International Journal of Web Based Communities (16 papers in press)

Regular Issues

• Factors Affecting Attachment Behaviors, Cognitive and Emotional Evaluations on Facebook Live streams  
  by Kien Pham 
  Abstract: The research trend of live streams has become increasingly popular in recent years. Therefore, the study aimed to investigate the factors of live streaming platforms that successfully stimulate consumers to generate corresponding behavioural responses. The stimulus-organism-response model was used as a theoretical background. With a sample of 324 online consumers, the study attempted to investigate the relationships among the constructs. The findings showed that stimuli, (e.g., live streamers’ professional ability, relationship connection with consumers, and social presence of the live streaming environment) have a significant influence on the organism, (e.g., cognitive and emotional evaluation of live streamers), and thus consumers’ responses (e.g., emotional and merchant attachment behaviours). Furthermore, this study found that the live broadcast platform’s pre-factors and the live broadcaster’s cognitive and emotional evaluations positively and significantly impact their attachment behaviour. The findings provide information to improve their live streaming platforms and businesses.
  Keywords: live streams; Facebook; attachment behaviour; cognitive; emotional; evaluation; stimulus-organism-response.
  DOI: 10.1504/IJWBC.2024.10055731
   
  
• Exploring the impact of COVID-19 Pandemic and Vaccine Dissemination on Airbnb's Popularity and Sentiment on Twitter  
  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
   
  
• YouTube and the Production of Online Video Cultures in Rural South India  
  by Srikanth Nayaka, Vamshi Krishna Reddy Vemireddy, Prabha Shankar Dwivedi 
  Abstract: This article examines how a group of villagers in rural South India shot to online fame by posting entertaining audio-visual content on YouTube. Motivated to showcase the countryside culture, villagers began sharing short videos depicting their daily rural lives. Some of their videos have gone viral, garnered millions of views, and amassed thousands of subscribers. Inspired by the positive reception, village video makers have taken their YouTube productions professionally and created a unique brand identity for their media content. From short films to web series, village video makers produce a wide variety of audio-visual content. Village video creators have accumulated a massive online following, digital stardom, and social media fame. This paper maps the emerging vernacular online video-sharing cultures and the rise of rural micro-celebrities through a close reading of a specific case study,
  Keywords: YouTube; platformisation; video cultures; vernacular creativity; digital cultures; digital labour; microcelebrity; digital India; South India; global south.
  DOI: 10.1504/IJWBC.2023.10060125
   
  

Special Issue on: Automatic Disinformation Detection on Social Media Platforms

• Data mining method of social media hot topics based on time series clustering  
  by Wei Wang 
  Abstract: In order to overcome the problems of large analysis error and low mining accuracy of traditional hot topic data mining methods, this paper proposes a new social media hot topic data mining method based on time series clustering. Firstly, the topic feedback forms of reading, comment, forwarding and praise are taken as the research objects. Secondly, the contribution value of various data in the topic heat is calculated to obtain the topic heat results. Finally, take the topic value as the goal and follow-up reports as the index to realise data mining. The test results show that the design method can accurately analyse the data of reading, comments, forwarding and likes. The analysis results of the number of follow-up reports have a high degree of fit with the actual results, and have a high mining accuracy, which is close to 100%.
  Keywords: time series clustering; social media hot topics; data mining; topic heat; topic value.
  DOI: 10.1504/IJWBC.2023.10049291
   
  
• Prediction method of e-commerce consumers' purchase behavior based on social network data mining  
  by Ming Yang 
  Abstract: In order to effectively improve the prediction accuracy of e-commerce consumers’ purchase behaviour and shorten the prediction time of e-commerce consumers’ purchase behaviour, a prediction method of e-commerce consumers’ purchase behaviour based on social network data mining is proposed. Firstly, according to the statistical characteristics of e-commerce consumers’ purchase behaviour, data mining method is used to extract the characteristics of e-commerce consumers’ purchase behaviour. Secondly, the social network analysis method is used to analyse the purchase behaviour characteristics of e-commerce consumers and the social network model. Finally, build the prediction model of e-commerce consumers’ purchase behaviour to realise the prediction of e-commerce consumers’ purchase behaviour. The experimental results show that the proposed method has a good effect on the prediction of e-commerce consumers’ purchase behaviour, and can effectively improve the prediction accuracy of e-commerce consumers’ purchase behaviour. The prediction deviation rate is only 1.8%.
  Keywords: social network analysis method; data mining methods; e-commerce consumers; purchase behaviour; prediction model.
  DOI: 10.1504/IJWBC.2023.10049292
   
  
• Cross modal retrieval of large-scale images in social media based on spatial distribution entropy  
  by Jie Ding, Guotao Zhao, Fang Xu 
  Abstract: In order to improve the cross-modal retrieval accuracy of large-scale social media images, a cross-modal retrieval method for large-scale social media images based on spatial distribution entropy is proposed. First, extract the information features of the colour and texture of the image, then use the image cross-modal retrieval method based on the spatial distribution entropy to calculate the spatial distribution entropy of the colour information and texture information features in the image, and finally use the Euclidean distance to judge the space between social media images. The matching degree of the distribution entropy, according to the matching degree to judge whether the image cross-modal retrieval is successful or not. The experimental results verify that the proposed method can implement comprehensive retrieval according to the specific characteristics of the retrieved images, and the matching degree of image retrieval is greater than 95%, and the retrieval accuracy is high.
  Keywords: spatial distribution entropy; social media; large-scale; cross-modal; image; retrieval.
  DOI: 10.1504/IJWBC.2023.10049668
   
  
• Data mining method of mobile e-commerce consumer purchase behavior  
  by Shuang Li 
  Abstract: In order to improve the accuracy and efficiency of data mining of consumer purchase behaviour in mobile e-commerce, a data mining method of consumer purchase behaviour in mobile e-commerce is proposed. Firstly, through the calculation of support in principal component analysis, the data characteristics of mobile e-commerce consumers’ purchase behaviour are extracted. Then, the original residual of purchase behaviour data is calculated through iterative test, and the original feature data is corrected to complete the preprocessing. Finally, the Boolean rules in association rules are used to determine the association degree between purchase behaviour data, and the minimum threshold of purchase behaviour data is calculated. By establishing the correlation function of mobile e-commerce consumer purchase behaviour data, mining the characteristic information of mobile e-commerce consumer purchase behaviour data, and completing the purchase behaviour data mining. The results show that the highest accuracy of data mining is 98.1%.
  Keywords: principal component analysis; iterative test method; Boolean rule; mobile e-commerce; consumer buying behaviour.
  DOI: 10.1504/IJWBC.2023.10050219
   
  
• An Encryption of Social Network User Browsing Trajectory Data Based on Adversarial Neural Network  
  by Xinliang Wang 
  Abstract: In order to solve the problems of high information loss rate, poor encryption effect and long encryption time existing in traditional social network user browsing trajectory data encryption methods, this paper proposes an encryption method of social network user browsing trajectory data based on adversarial neural network. Mutual information is used to extract browsing characteristics of social network users and calculate browsing path similarity of social network users, so as to determine the clustering centre of browsing trajectory data and realise browsing trajectory data mining. Combining with adversarial neural network, the symmetric encryption and decoding model is designed, and the user browsing feature data is input into the model to realise the user browsing feature data encryption. Experimental results show that the information loss rate of the proposed method is always lower than 5%, the encryption effect is good, and the average encryption time is 53 ms.
  Keywords: adversarial neural network; social network; user browsing trajectory; data encryption; data mining; symmetric encryption.
  DOI: 10.1504/IJWBC.2023.10050220
   
  
• An evolution trend evaluation of social media network public opinion based on unsupervised learning  
  by Yanhua Shen 
  Abstract: In order to overcome the problems of poor evaluation effect, low accuracy and long time in traditional methods, an evolution trend evaluation method of social media network public opinion based on unsupervised learning is proposed. Firstly, establish the evaluation index system of public opinion evolution trend of social media network. Then, the graph convolution neural network is used to combine the evaluation index with neighbourhood features to extract the mixed features of public opinion evolution trend, and the correlation degree of mixed features is calculated by correlation ranking method. Finally, the evaluation model of public opinion evolution trend based on unsupervised learning is constructed according to the correlation degree of mixed features, and the evaluation results are obtained. The experimental results show that the proposed method has good evaluation effect of public opinion evolution trend, high evaluation accuracy and short evaluation time.
  Keywords: unsupervised learning; social media networks; evolution of network public opinion; evolution trend assessment.
  DOI: 10.1504/IJWBC.2023.10051913
   
  
• A semantic retrieval model of social media data based on statistical theory  
  by Fuwang Li 
  Abstract: Aiming at the problems of low retrieval accuracy and efficiency in semantic retrieval model of social media data, this paper studies semantic retrieval model of social media data based on statistical theory. Statistical theory and ontology of semantic retrieval information of social media data are analysed to complete the labelling process of retrieval information. The semantic retrieval model of social media data is constructed by calculating the similarity of semantic distance and information amount and using statistical theory. Experimental results show that the recall rate of the proposed method is as high as 94%, and the accuracy is as high as 92%, both higher than other methods, and the retrieval time is only 18.2 s. Therefore, the semantic retrieval effect of social media data is good, and the semantic retrieval accuracy and efficiency of social media data are effectively improved.
  Keywords: statistical theory; statistical language model; social media data; semantic retrieval; retrieval model.
  DOI: 10.1504/IJWBC.2024.10055930
   
  
• Study on Mining repeated purchase behavior intention of online consumers based on big data clustering  
  by Kai Niu  
  Abstract: In order to improve the performance of traditional repeat purchase behaviour intention mining methods in mining accuracy, a repeat purchase behaviour intention mining method of online consumer users based on big data clustering is proposed. Users’ repeated purchase behaviour can be combined to reduce the dimension of network data. Extract the characteristics of repeated purchase behaviour of online consumers, judge the similarity of repeated purchase behaviour intention data of online consumers, establish a behaviour association rule mining model, and obtain the mining results of repeated purchase behaviour intention of online consumers. The simulation results show that the proposed method has high accuracy and short time to mine the repeated purchase behaviour intention of online consumers. The highest intention mining accuracy of this method can reach 99.99%.
  Keywords: big data clustering; network consumer; repeat purchase; behavioural intention; association rules.
  DOI: 10.1504/IJWBC.2024.10056880
   
  
• A feature extraction method of network social media data based on fuzzy mathematical model  
  by Zong-biao Zhang  
  Abstract: Aiming at the problems of low extraction accuracy and efficiency of traditional network social media data feature extraction methods, this paper proposes a network social media data feature extraction method based on fuzzy mathematical model. Firstly, by constructing the distributed data topology model and integrating the adaptive distributed data reorganisation algorithm, the network social media data is collected. Then, the continuous attributes of network social media data are discretised by using data mining algorithm, and the correlation characteristics of network social media data are analysed. Finally, the fuzzy mathematical model is used to identify the correlation characteristics of online social media data, and the feature error is corrected by the correction function to complete the feature extraction of online social media data. The experimental results show that the accuracy of feature extraction of online social media data extracted by the proposed method is as high as 98.5%.
  Keywords: fuzzy mathematical model; fuzzy set; membership function; artificial ant colony algorithm; online social media; data feature extraction.
  DOI: 10.1504/IJWBC.2024.10060517
   
  
• Social media user information security encryption method based on Chaotic Algorithm  
  by Yi  Xiao 
  Abstract: In order to overcome the problems of small entropy, poor image definition and low information security in traditional methods, this paper proposes a social media user information security encryption method based on chaotic algorithm. Firstly, the general social media user information encryption architecture was designed by chaotic image encryption method. Secondly, logistic chaotic map was defined according to the principle of fast chaotic encryption. Then, the chaotic function of social media user information was obtained by
  Keywords: chaotic algorithm; scrambling diffusion; social media users; information security encryption.
  DOI: 10.1504/IJWBC.2024.10060518
   
  
• Research and judgment method of social network hot news public opinion based on knowledge graph  
  by Gelang Li 
  Abstract: In order to improve the accuracy of the judgement of the development of news public opinion, this paper puts forward the research and judgement method of social network hot news public opinion based on knowledge graph. Through corpus annotation, character coding and time slice processing, the corpus of hot news on the internet is pre-processed, and the processed corpus information is used to construct knowledge atlas. In order to improve the accuracy of the analysis of news elements, the map is composed of several sub-maps with the most closely connected relationship. Finally, in combination with the real-time nature of news, the development of news public opinion is judged from the three perspectives of news evolution, spread and news heat. The test results show that the error of the method is less than 2% for the judgement of public opinion evolution degree, spread breadth and news heat.
  Keywords: knowledge graph; hot news on social networks; public opinion research and judgement; corpus processing; connectivity relation; real time.
  DOI: 10.1504/IJWBC.2024.10060519
   
  
• Anomaly detection method of social media user information based on data mining  
  by Xiaoyan Wan  
  Abstract: Aiming at the problems of low detection accuracy, recall and F1 value of traditional social media user information anomaly detection methods, a social media user information anomaly detection method based on data mining is proposed. Firstly, clean the social media data and eliminate the invalid and missing values in the data. Then, filter the abnormal user information in the social media data through the unsupervised k-means algorithm in data mining. Finally, according to the screening results, calculate the text word segmentation of user information, obtain the similarity of word frequency, and complete the detection of abnormal user information. The method provided by social media has the highest accuracy of 97.5%, which is the highest exception detection rate of 97.5% and the highest exception detection rate of social media.
  Keywords: data mining; social media; user information; K-means; Weibo.
  DOI: 10.1504/IJWBC.2024.10060520
   
  
• Study on news recommendation of social media platform based on improved collaborative filtering  
  by Bin Wu 
  Abstract: Aiming at the problems of low recommendation accuracy and low user interest in the existing methods, a news recommendation of social media platform based on improved collaborative filtering is designed. The initial key features of news data are determined, and the occurrence frequency of key features is counted by chi square, so as to realise feature extraction. Calculate the mutual information between different news data features, determine the correlation degree between features, and remove the data with similar features and low correlation degree. The collaborative filtering algorithm is improved by adding timing update, trust and other data in collaborative filtering. The improved collaborative filtering algorithm is used to build a recommendation model, and the news data characteristics and user preference data are input into the model to complete the recommendation. The experimental results show that the news data recommended by the proposed method has high accuracy and high user interest.
  Keywords: improving collaborative filtering; social media platform; news recommendation; active learning; covariance matrix; information gain.
  DOI: 10.1504/IJWBC.2024.10060521