Mining social media to create personalized recommendations for tourist visits

International audiencePhoto sharing platforms users often annotate their trip photos with landmark names. These annotations can be aggregated in order to recommend lists of popular visitor attractions similar to those found in classical tourist guides. However, individual tourist preferences can vary significantly so good recommendations should be tailored to individual tastes. Here we pose this visit personalization as a collaborative filtering problem. We mine the record of visited landmarks exposed in online user data to build a user-user similarity matrix. When a user wants to visit a new destination, a list of potentially interesting visitor attractions is produced based on the experience of like-minded users who already visited that destination. We compare our recommender to a baseline which simulates classical tourist guides on a large sample of Flickr users

The Shortest Path to Happiness: Recommending Beautiful, Quiet, and Happy Routes in the City

When providing directions to a place, web and mobile mapping services are all able to suggest the shortest route. The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant. To quantify the extent to which urban locations are pleasant, we use data from a crowd-sourcing platform that shows two street scenes in London (out of hundreds), and a user votes on which one looks more beautiful, quiet, and happy. We consider votes from more than 3.3K individuals and translate them into quantitative measures of location perceptions. We arrange those locations into a graph upon which we learn pleasant routes. Based on a quantitative validation, we find that, compared to the shortest routes, the recommended ones add just a few extra walking minutes and are indeed perceived to be more beautiful, quiet, and happy. To test the generality of our approach, we consider Flickr metadata of more than 3.7M pictures in London and 1.3M in Boston, compute proxies for the crowdsourced beauty dimension (the one for which we have collected the most votes), and evaluate those proxies with 30 participants in London and 54 in Boston. These participants have not only rated our recommendations but have also carefully motivated their choices, providing insights for future work.Comment: 11 pages, 7 figures, Proceedings of ACM Hypertext 201

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

A Big Data Analytics Method for Tourist Behaviour Analysis

© 2016 Elsevier B.V. Big data generated across social media sites have created numerous opportunities for bringing more insights to decision-makers. Few studies on big data analytics, however, have demonstrated the support for strategic decision-making. Moreover, a formal method for analysing social media-generated big data for decision support is yet to be developed, particularly in the tourism sector. Using a design science research approach, this study aims to design and evaluate a ‘big data analytics’ method to support strategic decision-making in tourism destination management. Using geotagged photos uploaded by tourists to the photo-sharing social media site, Flickr, the applicability of the method in assisting destination management organisations to analyse and predict tourist behavioural patterns at specific destinations is shown, using Melbourne, Australia, as a representative case. Utility was confirmed using both another destination and directly with stakeholder audiences. The developed artefact demonstrates a method for analysing unstructured big data to enhance strategic decision making within a real problem domain. The proposed method is generic, and its applicability to other big data streams is discussed

Identify popular hotspots through the analysis of movement patterns from social networks in rural areas: Case study of the Borbera Valley in North Italy

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesSocial networks are now an increasingly used tool, but analysis possibilities have not yet been fully exploited. In particular, the extraction of information from users' profiles and their processing could give different information. In this work we will focus on the possibilities of using this information to analyse the patterns of rural spaces. The work will be carried out through a review of the available bibliography on the topic, the construction of an application, and the subsequent analysis of the data extracted through the application. Based on the findings, suggestions are made about the intensity of people within an area or the changes that have occurred in social activities

User-Generated Geographic Information for Understanding Human Activities in Nature

In this thesis I have investigated how user-generated data can be applied to studying human-nature interactions on different spatial and temporal scales. User-generated geographic information refers to spatial data sets generated by and about people, such as social media data, sports tracking data, mobile phone data and participatory geographic information. Users of various digital platforms and mobile devices generate considerable amounts of data about their observations, activities and preferences in different environments. These data can potentially be used to fill information gaps about spatial and temporal patterns of human activities in nature. The aim with this thesis is to gain improved understanding of human-nature interactions based on user-generated geographic information with a focus on social media data from national parks and green spaces. The main objectives are to gain 1) a novel understanding about user-generated data, and 2) insights about human activities in nature on different scales through these questions: Where and when are people visiting nature? What are people doing and valuing in nature? Which users have shared their data from national parks and green spaces? This thesis consists of four articles and an introductory section. Article I provides an overview of social media data sources and analysis methods relevant for nature conservation, and highlights that most of the analytical opportunities are still unexplored in the growing body of literature using social media data in conservation science. Article II compares social media data with national park visitor survey and finds similar trends in both data sources regarding popular activities and visited places. Article III compares methods for detecting national park visitors’ place of residence from geotagged social media and assesses biases that affect the analysis. Article IV compares the use of social media data, sports application data, mobile phone data and participatory geographic information for understanding the use of urban green spaces and suggests that combining information from several sources provides a more comprehensive understanding of green space use and preferences. Overall, user-generated geographic information offers valuable insights about where, when and how people use and value nature, especially from areas that are otherwise difficult to monitor. There are several issues related to data access, bias and privacy in these data. Despite evident limitations, these data contribute to a better understanding of human activities in nature and complement traditional data sources with new and dynamic perspectives. In some areas, user-generated data might be the best available information about human activities. Data comparisons from national parks and green areas presented in this thesis also feed into other fields of research using social media and other user-generated data for studying human spatial behaviour.Tämän väitöskirjan tavoitteena on hankkia uutta tietoa ihmisen ja luonnon vuorovaikutussuhteesta sosiaalisen median ja muiden uusien käyttäjälähtöisten paikkatietoaineistojen pohjalta. Tutkimus keskittyy viheralueille ja kansallispuistoihin. Hyödynnän sosiaalisen median aineistoja, sekä muita mobiililaitteiden käytöstä syntyviä aineistoja viheralueiden ja kansallispuistojen käytön tutkimisessa, ja arvioin näiden aineistojen käytettävyyttä maantieteellisen tiedon lähteenä. Tutkimuksen tavoitteena on tarjota menetelmällistä ymmärrystä käyttäjien tuottamien paikkatietoaineistojen hyödyntämisestä luonnonsuojelututkimuksessa, sekä tuottaa tietoa luontovirkistyksen alueellisesta ja ajallisesta vaihtelusta eri mittakaavatasoilla. Tarkastelen tavoitteita seuraavien kysymysten kautta: Missä ja milloin ihmiset viettävät aikaa luonnossa? Mitä ihmiset tekevät viheralueilla ja kansallispuistoissa, ja mitä he näillä alueilla arvostavat? Ketkä jakavat maantieteellistä tietoa luontovierailuistaan? Väitöskirja koostuu johdanto-osiosta ja neljästä osatyöstä. Artikkeli I luo katsauksen sosiaalisen median aineistojen hyödyntämiseen luonnonsuojelututkimuksessa, ja kuvailee keskeiset aineistolähteet ja analyysimenetelmät. Artikkelissa tunnistetaan lähestymistapoja, joiden mahdollisuuksia ei vielä ole täysin hyödynnetty luonnon ja ihmisen vuorovaikutuksen tutkimisessa. Artikkeli II vertailee sosiaalisen median aineistoja kyselytutkimukseen ja kävijätilastoihin Pallas-Yllästunturin kansallispuistosta. Suosituimmat aktiviteetit ja vierailukohteet toistuvat molemmissa aineistoissa. Artikkeli III vertailee aika- ja paikkatietoon pohjautuvia menetelmiä sosiaalisen median käyttäjien kotimaan tunnistamiseen ja arvioi analyysiin liittyviä rajoitteita. Artikkeli IV vertailee sosiaalista mediaa, matkapuhelinaineistoja, urheilusovellusdataa, ja osallistavan paikkatietokyselyn tuloksia kaupungin viheralueiden käytön tutkimisessa. Aineistot tarjoavat toisiaan täydentävää tietoa viheralueiden käytöstä ja arvostuksesta. Käyttäjälähtöiset paikkatietoaineistot auttavat ymmärtämään missä, milloin ja miten ihmiset käyttävät ja arvostavat kansallispuistoja ja viheralueita, erityisesti alueilla joita on muuten hankala monitoroida. Aineistojen epävarma saatavuus kuitenkin rajoittaa näiden aineistojen käyttöä tutkimuksessa. Lisäksi käyttäjäryhmiin ja aineistojen maantieteelliseen kattavuuteen liittyvät vinoumat sekä yksityisyyden suojaan liittyvät kysymykset rajoittavat käytännön sovelluksia. Rajoitteista huolimatta ihmisten itse tuottamat paikkatietoaineistot tarjoavat arvokasta lisätietoa kansallispuistojen ja viheralueiden suunnittelun ja kestävän hallinnan tueksi. Kansallispuistoista ja viheralueilta tuotetut analyysit ja aineistovertailut tarjoavat uutta tietoa myös muille sovellusaloille joilla hyödynnetään uusia aineistoja ihmisten liikkumisen ja aktiviteettien tutkimiseen

Image Based Social Media and The Tourist Gaze A Phenomenological Approach

abstract: The emergence of social media in concert with improved camera and cell phone technologies has helped usher in an age of unprecedented visual communication which has radically changed the tourism industry worldwide. Serving as an important pillar of tourism and leisure studies, the concept of the tourist gaze has been left relatively unexamined within the context of this new visual world and more specifically image based social media. This phenomenological inquiry sought to explore how image based social media impacts the concept of the tourist gaze and furthermore to discover how the democratization of the gaze in concert with specific features of image based social media applications impacts the hermeneutic circle of the tourist gaze. This in-depth analysis of the user experience within the context of travel consisted of 19 semi-structured photo elicitation interviews and incorporated 57 participant generated photos. Six salient themes emerged from the study of this phenomenon; 1) sphere of influence, 2) exchange of information, 3) connections manifested, 4) impression management and content curation, 5) replicated travel photography, and 6) expectations. Analysis of these themes in conjunction with examples from the lived user experience demonstrate that the tourist gaze is being accelerated and expanded by image based social media in a rapid manner. Furthermore, democratization of the gaze as enabled by technological developments and specialized social media platforms is actively shifting the power role away from a small number of mass media influencers towards a larger number of branded individuals and social media influencers. Results of this inquiry support the theoretical assertions that the tourist gaze adapts to social and technological developments and demonstrates that the concept of the tourist gaze is increasingly important within tourism studies. Practical implications regarding the prevalence of real-time information, site visitation, and “taking only pictures” as sustainable touristic behavior are discussed.Dissertation/ThesisMasters Thesis Community Resources and Development 201