An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal

Devkota, Bidur; Miyazaki, Hiroyuki

doi:10.1109/cccs.2018.8586827

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…The developed algorithm exploits Twitter's functionalities to find a greater number of tweets with geographical location than methods that relied only on point geotagged and bounding box. They are methods, used previously by researchers to analyze mobility [33] , [31] , [41] , given that they offer positional coordinates of tweets - very accurate in the former and approximates in the latter -, but with the limitations on the amount of tweets available. Thus, by the end of the procedure, each geolocalized tweet can have one of these kinds of geographical tags: point geotagged, bounding box, local tweet and QCA tweet.…”

Section: Resultsmentioning

confidence: 99%

“…Twitter was used in analyzing tourism-related mobility as well. For example, to analyze the home country of tourists in Nepal [41] ; to check the official visitor counts in national parks [42] ; to analyze the relation between visitation rate of New York City's parks and their characteristics [43] , [44] . Generally, the tweet location used in previously cited analyses was that given by Twitter, either by an accurate localization or an approximate area where the tweet could be found.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating mobility of tourists. New Twitter-based procedure

Dueñas¹,

Martínez-Comesaña²,

Martínez³

et al. 2023

Heliyon

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating mobility of tourists. New Twitter-based procedure

Dueñas¹,

Martínez-Comesaña²,

Martínez³

et al. 2023

Heliyon

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“…The literature on recommender systems in the Nepalese context are limited. There are notably two authors [25][26][27][28][29][30] who have worked on the study of Tourist Recommender Systems for Nepalese tourism industries. In their papers [25][26][27][28][29][30], the author studied different aspects related to tweets and POI and the generation and distribution of geotagged tweets in Nepal, while [27] used volunteered geographic information and night-time light remote sensing data to identify tourism areas of interest [28].…”

Section: Literature Reviewmentioning

confidence: 99%

“…There are notably two authors [25][26][27][28][29][30] who have worked on the study of Tourist Recommender Systems for Nepalese tourism industries. In their papers [25][26][27][28][29][30], the author studied different aspects related to tweets and POI and the generation and distribution of geotagged tweets in Nepal, while [27] used volunteered geographic information and night-time light remote sensing data to identify tourism areas of interest [28]. The other author [29] worked on the design of religious tourist recommender systems and conducted a preliminary analysis on the design of a Tourist Recommender System for Nepal [30].…”

Section: Literature Reviewmentioning

confidence: 99%

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Shrestha,

Wenan,

Shrestha

et al. 2024

Computation

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This study introduces a data-driven and machine-learning approach to design a personalized tourist recommendation system for Nepal. It examines key tourist attributes, such as demographics, behaviors, preferences, and satisfaction, to develop four sub-models for data collection and machine learning. A structured survey is conducted with 2400 international and domestic tourists, featuring 28 major questions and 125 variables. The data are preprocessed, and significant features are extracted to enhance the accuracy and efficiency of the machine-learning models. These models are evaluated using metrics such as accuracy, precision, recall, F-score, ROC, and lift curves. A comprehensive database for Pokhara City, Nepal, is developed from various sources that includes attributes such as location, cost, popularity, rating, ranking, and trend. The machine-learning models provide intermediate categorical recommendations, which are further mapped using a personalized recommender algorithm. This algorithm makes decisions based on weights assigned to each decision attribute to make the final recommendations. The system’s performance is compared with other popular recommender systems implemented by TripAdvisor, Google Maps, the Nepal tourism website, and others. It is found that the proposed system surpasses existing ones, offering more accurate and optimized recommendations to visitors in Pokhara. This study is a pioneering one and holds significant implications for the tourism industry and the governing sector of Nepal in enhancing the overall tourism business.

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“…Related studies have used geo-tagged social media data for mining tourist locations without separating the data from local and non-local users [20,49]. Also, previous studies in the proposed area (i.e., Nepal) illustrated that approximately one third portion of the tweets was contributed by non-local users [61]. Hence, to avail more input for the underlying clustering algorithm, the proposed approach makes use of the tweets posted by both local and non-local users in the beginning.…”

Section: Identifying Taois By Cluster Pruningmentioning

confidence: 99%

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

et al. 2019

Self Cite

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Easy, economical, and near-real-time identification of tourism areas of interest is useful for tourism planning and management. Numerous studies have been accomplished to analyze and evaluate the tourism conditions of a place using free and near-real-time data sources such as social media. This study demonstrates the potential of volunteered geographic information, mainly Twitter and OpenStreetMap, for discovering tourism areas of interest. Active tweet clusters generated using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm and building footprint information are used to identify touristic places that ensure the availability of basic essential facilities for travelers. Furthermore, an investigation is made to examine the usefulness of nighttime light remotely sensed data to recognize such tourism areas. The study successfully discovered important tourism areas in urban and remote regions in Nepal which have relatively low social media penetration. The effectiveness of the proposed framework is examined using the F1 measure. The accuracy assessment showed F1 score of 0.72 and 0.74 in the selected regions. Hence, the outcomes of this study can provide a valuable reference for various stakeholders such as tourism planners, urban planners, and so on.

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An Exploratory Study on the Generation and Distribution of Geotagged Tweets in Nepal

Cited by 7 publications

References 17 publications

Estimating mobility of tourists. New Twitter-based procedure

Estimating mobility of tourists. New Twitter-based procedure

Personalized Tourist Recommender System: A Data-Driven and Machine-Learning Approach

Using Volunteered Geographic Information and Nighttime Light Remote Sensing Data to Identify Tourism Areas of Interest

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