Location-based social media and the strategic impact for companies

In the last couple of years online social networks expanded to a new field, location (Scellato and Mascolo, 2011). Technologies, such as smartphones and GPS, combined with users’ interest in being connected regardless of their location, created the opportunity for the appearance of location-based social media (Chow et al, 2010). This dissertation focuses in studying if location-based social media has a strategic impact for companies. To contextualize this subject, literature on Web 2.0 and online social media is reviewed. Furthermore, strategic frameworks (Resource Based View) and strategic concepts (Customer Relationship Management and Contextual Marketing) provide the theoretical base through which the discussion is carried. Empirical data collection is conducted in order to understand what are users’ preferences in the context of location-based social media, and to what extent they are willing to interact with companies. Through this process the research hypothesis presented in this dissertation are tested. The results are then extended to the strategic domain, allowing to comprehend under what assumptions location-based social media can be strategic for companies. Through the Resource Based View framework application contextual personalization is considered a factor that may conduct companies to obtain a sustained competitive advantage, by inducing switching costs to their customers, depending on companies’ propensity to appropriate returns from their existing superior capabilities. This dissertation concludes that location-based social networks can have a strategic impact for companies, under the assumptions that network effects exist in location-based social networks and that companies are able to use them in order to perform contextual personalization, originating switching costs for their customers. Additionally, this dissertation aims to contribute for the increase of the current knowledge over an emergent and present subject.Nos últimos dois anos as redes sociais online expandiram-se para uma nova área, localização (Scellato and Mascolo, 2011). Tecnologias, como os “smartphones” e GPS, combinadas com o interesse por parte dos utilizadores em estarem conectados, independentemente da sua localização, criaram a oportunidade para o aparecimento das redes sociais de geo-localização (Chow et al, 2010). Esta dissertação foca-se no estudo da existência ou não de impacto estratégico das redes sociais de geo-localização para as empresas. Para contextualizar este assunto, a literatura sobre Web 2.0 e as redes sociais online é revista. Adicionalmente, “frameworks” (“Resource Based View”) e conceitos (“Customer Relationship Management and Contextual Marketing”) estratégicos providenciam a base teórica através da qual a discussão é conduzida. A recolha de dados empíricos é conduzida com o intuito de compreender quais as preferências dos utilizadores das redes sociais de geo-localização, e até que ponto eles estão dispostos a interagir com as empresas. Através deste processo as hipóteses de investigação foram testadas. Os resultados foram posteriormente estendidos ao domínio estratégico, permitindo compreender sob que pressupostos as redes sociais de geo-localização são estratégicas para as empresas. Através da aplicação do “Resource Based View framework” a personalização contextual é considerada um factor que pode conduzir as empresas à obtenção de uma vantagem competitiva sustentada, induzindo custos de mudança aos seus consumidores, dependendo da capacidade das empresas em se apropriarem de retornos gerados pelas suas capacidades superiores existentes. Esta dissertação conclui que as redes sociais de geo-localização podem ter um impacto estratégico para as empresas, de acordo com os pressupostos de que os efeitos de rede existem nas redes sociais de geo-localização e de que as empresas são capazes de realizar personalização contextual através das mesmas, originando custos de mudança para os seus clientes. Adicionalmente, esta dissertação espera contribuir para o aumento do conhecimento actual sobre um tópico emergente e actual

Word of Mouth, the Importance of Reviews and Ratings in Tourism Marketing

The Internet and social media have given place to what is commonly known as the democratization of content and this phenomenon is changing the way that consumers and companies interact. Business strategies are shifting from influencing consumers directly and induce sales to mediating the influence that Internet users have on each other. A consumer review is “a mixture of fact and opinion, impression and sentiment, found and unfound tidbits, experiences, and even rumor” (Blackshaw & Nazarro, 2006). Consumers' comments are seen as honest and transparent, but it is their subjective perception what shapes the behavior of other potential consumers. With the emergence of the Internet, tourists search for information and reviews of destinations, hotels or services. Several studies have highlighted the great influence of online reputation through reviews and ratings and how it affects purchasing decisions by others (Schuckert, Liu, & Law, 2015). These reviews are seen as unbiased and trustworthy, and considered to reduce uncertainty and perceived risks (Gretzel & Yoo, 2008; Park & Nicolau, 2015). Before choosing a destination, tourists are likely to spend a significant amount of time searching for information including reviews of other tourists posted on the Internet. The average traveler browses 38 websites prior to purchasing vacation packages (Schaal, 2013), which may include tourism forums, online reviews in booking sites and other generic social media websites such as Facebook and Twitter.Peer reviewedFinal Accepted Versio

Spatio-semantic user profiles in location-based social networks

Knowledge of users’ visits to places is one of the keys to understanding their interest in places. User-contributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users’ association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets

Location-Based Social Network Data for Exploring Spatial and Functional Urban Tourists and Residents Consumption Patterns

Urban tourist destinations’ increasing popularity has been a catalyst for discussion about the tourist activity geographical circumscription. In this context, Big Data and more specifically location-based social networks (LBSN), appear as a valuable source of information to approach tourist and residents spatial interactions from a renewed perspective. This paper focuses on approaching similarities and differences between tourists and residents’ geographical and functional use of urban economic units. A user classificatory algorithm has been developed and applied on YELP’s Dataset for that purpose. A residents and tourists integration ratio has then been calculated and applied by types of businesses categories and their associated spatial distribution of the of 11 metropolitan areas provided in the sample: Champaign (Illinois, US), Charlotte (North Carolina, US), Cleveland (Ohio, US), Edinburgh (Scotland, UK), Las Vegas (Nevada, US), Madison (Wisconsin, US), Montreal (Quebec, CA), Pittsburgh (Pennsylvania, US), Phoenix (Arizona, US), Stuttgart (DE) and Toronto (Ontario, CA). Business category results show strong similarities in tourists and residents functional coincidence in the use of urban spaces and leisure offer, while there is a clear geographical concentration of activity for both user types in all analysed case studies

Deep Learning for Learning Representation and Its Application to Natural Language Processing

As the web evolves even faster than expected, the exponential growth of data becomes overwhelming. Textual data is being generated at an ever-increasing pace via emails, documents on the web, tweets, online user reviews, blogs, and so on. As the amount of unstructured text data grows, so does the need for intelligently processing and understanding it. The focus of this dissertation is on developing learning models that automatically induce representations of human language to solve higher level language tasks. In contrast to most conventional learning techniques, which employ certain shallow-structured learning architectures, deep learning is a newly developed machine learning technique which uses supervised and/or unsupervised strategies to automatically learn hierarchical representations in deep architectures and has been employed in varied tasks such as classification or regression. Deep learning was inspired by biological observations on human brain mechanisms for processing natural signals and has attracted the tremendous attention of both academia and industry in recent years due to its state-of-the-art performance in many research domains such as computer vision, speech recognition, and natural language processing. This dissertation focuses on how to represent the unstructured text data and how to model it with deep learning models in different natural language processing viii applications such as sequence tagging, sentiment analysis, semantic similarity and etc. Specifically, my dissertation addresses the following research topics: In Chapter 3, we examine one of the fundamental problems in NLP, text classification, by leveraging contextual information [MLX18a]; In Chapter 4, we propose a unified framework for generating an informative map from review corpus [MLX18b]; Chapter 5 discusses the tagging address queries in map search [Mok18]. This research was performed in collaboration with Microsoft; and In Chapter 6, we discuss an ongoing research work in the neural language sentence matching problem. We are working on extending this work to a recommendation system

Do We Need Help Using Yelp? Regulating Advertising on Mediated Reputation Systems

Yelp, Angie’s List, Avvo, and similar entities enable consumers to access an incredibly useful trove of information about peer experiences with businesses and their goods and services. These “mediated reputation systems,” gatherers and disseminators of consumer peer opinions, are more trusted by consumers than traditional commercial channels. They are omnipresent, carried everywhere on mobile devices, and used by consumers ready to transact. Though this information is valuable, a troubling conflict emerges in its presentation. Most of these reputation platforms rely heavily on advertising sales to support their business models. This reliance compels these entities to display persuasive advertising right along with their presentation of authentic peer information. Consumers expecting to access this authentic peer information must also confront a persuasive message. The revenue lifeblood for these platforms comes from the very businesses under peer review. This Article argues that the power of peer information provides an exceptionally credible context for persuasive advertising. Accordingly, advertising on reputation platforms should trigger more rigorous regulation in the form of disclosure requirements and prioritized enforcement

A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field