Brand personality in cultural tourism through social media

Purpose: This study aims to analyze the effect of the use of social media on the perception of brand personality and to identify its effect on customer brand engagement. Design/methodology/approach: The study adopted an exploratory approach, adapting Aaker's Brand Personality Scale (1997) to the context of cultural tourism before carrying out a quantitative study resorting to a structural equation modeling (SEM) in order to obtain empirical evidence to identify these relationships. Findings: The findings reveal that the use of social media has a positive effect on the perception of brand personality and that brand personality, likewise, has a positive effect on customer brand engagement. Research implications: This study indicates that transmission of an attractive brand personality according to the desires of the public, combined with dissemination through social media, is a valid strategy to improve customer brand engagement. Originality/value: This study represents an advance in the specialized literature on the value that consumers place on information transmitted through social media. Specifically, it sheds light on how the transmission of brand personality through social media affects customer brand engagement.Spanish Ministry of Education and Vocational Training under Grant FPU 15/0726

Importance of Social Media in the Image Formation of Tourist Destinations from the Stakeholders’ Perspective

This research aimed to provide a more informed and systematic basis on which to develop the positioning strategy in social media due to the interactive capacity and influence that social media has in the success of tourist destinations. In particular, we investigated the role of stakeholders. We carried out an exploratory study using a mixed method which included interviews and an analysis of the activity conducted on the official social media accounts (Facebook, Twitter, and Instagram) of the Spanish regions of Andalusia, Catalonia, and Valencia. The findings provide insight into how tourist destinations promote their image through the use of social media. Social media was found to be a strategic platform for enhancing brand image and achieving tourist engagement. Additionally, the role of stakeholders in supporting and facilitating the image destination strategy is worth highlighting. This study shows that the results achieved by social media can be improved by identifying all stakeholders and defining a content generation strategy by integrating and adding value.FEDER Andalusia Operational Program 2014-2020. Code: UMA18-FEDERJA-14

A regional survey of current practices on destination marketing organizations' Facebook pages: The case of EU and U.S.

Constantly changing nature of social network sites creates the need for continuous process of online benchmarking for identifying practices used by other parties. Facebook as the most used SNS still plays an increasingly important role as a marketing channel for destination marketing organizations (DMO). This paper explores basic characteristics of the official DMO Facebook Pages in order to quantify and present those characteristics in a regional context on the case of two travel markets (EU countries and U.S. states). The results show inconsistent practices in the EU and the USA. When comparing those two markets most similarities in practices are present in general usage of Facebook Pages, while indicative differences are recorded in terms of Page popularity, some posts' characteristics and most evidently in users' engagement. Understanding the Facebook usage practice under the regional spotlight can help DMOs and other service providers to evaluate their activities and if necessary to harmonize it to regional usage practice

Sentiment Analysis of Textual Content in Social Networks. From Hand-Crafted to Deep Learning-Based Models

Aquesta tesi proposa diversos mètodes avançats per analitzar automàticament el contingut textual compartit a les xarxes socials i identificar les opinions, emocions i sentiments a diferents nivells d’anàlisi i en diferents idiomes. Comencem proposant un sistema d’anàlisi de sentiments, anomenat SentiRich, basat en un conjunt ric d’atributs, inclosa la informació extreta de lèxics de sentiments i models de word embedding pre-entrenats. A continuació, proposem un sistema basat en Xarxes Neurals Convolucionals i regressors XGboost per resoldre una sèrie de tasques d’anàlisi de sentiments i emocions a Twitter. Aquestes tasques van des de les tasques típiques d’anàlisi de sentiments fins a determinar automàticament la intensitat d’una emoció (com ara alegria, por, ira, etc.) i la intensitat del sentiment dels autors a partir dels seus tweets. També proposem un nou sistema basat en Deep Learning per solucionar el problema de classificació de les emocions múltiples a Twitter. A més, es va considerar el problema de l’anàlisi del sentiment depenent de l’objectiu. Per a aquest propòsit, proposem un sistema basat en Deep Learning que identifica i extreu l'objectiu dels tweets. Tot i que alguns idiomes, com l’anglès, disposen d’una àmplia gamma de recursos per permetre l’anàlisi del sentiment, a la majoria de llenguatges els hi manca. Per tant, utilitzem la tècnica d'anàlisi de sentiments entre idiomes per desenvolupar un sistema nou, multilingüe i basat en Deep Learning per a llenguatges amb pocs recursos lingüístics. Proposem combinar l’ajuda a la presa de decisions multi-criteri i anàlisis de sentiments per desenvolupar un sistema que permeti als usuaris la possibilitat d’explotar tant les opinions com les seves preferències en el procés de classificació d’alternatives. Finalment, vam aplicar els sistemes desenvolupats al camp de la comunicació de les marques de destinació a través de les xarxes socials. Amb aquesta finalitat, hem recollit tweets de persones locals, visitants i els gabinets oficials de Turisme de diferents destinacions turístiques i es van analitzar les opinions i les emocions compartides en ells. En general, els mètodes proposats en aquesta tesi milloren el rendiment dels enfocaments d’última generació i mostren troballes apassionants.Esta tesis propone varios métodos avanzados para analizar automáticamente el contenido textual compartido en las redes sociales e identificar opiniones, emociones y sentimientos, en diferentes niveles de análisis y en diferentes idiomas. Comenzamos proponiendo un sistema de análisis de sentimientos, llamado SentiRich, que está basado en un conjunto rico de características, que incluyen la información extraída de léxicos de sentimientos y modelos de word embedding previamente entrenados. Luego, proponemos un sistema basado en redes neuronales convolucionales y regresores XGboost para resolver una variedad de tareas de análisis de sentimientos y emociones en Twitter. Estas tareas van desde las típicas tareas de análisis de sentimientos hasta la determinación automática de la intensidad de una emoción (como alegría, miedo, ira, etc.) y la intensidad del sentimiento de los autores de los tweets. También proponemos un novedoso sistema basado en Deep Learning para abordar el problema de clasificación de emociones múltiples en Twitter. Además, consideramos el problema del análisis de sentimientos dependiente del objetivo. Para este propósito, proponemos un sistema basado en Deep Learning que identifica y extrae el objetivo de los tweets. Si bien algunos idiomas, como el inglés, tienen una amplia gama de recursos para permitir el análisis de sentimientos, la mayoría de los idiomas carecen de ellos. Por lo tanto, utilizamos la técnica de Análisis de Sentimiento Inter-lingual para desarrollar un sistema novedoso, multilingüe y basado en Deep Learning para los lenguajes con pocos recursos lingüísticos. Proponemos combinar la Ayuda a la Toma de Decisiones Multi-criterio y el análisis de sentimientos para desarrollar un sistema que brinde a los usuarios la capacidad de explotar las opiniones junto con sus preferencias en el proceso de clasificación de alternativas. Finalmente, aplicamos los sistemas desarrollados al campo de la comunicación de las marcas de destino a través de las redes sociales. Con este fin, recopilamos tweets de personas locales, visitantes, y gabinetes oficiales de Turismo de diferentes destinos turísticos y analizamos las opiniones y las emociones compartidas en ellos. En general, los métodos propuestos en esta tesis mejoran el rendimiento de los enfoques de vanguardia y muestran hallazgos interesa.This thesis proposes several advanced methods to automatically analyse textual content shared on social networks and identify people’ opinions, emotions and feelings at a different level of analysis and in different languages. We start by proposing a sentiment analysis system, called SentiRich, based on a set of rich features, including the information extracted from sentiment lexicons and pre-trained word embedding models. Then, we propose an ensemble system based on Convolutional Neural Networks and XGboost regressors to solve an array of sentiment and emotion analysis tasks on Twitter. These tasks range from the typical sentiment analysis tasks, to automatically determining the intensity of an emotion (such as joy, fear, anger, etc.) and the intensity of sentiment (aka valence) of the authors from their tweets. We also propose a novel Deep Learning-based system to address the multiple emotion classification problem on Twitter. Moreover, we considered the problem of target-dependent sentiment analysis. For this purpose, we propose a Deep Learning-based system that identifies and extracts the target of the tweets. While some languages, such as English, have a vast array of resources to enable sentiment analysis, most low-resource languages lack them. So, we utilise the Cross-lingual Sentiment Analysis technique to develop a novel, multi-lingual and Deep Learning-based system for low resource languages. We propose to combine Multi-Criteria Decision Aid and sentiment analysis to develop a system that gives users the ability to exploit reviews alongside their preferences in the process of alternatives ranking. Finally, we applied the developed systems to the field of communication of destination brands through social networks. To this end, we collected tweets of local people, visitors, and official brand destination offices from different tourist destinations and analysed the opinions and the emotions shared in these tweets

Facebook Activity of Oklahoma Agritourism Operations

Agritourism is an expanding industry in rural areas. Agritourism operators seek to efficiently market their operations and reach consumers far removed from agriculture. Social media serves a crucial role in tourism marketing; however, limited research exists on agritourism marketing to advise agritourism operators or those who advise agritourism operators. A quantitative content analysis was performed on 174 Oklahoman agritourism operation Facebook pages to describe posts, events, and business information created during the month of June 2018.Pages with at least one original post had more page likes than pages without. Amongst farm types, hunting agritourism operations had the lowest proportion of pages with at least one original post, while farm-to-table agritourism operations had the highest proportion. Number of reviews had a very strong relationship to total page likes, while other factors such as number of community and event posts had only a moderate relationship to page likes. Events were not frequent on Oklahoma agritourism Facebook pages, and overall page activity did not have a relationship with the number of people interested in going to events. Amongst original posts, posts created by the agritourism operator were most frequent, followed by posts shared from other sources. Pages with at least one live video or post about an event were most active. Hashtags were infrequently observed amongst Facebook posts, with limited consistency within individual pages or across multiple pages. Pages with an advertisement had more page likes than pages without advertisements.Recommendations to agritourism operators include encouraging agritourism visitors to create Facebook content, utilizing Facebook advertisements, and creating at least one original post. Additionally, agritourism operators should create a variety of types of original posts and utilize advertisements. Perhaps most important, agritourism operations should be wary of emphasizing quantity over quality in Facebook marketing. Future research should qualitatively describe Facebook pages and interview agritourism operators and visitors. Additionally, comparing Facebook data to "real life" data, such as business revenue/expense and number of visitors, could further measure the effective of various Facebook marketing practices