Understanding Group Structures and Properties in Social Media

Abstract. The rapid growth of social networking sites enables people to connect to each other more conveniently than ever. With easy-to-use social media, people contribute and consume contents, leading to a new form of human interaction and the emergence of online collective behav-ior. In this chapter, we aim to understand group structures and proper-ties by extracting and profiling communities in social media. We present some challenges of community detection in social media. A prominent one is that networks in social media are often heterogeneous. We intro-duce two types of heterogeneity presented in online social networks and elaborate corresponding community detection approaches for each type, respectively. Social media provides not only interaction information but also textual and tag data. This variety of data can be exploited to profile individual groups in understanding group formation and relationships. We also suggest some future work in understanding group structures and properties. Key words: social media, community detection, group profiling, het-erogeneous networks, multi-mode networks, multi-dimensional networks

MC2: MPEG-7 content modelling communities

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThe use of multimedia content on the web has grown significantly in recent years. Websites such as Facebook, YouTube and Flickr cater for enormous amounts of multimedia content uploaded by users. This vast amount of multimedia content requires comprehensive content modelling otherwise retrieving relevant content will be challenging. Modelling multimedia content can be an extremely time consuming task that may seem impossible particularly when undertaken by individual users. However, the advent of Web 2.0 and associated communities, such as YouTube and Flickr, has shown that users appear to be more willing to collaborate in order to take on enormous tasks such as multimedia content modelling. Harnessing the power of communities to achieve comprehensive content modelling is the primary focus of this research. The aim of this thesis is to explore collaborative multimedia content modelling and in particular the effectiveness of existing multimedia content modelling tools, taking into account the key development challenges of existing collaborative content modelling research and the associated modelling tools. Four research objectives are pursued in order to achieve this; first, design a user experiment to study users’ tagging behaviour with existing multimedia tagging tools and identify any relationships between such user behaviour; second, design and develop a framework for MPEG-7 content modelling communities based on the results of the experiment; third, implement an online service as a proof of concept of the framework; fourth, validate the framework through the online service during a repeat of the initial user experiment. This research contributes first, a conceptual model of user behaviour visualised as a fuzzy cognitive map and, second, an MPEG-7 framework for multimedia content modelling communities (MC2) and its proof of concept as an online service. The fuzzy cognitive model embodies relationships between user tagging behaviour and context and provides an understanding of user priorities in the description of content features and the relationships that exist between them. The MC2 framework, developed based on the fuzzy cognitive model, is deep-rooted in user content modelling behaviour and content preferences. A proof of concept of the MC2 framework is implemented as an online service in which all metadata is modelled using MPEG-7. The online service is validated, first, empirically with the same group of users and through the same experiment that led to the development of the fuzzy cognitive model and, second, functionally against the folksonomy and MPEG-7 content modelling tools used in the initial experiment. The validation demonstrates that MC2 has the advantages without the shortcomings of existing multimedia tagging tools by harnessing the ease of use of folksonomy tools while producing comprehensive structured metadata.Supported by UK Engineering and Physical Sciences Research Council (EPSRC

Processo de mineração de conteúdos em mídias sociais para auxílio na gestão de destinos turísticos

Orientador: Prof. Dr. Alexandre Augusto BizDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Humanas, Programa de Pós-Graduação em Turismo. Defesa: Curitiba, 16/12/2014Inclui bibliografiaResumo: O presente estudo teve por objetivo propor um processo de mineração de conteúdos em mídias sociais para auxiliar na gestão de destinos turísticos composto por sete fases, elaborado com base nas metodologias propostas por Neves (2013), Hea, Zha e Li (2013), Kalampokis, Tambouris e Tarabanis (2013), Abrahams, Jiao, Fan, Wang e Zhang (2013) e nos modelos de descoberta de conhecimento propostos por Fayyad, Piatetsky-Shapiro e Smyth (1996), Chapman et al. (2000) e Han, Kamber e Pei (2012). Caracteriza-se como pesquisa exploratória e descritiva e como método de investigação foram utilizados métodos mistos. Embora explore o monitoramento nas mídias sociais Facebook, Twitter e YouTube, o processo proposto foi verificado a partir da mineração de conteúdos do Twitter que tivessem os termos da ontologia de aplicação de atrativos e serviços turísticos (hospedagem, alimentação e transportes) das cidades de Curitiba (PR) e Foz do Iguaçu (PR), por opção metodológica e pela dificuldade em obter dados relevantes nas demais mídias sociais investigadas devido a limitações em suas Application Programming Interface (API). O presente processo mostrou-se ser eficaz para coletar conteúdos relevantes e identificar assuntos populares nas mídias sociais, realizar análises quantitativas e qualitativas, bem como auxiliar às Destination Management Organizations – DMO na gestão de destinos turísticos, bem como no processo de tomada de decisões estratégicas e operacionais. Como resultado das análises da utilização das mídias sociais pelas DMO investigadas, constatou-se que o Facebook e o Twitter são mais utilizadas do que o YouTube, que ainda é pouco explorado em relação às demais. Identificou-se ainda que apesar das ações, estratégias e conteúdos publicados serem semelhantes, as abordagens e objetivos variam e os esforços e ações das DMO nas mídias sociais ainda são experimentais. Através das entrevistas semiestruturadas pessoais realizadas com os responsáveis pela gestão e atualização dos perfis em mídias sociais das DMO constatou-se que nenhuma DMO monitora as mídias sociais efetivamente utilizando softwares de monitoramento de mídias sociais ou técnicas de mineração de conteúdos. Entretanto, ainda que superficialmente, as DMO utilizam-se da ferramenta analítica do Facebook para monitorar e analisar o desempenho das ações e publicações. Por fim, foi possível identificar a inexperiência, a falta de conhecimento técnico e de recursos humanos e financeiros como as principais limitações frente a utilização e monitoramento de mídias sociais pelas DMO investigadas. Como sugestão de trabalhos futuros, sugere-se a elaboração do modelo teórico de gestão do conhecimento para que os resultados e conhecimentos obtidos sejam explicitados às instâncias de governança (federal, estadual e municipal), para os demais atores públicos e privados envolvidos na atividade turística, a ampliação das ontologias de aplicação elaboradas e ao monitoramento e mineração de conteúdos em mídias sociais sobre outras organizações turísticas públicas e privadas ou outros eventos como as Olimpíadas no Rio de Janeiro em 2016. Palavras-chave: turismo; gestão de destinos turísticos; mídias sociais; monitoramento em mídias sociais; mineração de conteúdos em mídias sociais.Abstract: This exploratory and descriptive research used mixed to propose a social media mining process and framework to support tourist destinations management based on the proposed methodologies by Neves (2013), Hea, Zha and Li (2013), Kalampokis, Tambouris and Tarabanis (2013), Abrahams, Jiao, Fan, Wang e Zhang (2013) and other knowledge-discovery in databases (KDD) models proposed by Fayyad, Piatetsky-Shapiro and Smyth (1996), Chapman et al. (2000) and Han, Kamber and Pei (2012). Although the study exploits social media mining on Facebook, Twitter and YouTube, the process was verified purely on Twitter content containing tourist attractions and services keywords or hashtags (#) of the cities of Curitiba-PR and Foz do Iguaçu-PR – Brazil, presented on the application ontology developed. The data analysis using only data from Twitter was a methodological option because of the difficulty to collect relevant and reliable data from the other investigated social medias due to its Application Programming Interface’s limitations. The process proved to be effective and successful to collect relevant data, identify popular topics on social media, perform quantitative and qualitative analyzes as well as assisting the Destination Management Organizations – DMO in destination management and decision making process. Findings of the use of social media by the DMO analyzed shows that Facebook and Twitter are the main social media used and YouTube can be better explored. It was found that despite the actions, strategies and contents published are similar, the approaches and goals vary and the social media efforts and actions are still experimental. Finally, through semi-structured interviews conducted with the responsible for managing and updating the DMO’s social media profiles was found that any DMO effectively monitors its social media using a social media monitor software or data mining techniques. However, even superficially, the DMOs use the analytical tool offered by Facebook itself to monitor and analyze its social media performance. It was also identified that inexperience, lack of technical knowledge, human and financial resources are the main constraints facing the social media use and monitoring by DMO investigated. Suggestions for future work are the knowledge management theoretical model development, the expansion of the ontologies, monitoring other public and private tourist organizations and other events such as the Rio 2016 Summer Olympics held in Rio de Janeiro, Brazil. Key words: tourism; destination management; social media; social media monitoring; social media mining