuous mobility based on rolling gait planning. The main cause of robot displacement error and the corresponding improvement locomotion are gained through dynamic analysis. The results of the theoretical analysis are verified by experiments on a physical prototype.

Originality/value

The work introduced in this paper is a novel exploration of applying the mechanism with only revolute joints to the field of tetrahedral rolling robots. It is also an attempt to use the improved rolling locomotion making this kind of mobile robot more practical. Meanwhile, the reasonable engineering structure of the robot provides feasibility for load carrying.

Details

Industrial Robot: the international journal of robotics research and application, vol. 48 no. 4
Type: Research Article
ISSN: 0143-991X

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Article
Publication date: 26 January 2022

Ziming Zeng, Shouqiang Sun, Tingting Li, Jie Yin and Yueyan Shen

The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search…

611

Abstract

Purpose

The purpose of this paper is to build a mobile visual search service system for the protection of Dunhuang cultural heritage in the smart library. A novel mobile visual search model for Dunhuang murals is proposed to help users acquire rich knowledge and services conveniently.

Design/methodology/approach

First, local and global features of images are extracted, and the visual dictionary is generated by the k-means clustering. Second, the mobile visual search model based on the bag-of-words (BOW) and multiple semantic associations is constructed. Third, the mobile visual search service system of the smart library is designed in the cloud environment. Furthermore, Dunhuang mural images are collected to verify this model.

Findings

The findings reveal that the BOW_SIFT_HSV_MSA model has better search performance for Dunhuang mural images when the scale-invariant feature transform (SIFT) and the hue, saturation and value (HSV) are used to extract local and global features of the images. Compared with different methods, this model is the most effective way to search images with the semantic association in the topic, time and space dimensions.

Research limitations/implications

Dunhuang mural image set is a part of the vast resources stored in the smart library, and the fine-grained semantic labels could be applied to meet diverse search needs.

Originality/value

The mobile visual search service system is constructed to provide users with Dunhuang cultural services in the smart library. A novel mobile visual search model based on BOW and multiple semantic associations is proposed. This study can also provide references for the protection and utilization of other cultural heritages.

Details

Library Hi Tech, vol. 40 no. 6
Type: Research Article
ISSN: 0737-8831

Keywords

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