Forthcoming and Online First Articles

International Journal of Web Based Communities

International Journal of Web Based Communities (IJWBC)

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International Journal of Web Based Communities (17 papers in press)

Regular Issues

  • Exploring the impact of COVID-19 Pandemic and Vaccine Dissemination on Airbnb's Popularity and Sentiment on Twitter   Order a copy of this article
    by Sina Shokoohyar, Vahid Ghomi, Amirsalar Jafari Gorizi, Weimin Liang, Charlie Evert 
    Abstract: This study aims to quantify the sentiment of those discussing Airbnb on Twitter and visualise how this sentiment differed in three main periods: prior to the pandemic (pre-COVID-19), and during the pandemic before vaccines were disseminated (pre-vaccine), and during the pandemic, after vaccines were disseminated (post-vaccine). 344,705 tweets relating to Airbnb are collected. In this study, popularity, and usage analytics, sentiment analytics, voice analytics, and topic mining analytics were utilised. Through exploring the data in these three periods, it is possible to distinguish inverse correlations between the number of COVID-19 cases/deaths as compared to the popularity and positive sentiment of Airbnb-related tweets. Other findings include the topics most mentioned along with Airbnb on Twitter and an illustration of how the
    Keywords: peer-to-peer accommodation; Airbnb; COVID-19 pandemic; voice analytics; topic mining analytics; latent Dirichlet allocation; LDA.
    DOI: 10.1504/IJWBC.2024.10056991
  • The Hidden Impact of Hashtags on Instagram: Navigational Heuristics on Source Trustworthiness   Order a copy of this article
    by Ye Han, Shuang Wu, Peter Haried 
    Abstract: Hashtags are popular navigability tools in a social media-driven environment. However, social media users have purposely employed a hashtag stuffing strategy, where many unrelated hashtags are added to a post to increase the visibility of the post and drive viewership. The results of the current study suggest a potential negative impact of hashtags on source trustworthiness assessment made by Instagram users through heuristic processing. This research conducted two experimental studies with samples from the overall Instagram population. Study 1 (N = 174) was a 2
    Keywords: hashtags; source trustworthiness; technological heuristics; social media; hashtag stuffing; affordance.
    DOI: 10.1504/IJWBC.2024.10062541

Special Issue on: Consumer Behaviour in Mobile Commerce and Social Media

  • Study on Detection of Impulsive Purchase Behavior of E-commerce Platform Consumers Based on Social Network Media   Order a copy of this article
    by Bo An 
    Abstract: Studying consumers’ impulsive purchasing behaviour helps to understand their purchasing behaviour and increase sales revenue. Therefore, this article proposes a method for detecting consumer impulse buying behaviour on e-commerce platforms based on social network media. Firstly, collect data on consumer purchasing behaviour; Secondly, preprocess the characteristics of impulse buying behaviour based on the RFM function. Then, considering the polarity and degree of emotional words, calculate impulsive emotion scores based on an emotion dictionary; Finally, use the LSH algorithm to find the nearest neighbour point that matches each user’s emotional needs, and use the input of LOF to find the extreme point, obtaining the detection results of impulse buying behaviour. The results show that the detection recall rate of this method can reach 99.0%, the detection error is only 0.02, and the detection time is only 8.9 seconds. The detection effect of this method is good.
    Keywords: social network media; Impulsive purchasing behaviour; e-commerce platforms; K-nearest neighbour method; LOF method; LSH algorithm.
    DOI: 10.1504/IJWBC.2024.10061785
  • Study on Distributed network anomaly attack detection method based on machine learning   Order a copy of this article
    by Qiaoyun Chen, Youyou Li 
    Abstract: To overcome the problems of traditional methods such as low detection accuracy, high false alarm rate and long detection time, a distributed network anomaly attack detection method based on machine learning is proposed. Firstly, the local density of network operation data points is estimated by combining the Gaussian kernel and cut-off check, and the network operation data is clustered by the DPCA algorithm. Secondly, through the constructed attack model, abnormal attack characteristics are determined and important features are screened. Finally, the naive Bayes in machine learning is used to determine the attribute characteristics of each category in the clustering results. Match the category attribute feature with the important feature to get the anomaly attack detection result. The experimental results show that the maximum detection accuracy of this method is 98%, the average false alarm rate is 2.64%, and the detection time varies between 0.25 s and 0.68 s.
    Keywords: machine learning; distributed; network abnormality; attack detection; DPCA algorithm; naive Bayes.
    DOI: 10.1504/IJWBC.2024.10061786
  • A Precise sensing method of campus network security situation based on fuzzy clustering algorithm   Order a copy of this article
    by Ranran Yin, Zhenyu Yang 
    Abstract: To ensure the safe operation of the campus network and improve the sensing accuracy and convergence speed, a precise sensing method for campus network security situations based on a fuzzy clustering algorithm is proposed. Firstly, the constructed element model is used to extract the situation elements, and the situation information is processed through the non-negative matrix decomposition algorithm. Secondly, the Kalman entropy method is used to estimate the security situation of the whole network of the network campus, and the new information on the network security situation is calculated. Finally, according to the characteristics of campus network security situation awareness, the network security situation awareness is realised through a fuzzy clustering algorithm. The experimental results show that the MAPE value and RMSE value of the proposed method are low, and the RMSE value is maintained below 0.15, the convergence speed is fast, and can comprehensively reflect the network security situation.
    Keywords: fuzzy clustering algorithm; campus network; security situation awareness; Kalman filter model; transitive closure.
    DOI: 10.1504/IJWBC.2024.10061787
  • Accurate Prediction of Purchasing Behavior of Cross border E-commerce Consumers under Social Media Marketing   Order a copy of this article
    by Zhengya Guo 
    Abstract: Traditional cross-border e-commerce consumer purchase behaviour prediction methods have problems with low reliability and high prediction error rate. This article designs a research on accurate prediction of purchasing behaviour of cross border e-commerce consumers under social media marketing. Firstly, determine and extract the purchasing behaviour characteristics of cross-border e-commerce consumers under social media marketing; Then, determine the initial centroid set of feature data, determine similar data by calculating the Euclidean distance between feature data, and remove similar data; Finally, by calculating the information entropy of the feature data, determine the weight value of the feature data, and use the integration algorithm and loss function to achieve accurate prediction of purchase behaviour. The test results show that the proposed method improves the reliability of prediction and reduces the prediction error rate.
    Keywords: social media marketing; cross border e-commerce consumers; purchase behaviour; logistic regression; Euclidean distance; histogram algorithm.
    DOI: 10.1504/IJWBC.2024.10061788
  • Deep Mining of E-commerce Consumer Behavior Data Based on Concept Hierarchy Tree   Order a copy of this article
    by Yingchun Han 
    Abstract: In order to solve the problems of low data collection efficiency, high noise, and low accuracy in traditional e-commerce consumer behaviour user data mining methods, a deep mining method for e-commerce consumer behaviour data based on concept hierarchy tree is proposed. Use Python scripting language to collect e-commerce consumer behaviour data from e-commerce platforms, and use Myriad filtering algorithm to remove the interference noise in e-commerce consumer behaviour data. Based on non-interference noise free e-commerce consumer behaviour data, utilising domain expert participation and machine learning algorithms, a concept hierarchical tree based e-commerce consumer behaviour data mining model is established to achieve deep mining of e-commerce consumer behaviour data. Experimental results show that the method proposed in this paper collects e-commerce consumer behaviour data more quickly, effectively removes interference noise contained in e-commerce consumer behaviour data, and can effectively and deeply mine the behavioural preferences of e-commerce consumers, with significant applicability.
    Keywords: concept hierarchy tree; e-commerce consumer; behaviour data; deep mining; myriad filtering algorithm.
    DOI: 10.1504/IJWBC.2024.10061789
  • Customer Relationship Value Evaluation Method for E-commerce Platform Based on Fuzzy Clustering   Order a copy of this article
    by Sanping Qiu 
    Abstract: In order to improve the accuracy of customer relationship value evaluation on e-commerce platforms and reduce evaluation time, this paper proposes a fuzzy clustering-based customer relationship value evaluation method for e-commerce platforms. Firstly, consider the changes in customer relationships at different stages of the lifecycle and analyse the timeliness characteristics of customer relationships; then, the Weibull distribution function is introduced to calculate the length of customer lifecycle; finally, cluster the investment return on tangible assets of the enterprise, and invert the investment return on intangible assets of the enterprise. Use fuzzy clustering to evaluate the customer relationship value and obtain the final evaluation result. The results show that the method proposed in this paper can effectively improve evaluation efficiency and evaluation efficiency, with an evaluation time of only 2.8 seconds and an evaluation accuracy of up to 99.6%.
    Keywords: fuzzy clustering; e-commerce platform; customer relationship value evaluation; life cycle length; return rate splitting method.
    DOI: 10.1504/IJWBC.2024.10061790
  • The Impact of Payment Methods on Consumer Behavior in the E-commerce Environment   Order a copy of this article
    by Yanxi Zhou 
    Abstract: The changes in payment methods in the e-commerce environment also have an impact on consumer behaviour. Therefore, the study investigates the impact of payment methods on consumer behaviour in the e-commerce environment. The impact of e-commerce mainly includes factors such as social environment, service methods, and consumer psychology. Payment types include online bank card payment, electronic cash, electronic check, third-party payment platform and mobile payment. Analyse the impact of payment methods on consumer behaviour from four aspects: purchase decision, purchase intention, consumption motivation, and consumption habits. The example analysis results show that the proportion of consumers choosing electronic payment methods reaches 90%, indicating that electronic payment methods have changed consumers’ purchasing decisions; The choice of electronic payment methods and the amount of consumption also exceed the amount of cash, indicating that consumers are more willing to engage in online consumption in e-commerce, changing their purchasing intention.
    Keywords: E-commerce environment; payment method; consumer behaviour; Influencing factors.
    DOI: 10.1504/IJWBC.2024.10061791
  • Privacy Protection of Multiple Sensitive Attribute Data for Users on E-commerce Social Media Platforms   Order a copy of this article
    by Na Wang, Ji Zhang, Feng Gao 
    Abstract: Protecting the privacy of multi-sensitive attribute data is of great significance for safeguarding the interests of individuals, enterprises, and countries, as well as promoting technological development. Therefore, this paper proposes a privacy protection method of multiple sensitive attribute data for users on e-commerce social media platforms. An improved artificial bee colony algorithm is used to improve the KHM algorithm and mine multi-sensitive attribute data. Based on a personalised anonymity model, the mined multi-sensitive attribute data is quantified in a graded manner according to the sensitivity value and user privacy requirements of each attribute data. Low-sensitive attribute data is protected by equivalent expression privacy protection, while high-sensitive attribute data is encrypted and hidden by an improved fully homomorphic encryption method, thus achieving data privacy protection. The experimental results show that the probability of successful abnormal extraction of data by hackers using this method is less than 0.05, which improves privacy security.
    Keywords: e-commerce; multiple sensitive; attribute data; privacy protection; KHM algorithm; social media.
    DOI: 10.1504/IJWBC.2024.10061792
  • Research on personalized short video push on social media platforms based on affinity propagation clustering   Order a copy of this article
    by Miao Wang 
    Abstract: Personalised short video push on social media platforms can help enterprises improve user experience, competitiveness, and marketing effectiveness. This article proposes a personalised short video push method on social media platforms based on affinity propagation clustering. By determining the attractiveness, belonging, and reference between data points, the optimal clustering centre of the affinity propagation clustering algorithm is selected to achieve user behaviour data collection. Based on the data collection results, Markov matrix is used to extract user sentiment labels, combined with sentiment labels and XGBoost model to predict user personalised preferences. The i Expand algorithm is used to determine user interest vectors and generate recommendation lists, achieving personalised short video push on social media platforms. The experimental results show that the maximum push accuracy of this method is 97%, the maximum time consumption is 97.4 ms, and the maximum satisfaction with push results is 98.6.
    Keywords: affinity propagation clustering; social media platforms; short video; personalised; push; sentiment labels; XGBoost model; i Expand algorithm.
    DOI: 10.1504/IJWBC.2024.10061793
  • A Method for Tracing Big Data of Network Public Opinion Based on Data Mining Algorithms   Order a copy of this article
    by Shumin Zhi, Lin Yu 
    Abstract: In order to achieve accurate traceability of massive public opinion data, this study carried out a study on the traceability method of network public opinion big data based on data mining algorithm. First of all, the network public opinion data is cleaned up and its data characteristics are mined. Then, the extracted public opinion features are taken as the input of the recursive neural network, which is used to construct the attention model and output the prediction results of the network public opinion. Finally, determine the network public opinion information to be tracked. Support vector machine is used to improve the probability packet tagging tracking algorithm and output the tracking results of public opinion information. The experimental results show that the implementation efficiency of this method is higher than 99%, and the average error of data tracing is less than 0.1, which has great application value.
    Keywords: data mining algorithms; online public opinion; big data; traceability methods; kernel fuzzy clustering; probability packet labelling.
    DOI: 10.1504/IJWBC.2024.10061794
  • Comprehensive evaluation method of live streaming business model in online marketing environment   Order a copy of this article
    by Miaomiao Zhang, Pianpian Zhao, Pei Wang 
    Abstract: In order to effectively analyse the business model of live streaming, this article studies a comprehensive evaluation method for live streaming business models in an online marketing environment. Firstly, the analytic hierarchy process is used to determine the 20 evaluation indicators of the indicator layer. Then, calculate the indicator weights based on the entropy method. Finally, based on comprehensive evaluation indicators, conduct a comprehensive evaluation of the live streaming business model. Through experiments, it can be seen that the evaluation method proposed in this article can accurately analyse the importance of evaluation indicators, and the evaluation results are consistent with the actual state of the live streaming sales business model. Multiple secondary indicators designed can better measure economic effectiveness, marketing effectiveness, market competition, and consumer influence, which can reflect the good practical application performance of the proposed method in this article.
    Keywords: network marketing; live streaming with goods; business model; comprehensive assessment; evaluation metrics.
    DOI: 10.1504/IJWBC.2024.10061795
  • Feature Extraction of News Communication on Microblog Platform Based on Multilevel Sliding Window Model   Order a copy of this article
    by Cheng Cui 
    Abstract: In order to fully understand the characteristics of news dissemination on Microblog platforms, this article proposes a method for extracting news dissemination features on Microblog platforms based on a multi-level sliding window model. Firstly, identify the four major characteristics of Microblog platform news, including high timeliness, free writing style, rich personal emotional bias, and the ability to restore the truth of the news. Secondly, a Microblog platform news communication representation model is constructed using the news communication theme content as input and the Microblog platform news communication representation vector as output. Finally, determine the multi-level sliding window counter, correspond to the sub window positions, and segment the feature data to complete the feature extraction of news dissemination on Microblog platform. The experimental results show that the proposed method has a high recall rate for feature extraction and good feature data balance.
    Keywords: multilevel sliding window model; Microblog platform; news dissemination; feature extraction.
    DOI: 10.1504/IJWBC.2024.10061796
  • A Method for Identifying Consumer Emotional Tendency in the   Order a copy of this article
    by Jie Liu 
    Abstract: In order to achieve accurate and rapid identification of consumer emotional tendencies, a method for identifying consumer emotional tendencies under the
    Keywords: the ‘live streaming+e-commerce’ model; consumer; emotional orientation identification; HowNet similarity; Google Similarity.
    DOI: 10.1504/IJWBC.2024.10061797
  • Abnormal Behavior Detection of E-commerce Consumers Based on Improved Hidden Markov Model   Order a copy of this article
    by Meng Su  
    Abstract: To address the issues of low anomaly detection rate, high false positive rate, and long detection time in traditional methods, an abnormal behaviour detection method for e-commerce consumers based on an improved hidden Markov model is proposed. The Scrapy spider framework is used to collect e-commerce consumer behaviour data, including purchase data, browsing data, search data, and evaluation data. The collected data is processed using an improved K-means algorithm for clustering, with normalisation, missing value imputation, and outlier removal applied to the clustering results. The MOPSO algorithm is used to optimise the parameters of the hidden Markov model, and the processed data is then input into the improved hidden Markov model to output the relevant detection results. Experimental results show that the maximum anomaly detection rate of this method is 96.7%, the maximum false positive rate is 4.7%, and the average detection time is 0.73 s.
    Keywords: improved hidden Markov model; e-commerce consumers; abnormal behaviour detection; Scrapy crawler architecture; improved k-means algorithm; MOPSO algorithm.
    DOI: 10.1504/IJWBC.2024.10062542

Special Issue on: Research Advances on User Interactions in Social Media Using Data

  • Customer Churn Prediction Based on Customer Value and User Evaluation Emotions in Online Marketing   Order a copy of this article
    by Huanan Mo  
    Abstract: In order to improve the accuracy and usefulness of the churn prediction model, the core elements of the research content were designed to include collecting data on customer purchase behaviour and reviews, quantifying and analysing customer value, analysing customer sentiment in reviews, and combining customer value factors and review sentiment factors in the model. The results of the study show that the model performs best on different indicators, and the area of the main characteristic curve is the largest, which is significantly higher than that of the traditional model. Its hit rate, coverage rate and improvement coefficient also perform well. At the same time, when the sample size increases, the improvement coefficient increases the most, reaching 0.41. In conclusion, the model performs well in customer churn prediction, and it can provide certain reference value for the research field of customer churn prediction.
    Keywords: online marketing; customer value; evaluate emotions; customer churn prediction; CCP; fusion model.
    DOI: 10.1504/IJWBC.2025.10062543