scholarly journals Coastal Tourism Spatial Planning at the Regional Unit: Identifying Coastal Tourism Hotspots Based on Social Media Data

2021 ◽  
Vol 10 (3) ◽  
pp. 167
Author(s):  
Gang Sun Kim ◽  
Joungyoon Chun ◽  
Yoonjung Kim ◽  
Choong-Ki Kim
Keyword(s):  
Social Media ◽  
Spatial Planning ◽  
Regional Scale ◽  
Coastal Tourism ◽  
Spatial Unit ◽  
Social Media Data ◽  
Tourist Attractions ◽  
Distribution Maps ◽  
Media Data ◽  
Spatial Hotspots

There is an increasing need for spatial planning to manage coastal tourism, and applying social media data has emerged as an effective strategy to support coastal tourism spatial planning. Researchers and decision-makers require spatially explicit information that effectively reveals the current visitation state of the region. The purpose of this study is to identify coastal tourism hotspots considering appropriate spatial units in the regional scale using social media data to examine the advantages and limitations of applying spatial hotspots to spatial planning. Data from Flickr and Twitter with 30″ spatial resolution were obtained from four South Korean regions. Coastal tourism hotspots were then derived using Getis-Ord Gi. Comparing the derived hotspot maps with the visitation rate distribution maps, the derived hotspot maps sufficiently identified the spatial influences of visitors and tourist attractions applicable for spatial planning. As the spatial autocorrelation of social media data differs based on the spatial unit, coastal tourism hotspots according to spatial unit are inevitably different. Spatial hotspots derived from the appropriate spatial unit using social media data are useful for coastal tourism spatial planning.

Discovering New Tourist Attractions Through Social Media Data: A Case Study in Sabah Malaysia

2018 ◽  
Author(s):  
Maisarah Abdul Halim ◽  
Noraain Mohamed Saraf ◽  
Nur Idzhainee Hashim ◽  
Abdul Rauf Abdul Rasam ◽  
Ahmad Norhisyam Idris ◽  
...  
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Tourist Attractions ◽  
Media Data

Do social media data indicate visits to tourist attractions? A case study of Shanghai, China

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Huilin Liang ◽  
Qingping Zhang
Keyword(s):  
Social Media ◽  
Survey Data ◽  
Systematic Research ◽  
Social Media Data ◽  
Content Type ◽  
Tourist Attractions ◽  
Cost Efficient ◽  
Positive Correlations ◽  
Use Studies ◽  
Media Data

PurposeCan Chinese social media data (SMD) be used as an alternative to traditional surveys used to understand tourists' visitation of attractions in Chinese cities? The purpose of this paper is to explore this question.Design/methodology/approachPopular tourism SMD sources in China, such as Ctrip, Weibo and Dazhong Dianping (DZDP), were used as data source, and the relationships between these sources and traditional data sources were studied with statistical methods. Data from Shanghai were used in this study since it is rich in tourism resources and developed in information.FindingsA systematic research method was followed and led to the following conclusions: There were positive correlations for attraction visitation between Chinese SMD and traditional survey data; Chinese SMD source could temporally indicate visits to Shanghai tourist attractions; Ctrip SMD generally performed less well than Weibo or DZDP, and different SMD performed differently depending on the specific attractions and time units in the visitation calculation process; and factors including visitation, distance from the city center and the grade of attractions might affect the prediction performance based on data from the SMD. The findings suggest that Chinese SMD could be used as a cost-efficient and reliable proxy for traditional survey data to predict Chinese attraction visitation.Originality/valueThis study applies and improves the methods of SMD reliability in attraction use studies, supplies the gap for premise, basis and foundation for the large amounts of tourism researches using SMD in China and could promote and inspire more efficient and advanced measures in tourism management and urban development.

Smart De-Identification of Social Media Data

2014 ◽  
Author(s):  
Kathleen M. Carley ◽  
L. R. Carley ◽  
Jonathan Storrick
Keyword(s):  
Social Media ◽  
Social Media Data ◽  
Media Data

Mining Social Media Data to Study the Consequences of Dementia Diagnosis on Caregivers and Relatives (Preprint)

2018 ◽  
Author(s):  
Anika Oellrich ◽  
George Gkotsis ◽  
Richard James Butler Dobson ◽  
Tim JP Hubbard ◽  
Rina Dutta
Keyword(s):  
Social Media ◽  
Family Relationships ◽  
Text Processing ◽  
Automated Analysis ◽  
Health Concern ◽  
Dementia Diagnosis ◽  
Data Set ◽  
Social Media Data ◽  
Real Time Processing ◽  
Media Data

BACKGROUND Dementia is a growing public health concern with approximately 50 million people affected worldwide in 2017 and this number is expected to reach more than 131 million by 2050. The toll on caregivers and relatives cannot be underestimated as dementia changes family relationships, leaves people socially isolated, and affects the finances of all those involved. OBJECTIVE The aim of this study was to explore using automated analysis (i) the age and gender of people who post to the social media forum Reddit about dementia diagnoses, (ii) the affected person and their diagnosis, (iii) relevant subreddits authors are posting to, (iv) the types of messages posted and (v) the content of these posts. METHODS We analysed Reddit posts concerning dementia diagnoses. We used a previously developed text analysis pipeline to determine attributes of the posts as well as their authors to characterise online communications about dementia diagnoses. The posts were also examined by manual curation for the diagnosis provided and the person affected. Furthermore, we investigated the communities these people engage in and assessed the contents of the posts with an automated topic gathering technique. RESULTS Our results indicate that the majority of posters in our data set are women, and it is mostly close relatives such as parents and grandparents that are mentioned. Both the communities frequented and topics gathered reflect not only the sufferer's diagnosis but also potential outcomes, e.g. hardships experienced by the caregiver. The trends observed from this dataset are consistent with findings based on qualitative review, validating the robustness of social media automated text processing. CONCLUSIONS This work demonstrates the value of social media data sources as a resource for in-depth studies of those affected by a dementia diagnosis and the potential to develop novel support systems based on their real time processing in line with the increasing digitalisation of medical care.

Citizens, Elites, and Social Media Methodological Challenges and Opportunities in the Study of Persuasion and Mobilization

2019 ◽  
pp. 1036-1058
Author(s):  
Philip Habel ◽  
Yannis Theocharis
Keyword(s):  
Social Media ◽  
Big Data ◽  
Supply And Demand ◽  
Political Process ◽  
The Political ◽  
The Novel ◽  
Complete Understanding ◽  
Social Media Data ◽  
Challenges And Opportunities ◽  
Media Data

In the last decade, big data, and social media in particular, have seen increased popularity among citizens, organizations, politicians, and other elites—which in turn has created new and promising avenues for scholars studying long-standing questions of communication flows and influence. Studies of social media play a prominent role in our evolving understanding of the supply and demand sides of the political process, including the novel strategies adopted by elites to persuade and mobilize publics, as well as the ways in which citizens react, interact with elites and others, and utilize platforms to persuade audiences. While recognizing some challenges, this chapter speaks to the myriad of opportunities that social media data afford for evaluating questions of mobilization and persuasion, ultimately bringing us closer to a more complete understanding Lasswell’s (1948) famous maxim: “who, says what, in which channel, to whom, [and] with what effect.”

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