Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China

Fan, Tianhui; Guo, Naijing; Ren, Yujie

doi:10.1007/s10708-019-10072-8

Cited by 14 publications

(20 citation statements)

References 38 publications

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“…Likewise, Stojanovski et al (2018) applied DBSCAN cluster algorithms and the sentiment analysis on Twitter messages regarding the 2014 FIFA World Cup to address the emotional attitude of fans during this sport event and social hotspots within New York. Furthermore, recent studies have applied this clustering methodology to different social media platforms directly revealing consumers' sentiments through rating applications such as Dianping – a social platform for catering services– all of which show the accuracy of DBSCAN algorithm to identify consumer clusters (Fan et al , 2019).…”

Section: Related Workmentioning

confidence: 99%

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Pindado¹,

Barrena

2020

BFJ

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PurposeThis paper investigates the use of Twitter for studying the social representations of different regions across the world towards new food trends.Design/methodology/approachA density-based clustering algorithm was applied to 7,014 tweets to identify regions of consumers sharing content about food trends. The attitude of their social representations was addressed with the sentiment analysis, and grid maps were used to explore subregional differences.FindingsTwitter users have a weak, positive attitude towards food trends, and significant differences were found across regions identified, which suggests that factors at the regional level such as cultural context determine users' attitude towards food innovations. The subregional analysis showed differences at the local level, which reinforces the evidence that context matters in consumers' attitude expressed in social media.Research limitations/implicationsThe social media content is sensitive to spatio-temporal events. Therefore, research should take into account content, location and contextual information to understand consumers' perceptions. The methodology proposed here serves to identify consumers' regions and to characterize their attitude towards specific topics. It considers not only administrative but also cognitive boundaries in order to analyse subsequent contextual influences on consumers' social representations.Practical implicationsThe approach presented allows marketers to identify regions of interest and localize consumers' attitudes towards their products using social media data, providing real-time information to contrast with their strategies in different areas and adapt them to consumers' feelings.Originality/valueThis study presents a research methodology to analyse food consumers' understanding and perceptions using not only content but also geographical information of social media data, which provides a means to extract more information than the content analysis applied in the literature.

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Section: Related Workmentioning

confidence: 99%

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Pindado¹,

Barrena

2020

BFJ

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“…In this part, the test aftereffect of proposed FIC-PIXDCDC Method is contrasted and two existing Density-based spatial clustering of uses with commotion (DBSCAN) calculation [1] and Density-based Clustering Places in Geo-Social Networks (DCPGS) [2] is introduced. The proficiency of proposed FIC-PIXDCDC Method is dissected utilizing measurements, for example, clustering exactness, clustering time and error rate with assistance of underneath table and diagram.…”

Section: Resultsmentioning

confidence: 99%

“…Density-based spatial clustering of applications with noise (DBSCAN) algorithm was introduced in [1] for consumer clusters discovery with geo-tagged social network information. However, clustering performance of geo-social data was not efficient.…”

Section: Introductionmentioning

confidence: 99%

Information Criteria Based Partitioned Iterative X-means Dice Correlation Clustering Algorithm for Big Geo-social Data

Sreeku¹,

Abraham²,

Premalatha³

et al. 2022

Preprint

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Geo-social data is location-based social media data which is generated by people on social network (i.e. face book, twitter etc.,) that is related to specific locations. There are lot of social users who are generates very large amount of data called “Big Data” that is difficult to be analyzed and make real-time decisions. Few research works have been designed for clustering geo-social data using different techniques. However, clustering performance of conventional algorithms was not higher to exactly find frequently visited location of users in social network when taking big geo-social dataset as input. In order to overcome such drawbacks, a Focused Information Criterion based Partitioned Iterative X-means Dice Correlation Data Clustering (FIC-PIXDCDC) Method is proposed in this work. The FIC-PIXDCDC Method groups the similar geo-social data with higher accuracy and lesser time. In FIC-PIXDCDC method, geo-social data (i.e., user, location and time) from Weeplaces dataset is initially taken as an input. After obtaining input, FIC-PIXDCDC method chooses number of clusters and centroids randomly. Then, FIC-PIXDCDC calculates dice correlation between each input geo-social data and cluster centroids. Subsequently, FIC-PIXDCDC method applies Focused Information Criterion to construct optimal number of clusters for a given big dataset. This process of FIC-PIXDCDC method is repetitive until no deviation in cluster centroids. Appropriately, FIC-PIXDCDC strategy group's interrelated geo-social information along with accuracy at higher rate and lower time to accurately find area's data of regularly visited clients in social network. Trial assessment of FIC-PIXDCDC strategy is completed on elements, for example, clustering time, clustering precision, error rate as for number of geo-social information.

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“…and hotel and homestay inn data from online travel agencies (Airbnb, Ctrip, etc.) have become freely accessible to researchers, enabling them to comprehensively investigate social and economic phenomena [17][18][19]. The superiority of these datasets in terms of their realtime capability, lower costs, and wider coverage than previous platforms have provided new opportunities for quantifying the spatiotemporal patterns of tourism resources at a fine scale [20].…”

Section: Introductionmentioning

confidence: 99%

Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

Zheng

Huang

2022

Sustainability

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Understanding the spatiotemporal patterns and key determinants of rural homestay industry agglomeration is crucial for the well-planning and well-management of rural tourism during the process of rural revitalization in China. By employing multi geospatial datasets, this study investigated the long-term spatiotemporal patterns and their key determinants of homestay inns during the period 2004–2019 in Moganshan, a well-known rural tourism destination in Zhejiang Province, China. The kernel density estimation and spatial autocorrelation were integrated to identify the hotspots of rural homestay inns at a fine scale. The key determinants were further uncovered using multiple stepwise regression and logistic regression models. The result shows that the overall growth of homestay inns was slow at the early stage and has progressed rapidly since 2014, with 94.2% of homestay inns newly opened during the period 2014–2019. The first hotspot was located in Moganshan National Park and then spread to the surrounding villages. Three hotspot zones have emerged, including the northern hotspot zone (Sihe-Xiantan), central hotspot zone (Houwu-Park-Liaoyuan), and southern hotspot zone (Ziling-Laoling-Lanshukeng) by 2019. The modeling indicates that government policy was an essential determinant for the increase in homestay inns, followed by entrepreneurship and investment. The new homestay inns were more likely to occur in settlements close to scenic spots, river networks, and cultivated land. Abundant scenic spots and heterogeneous landscapes were also preferred when selecting sites and executing landscape design for homestay inns. Our empirical study has provided practical insights for policy makers, entrepreneurs, and planners for future sustainable homestay industry development.

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Consumer clusters detection with geo-tagged social network data using DBSCAN algorithm: a case study of the Pearl River Delta in China

Cited by 14 publications

References 38 publications

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Using Twitter to explore consumers' sentiments and their social representations towards new food trends

Information Criteria Based Partitioned Iterative X-means Dice Correlation Clustering Algorithm for Big Geo-social Data

Characterizing the Spatiotemporal Patterns and Key Determinants of Homestay Industry Agglomeration in Rural China Using Multi Geospatial Datasets

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