SNS user classification and its application to obscure POI discovery

Zhuang, Chenyi; Ma, Qiang; Yoshikawa, Masatoshi

doi:10.1007/s11042-016-4034-6

Cited by 10 publications

(4 citation statements)

References 23 publications

(28 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2.3.1 Degree. The degree is the number of links showing how many relationships a node (member) has on a social network (Wasserman and Faust, 1994;Zhuang et al, 2017).…”

Section: Network Variablesmentioning

confidence: 99%

The effect of network structure on the purchase of virtual goods on social networking services

Lee

Cha

2021

INTR

View full text Add to dashboard Cite

PurposeThis paper aims to identify the effect of social structure variables on the purchase of virtual goods. Using field data, it also tests whether their effects on a social networking service are dynamic.Design/methodology/approachTo achieve the research objectives, the authors have applied the random effects panel Tobit model with actual time-series corporate data to explain a link between network structure factors and actual behavior on social networking services.FindingsThe authors have found that various network structure variables such as in-degree, in-closeness centrality, out-closeness centrality and clustering coefficients are significant predictors of virtual item sales; while the constraint is marginally significant, out-degree is not significant. Furthermore, these variables are time-varying, and the dynamic model performs better in a model fit than the static one.Practical implicationsThe findings will help social networking service (SNS) operators realize the importance of understanding network structure variables and personal motivations or the behavior of consumers.Originality/valueThis study provides implications in that it uses various and dynamic network structure variables with panel data.

show abstract

“…2.3.1 Degree. The degree is the number of links showing how many relationships a node (member) has on a social network (Wasserman and Faust, 1994;Zhuang et al, 2017).…”

Section: Network Variablesmentioning

confidence: 99%

The effect of network structure on the purchase of virtual goods on social networking services

Lee

Cha

2021

INTR

View full text Add to dashboard Cite

show abstract

“…The paper titled BSNS User Classification and its Application to Obscure POI Discovery^by Zhuang et al [10] investigates the problem of mining the information asymmetry regarding location among various user groups of social networking services. To solve this problem, it proposes three models for automatically estimating the user's Blocation authority^(i.e.…”

Section: Summary Of the Accepted Papersmentioning

confidence: 99%

Guest Editorial: Multimedia in Social Mobile Computing (MSMC)

Ferri

Grifoni

D’Ulizia

et al. 2017

Multimed Tools Appl

View full text Add to dashboard Cite

“…The former effort to increase the number of tourist attractions in each area is more important, especially in areas with few well-known tourist attractions. In particular, there are studies [13], [18], [25], [44], [45] on tourism spot mining as a significant approach to increase the number of tourist attractions. These studies on spot mining are based on user-generated content about spots posted on the Web, such as SNS [13], [25] and photo-sharing services [18], [44], [45], to search a tourist destination for obscure spots that are worth visiting but relatively less known.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, there are studies [13], [18], [25], [44], [45] on tourism spot mining as a significant approach to increase the number of tourist attractions. These studies on spot mining are based on user-generated content about spots posted on the Web, such as SNS [13], [25] and photo-sharing services [18], [44], [45], to search a tourist destination for obscure spots that are worth visiting but relatively less known. For example, Katayama et al [13] are mining potential tourist spots from location information in Foursquare check-in logs.…”

Section: Introductionmentioning

confidence: 99%

Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

Katsumi

Yamada

Ochiai

2023

Journal of Information Processing

View full text Add to dashboard Cite

With the rapid spread of social networking services (SNS) and social media, tourism has become a more accessible leisure activity. While many tourists visit well-known tourist destinations, the number of tourists visiting lesser-known ones in non-urban or rural areas has been in decline, and the decline has been exacerbating various problems such as economic disparities. Under these circumstances, it is becoming increasingly important to attract tourists to lesser-known areas with few tourism resources, because attracting tourists brings various positive effects to local residents and municipalities. Existing efforts to attract tourists include studies on tourist spot mining, which search a tourist destination for sightseeing spots that are worth visiting but relatively less well known, based on user-generated content about spots on the Web, such as posts on SNS and images on photo-sharing services. However, as they rely on the amount of user-generated content about potential sightseeing spots to be mined, these methods are limited in discovering a new sightseeing spot that is not recognized as a tourist attraction in advance. To address these challenges, we are inspired by an interesting phenomenon: a previously little-known spot that looks similar to a famous place becomes a hot topic on social media, attracting many people and suddenly becoming a popular tourist attraction. In this paper, we call such a spot with a similar appearance to a well-known one, as generic point-of-interest (POI), and we provide a novel concept of discovering new sightseeing spots by computationally finding generic POIs; we give a new perspective of discovering a new tourist attraction by characterizing a commonplace spot that has a similar appearance to a well-known one as a new tourist attraction. We also propose a pipeline implemented for mining generic POIs, where we calculate the similarity between candidate spots of generic POIs and famous sightseeing spots. Furthermore, we evaluate the feasibility of our approach both through human evaluation and through a photo contest on the Web we held with Tourism Shikoku. The results prove that our proposed method can discover new tourist destinations. The primal advantage of our approach is that it gifts new tourism value to a spot with at least a single place photo similar to a famous sightseeing spot, and thus it discovers a new tourist attraction even in areas with a minimal number of user-submitted images. We hope that our novel concept will be the first step in efforts to attract tourists in various regions around the world, enabling utilization of local spots that even the locals may not be aware of.

show abstract

SNS user classification and its application to obscure POI discovery

Cited by 10 publications

References 23 publications

The effect of network structure on the purchase of virtual goods on social networking services

The effect of network structure on the purchase of virtual goods on social networking services

Guest Editorial: Multimedia in Social Mobile Computing (MSMC)

Characterizing Generic POI: A Novel Approach for Discovering Tourist Attractions

Contact Info

Product

Resources

About