Tourist Attraction Popularity Mapping based on Geotagged Tweets

Abstract

The development of tourist attractions is now highly influenced by social media. The speed at which information can be disseminated via the Internet has become an essential factor in enabling distinct tourist attractions to potentially gain high popularity in a relatively short time. This condition was not as prevalent several years ago when tourism promotion remained limited to a certain kind of media. As a consequence, rapid change in the relative popularity of tourist attractions is inevitable. Against this, knowledge of tourist attraction hotspots is essential in tourism management. This means there is a need to study how to both quickly determine the popularity level of tourist attractions and encompass a relatively large area. This article utilised tweet data from microblogging website Twitter as the basis from which to determine the popularity level of a tourist attraction. Data mining was conducted using Python and the Tweepy module. The tweet data were collected at the end of April and early May 2017, at times when there are several long holiday weekends. A Tweet Proximity Index (TPI) was used to calculate both the density and frequency of tweets based on a defined search radius. A Density Index (DI) was also used as a technique for determining the popularity. The results from both approaches were then compared to a random survey about people’s perceptions of tourist attractions in the study area. The result shows that geotagged tweet data can be used to determine the popularity of a tourist attraction, although it still only achieved a medium level of accuracy. The TPI approach used in this study produced an accuracy of 76.47%, while the DI achieved only 58.82%. This medium accuracy does indicate that the two approaches are not yet strong enough to be used for decision-making but should be more than adequate as an initial description. Further, it is necessary to improve the method of indexing and the exploration of other aspects of Twitter data.

Crowded and popular: The two sides of the coin affecting theme-park experience, satisfaction, and loyalty

“…Scholars have thus proposed various approaches to discover popular attractions from geotagged data. For example, Wibowo, Bustomi, and Sukamdi (2019) showed that geotagged Twitter data can be used to determine the popularity of a tourist attraction, although it achieved only a medium level of accuracy. Peng and Huang (2017) extracted hotspots by integrating spatial clustering and text-mining approaches, also using Flickr geotagged images to discover popular tourist attractions.…”

Revealing Tourist Hotspots in Yogyakarta City Based on Social Media Data Clustering

“…Information about tourist hotspot clusters is therefore needed, especially in the current era, as many tourist sites prioritize their photographic quality. Photogenic attractions have the potential to gain popularity through information dissemination on social media (Wibowo et al, 2019).…”

Pemanfaatan Data Twitter Dalam Penanggulangan Bencana Banjir dan Longsor

“…Informasi lokasi didapat jika pengguna twiiter menyematkan informasi lokasi pada tweet mereka. Penyematan informasi lokasi ini sifatnya opsional sehingga pengguna bisa memilih untuk tidak menyematkan informasi lokasi pada tweet mereka, seperti sebuah penelitian yang menyatakan bahwa hanya 7,9% dari jumlah responden yang mengaktifkan informasi lokasi di tweet mereka [28]. Informasi lokasi yang didapat dari DEA bukan berasal dari penyematan lokasi dari tweet yang dibuat, tetapi dari lokasi yang dicantumkan di profil.…”

“…Sehingga membuat tweet yang memiliki informasi lokasi atau bergeotag mayoritas berada di Pulau Jawa, sehingga penyajian peta sebaran tweet dalam penelitian ini hanya difokuskan di Pulau Jawa dan dalam rentang waktu puncak percakapan. Peta sebaran tweet umumnya dibuat dengan analisis kepadatan yaitu kernel density [8,28,29].…”

“…Fungsi kota wisata membuat Kota Yogyakarta banyak dikunjungi terutama pada liburan dan akhir pekan. Obyek wisata seperti Jalan Malioboro merupakan salah satu lokasi paling populer, sehingga memiliki kepadatan tweet tertinggi [28]. Apalagi waktu liburan bertepatan pada masa krisis bencana banjir Jakarta dan longsor di Bogor (1-5 Januari).…”