Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Digital marketing: how the beauty market has changed with the emergence of digital influencers

Digital influencers are now one of the primary sources of data to which customers turn when seeking advice and suggestions for pre-purchase in a globe characterized by intense competition. The growing power of digital influencers in the industries where they work can increasingly be realized. The brands should attract customers to their primary goal: sales growth. It is possible in many forms, but digital influencers are one of the most precious strategies. It is because digital influencers have a closer connection with clients. Thus, for instance, bloggers and YouTubers are anticipated to have the confidence of their followers, which is very difficult to hope from any other brand promoter. The ultimate objective of strategy type is to induce those followers to purchase their products and remain loyal to the brands. In this context, this research seeks to define possible implications of the followers-blogger relationship on the buying conduct of followers of beauty bloggers. The goal is to formulate feasible theories from information observation using inductive reasoning and a methodology combining quantitative and qualitative methods. Although hard to assess, they proved to be a pushed source of data that every customer of cosmetics has in the account. Concerning purchase decisions, the more evident trend is the introduction of brands and new products. The latter has shown the marketing ability of bloggers to generate requirements. All the follower-blogger interaction improves the interest of followers in beauty products and thus boosts the number of products bought per year.Os influenciadores digitais são agora uma das principais fontes de dados a que os clientes recorrem quando procuram aconselhamento e sugestões de pré-compra num globo caracterizado por uma concorrência intensa. O poder crescente dos influenciadores digitais nas indústrias pode ser cada vez mais percebido. As marcas devem atrair os clientes para sua meta principal: o crescimento das vendas. Isso é possível de muitas formas, mas os influenciadores digitais são uma das estratégias mais preciosas. Isso ocorre porque os influenciadores digitais têm uma conexão mais próxima com os clientes. Assim, por exemplo, bloggers e Youtubers devem ter a confiança de seus seguidores, o que é muito difícil de esperar de qualquer outro promotor de marca. O objetivo final dessa estratégia é induzir esses seguidores a comprar seus produtos e permanecerem leais às marcas. Esta pesquisa busca definir possíveis implicações da relação seguidores-blogger na conduta de compra de seguidores de bloggers de beleza. Utilizando raciocínio indutivo e uma metodologia que combina métodos quantitativos e qualitativos, o objetivo é formular teorias viáveis a partir da observação da informação. Apesar de difíceis de avaliar, elas provaram ser uma fonte de dados que todo cliente de cosméticos tem em conta. No que diz respeito às decisões de compra, a tendência mais clara é a introdução de marcas e novos produtos. Este último tem mostrado a capacidade de marketing dos bloggers para gerar requisitos. A interação seguidor-blogger melhora o interesse dos seguidores em produtos de beleza e, assim, aumenta a quantidade de produtos comprados por ano

Harnessing big data to inform tourism destination management organizations

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceIn the last few years, Portugal has been witnessing a rapid growth of tourism, which reflects positively in many aspects, especially in what regards economic factors. Although, it also leads to a number of challenges, all of them difficult to quantify: tourist congestions, loss of city identity, degradation of patrimony, etc. It is important to ensure that the required foundations and tools to understand and efficiently manage tourism flows exist, both in the city-level and country-level. This thesis studies the potential of Big data to inform destination management organizations. To do so, three sources of Big data are discussed: Telecom, Social media and Airbnb data. This is done through the demonstration and analysis of a set of visualizations and tools, as well as a discussion of applications and recommendations for challenges that have been identified in the market. The study begins with a background information section, where both global and local trends in tourism will be analyzed, as well as the factors that affect tourism and consequences of the latter. As a way to analyze the growth of tourism in Portugal and provide prototypes of important tools for the development of data driven tourism policy making, Airbnb and telecom data are analyzed using a network science approach to visualize country-wide tourist circulation and presents a model to retrieve and analyze social media. In order to compare the results from the Airbnb analysis, data regarding the Portuguese hotel industry is used as control data

Give the Fans What They Want: A Market Segmentation Approach to Sport Fans’ Social Media Usage

The purpose of this study was to construct a model that segments fans of professional sport based on the type of social media platform they preferred to use as well as their social media usage motivations. In addition, the current study sought to investigate whether previously identified motives like escape and socialization, have transformed into more selfish motives such as narcissism. Convenience and snowball sampling techniques were used to collect data from fans of professional sport who specifically used social media to consume sport, resulting in a total sample size of 176. The online survey instrument was comprised of items from the previously validated Motivation Scale for Sport Online Consumption (MSSOC; Seo & Green, 2008) scale and the Narcissism Personality Inventory-16 (NPI-16; Ames, Rose, & Anderson, 2006) scale. In addition, several frequency, usage, and duration items, including how often respondents used Facebook, Twitter, Instagram, and Snapchat, were generated to gauge how often respondents spent time on social media consuming sport. Composite scores were calculated for the MSSOC and NPI-16 responses. Hierarchical cluster analysis revealed three distinct social media preference groups labeled a) Facebook Devotees (n=51), b) Infrequent Users (n=71), and c) Social Media Aficionados (n=54). Facebook Devotees generally preferred to use Facebook more than any other social media platform, while the Social Media Aficionados had the highest mean usage rates for Twitter, Instagram, and Snapchat. Descriptive discriminant analysis indicated that 67% of the differences among Facebook Devotees, Infrequent Users, and Social Media Aficionados can be attributed to social media preference. With regard to social media usage motivation, hierarchical cluster analysis identified two groups labeled a) Multifaceted Fans (n=72) and b) Casual Supporter (n=104). Multifaceted fans exhibited high levels of motivation for nearly all usage motivations, while Casual Supporters had high motivation mean scores for only two motivations, “passing the time,” and “information.” Descriptive discriminant analysis revealed that 61% of the differences between Multifaceted Fans and Casual Supporters was explained by social media usage motivation. Finally, a Pearson correlation analysis (two-tailed) revealed no statistically significant correlations between narcissism and social media usage motivation. Overall, the findings from this study provide sport organizations with valuable marketing and communication information. The fan segments uncovered in the results reveal that fans have different motivations for consuming sport via social media. Sport organizations can use this information to tailor their social media strategy to specific fan segments, increasing engagement, strengthening fans’ brand loyalty, and ultimately generating more revenue

A sentiment based approach to pattern discovery and classification in social media

Social media allows people to participate, express opinions, mediate their own content and interact with other users. As such, sentiment information has become an integral part of social media. This thesis presents a sentiment-based approach to analyse content and social relationships in social media.First, this thesis aims to construct building blocks for sentiment analysis in social media, using sentiment in the form of mood. To that end, the problem of supervised mood classification is investigated. This line of work provides insights into what features in a generic document classification problem can be transferred to a mood classification problem in social media. As data in social media is normally large scale, novel scalable feature sets are introduced for this task. In particular, a novel set of psycholinguistic features is proposed and validated, which does not require a supervised feature selection phase and can therefore be applied for mood analysis at a large scale. Next, under an unsupervised setting, this thesis explores the new problem of pattern discovery in social media using sentiment information. The result is the discovery of intrinsic patterns of moods, each of which can be considered as a group of moods similar to a basic emotion studied in psychology, and therefore providing valuable empirical evidence about the structure of human emotion in the social media domain in a data-driven approach.The second major contribution of this thesis explores the use of sentiment information conveyed in on-line social diaries for detection of real-world events in a large scale setting. In particular, this thesis introduces the novel concept of 'sentiment burst' and employs a stochastic model for detection, and subsequent extraction, of events in social media. The resultant model is a powerful bursty detection algorithm suitable for on-line deployment on ever-growing datasets such as social media. An additional contribution in this line of work is an effective method for evaluating and ranking events using Google Timeline. This offers an objective measure by which to evaluate event detection a topic that is largely under explored in the current literature due to a general lack of human groundtruth.Next, under an egocentric analysis, sentiment information is used to study the impact of the demographics and personalities of users on the messages they create. In particular, we examine how the age and social connectivity of on-line users correlate with the affective, topical and psycholinguistic features of the texts they author. Using a large, ground-truthed dataset of millions of users and on-line diaries, we investigate various important questions posed in social media analysis, psychology and sociology. For example, is there a difference with regard to topic, psycholinguistic features and mood in the messages written by old versus young users? What features are predictive of a user's personality? Of extraversion and introversion? Are there features that are predictive of influence? The results obtained by our sentiment-based approach are encouraging, do not require an expensive feature selection phase and thus suggest a new and promising approach for egocentric analysis in the social media domain.Finally, the sentiment information conveyed in media content is investigated with respect to the networking and interaction aspects of a social media system. Sentiment information is studied in parallel with two other common aspects of social media content: topics and linguistic styles. Sentiment information is proved in this thesis to provide additional insights into the process of community formation. It is also shown to be a powerful predictor of community membership for a message or a user at a lighter computational cost

Distances in the field : mapping similarity and familiarity in the production, curation and consumption of Australian art music

This thesis provides a timely intervention in the investigation of cultural fields by employing traditional and new data analytics to expand our understanding of fields as multi-dimensional sites of production, curation and consumption. Through a case study of contemporary Australian art music, the research explores the multiple ways in which the concept of ‘distance’ contributes to how we conceive of and engage with fields of artistic practice. While the concept of distance has often been an implicit or axiomatic concern for cultural sociology, this thesis foregrounds how it can be used to analyse fields from multiple perspectives, at multiple scales of enquiry and using diverse methodologies. In doing so, it distinguishes between notions of distance in the related concepts of similarity and familiarity. In the former, the relative proximities of cultural producers can be mapped to discern and contrast the organising principles which underlie different perspectives of a field. In the latter, the degree of an individual’s familiarity with an item or genre can be included in theorisations of cultural preferences and their social dimensions. This is disrupted in a field such as Australian art music, however, as its emphasis on experimentation and innovation presents barriers to developing familiarity. Distance can be considered a defining characteristic of this field, and motivates its selection as a critical case study from which to investigate how audiences form attachments to distant musical sounds. The investigation of distance from multiple perspectives, using different scales of analysis and across a series of focal points in the lifecycle of artist practice, provides an analysis of Australian art music in terms of the tensions which emerge from these intersecting representations of the field. The singular spatial representation of ‘objective relations’ in a field, and a concern with power and domination – as found in the approach of Bourdieu – is replaced by a multiplicity of sets of relations and a concern with their organising principles and juxtapositions. The thesis argues that the actor constellations which distances produce are intimately linked to our capacity to engage with fields as discrete and knowable domains of cultural practice. Beyond our capacity to know a cultural field, it also argues for the importance of reconsidering how we form attachments to distant musical tastes. As an avant-garde genre which embraces foreign and confounding sounds, audiences require the capacity to draw on a range of consumption strategies and techniques to successfully engage with and value the unfamiliar

Social media data for conservation science : A methodological overview

Improved understanding of human-nature interactions is crucial to conservation science and practice, but collecting relevant data remains challenging. Recently, social media have become an increasingly important source of information on human-nature interactions. However, the use of advanced methods for analysing social media is still limited, and social media data are not used to their full potential. In this article, we present available sources of social media data and approaches to mining and analysing these data for conservation science. Specifically, we (i) describe what kind of relevant information can be retrieved from social media platforms, (ii) provide a detailed overview of advanced methods for spatio-temporal, content and network analyses, (iii) exemplify the potential of these approaches for real-world conservation challenges, and (iv) discuss the limitations of social media data analysis in conservation science. Combined with other data sources and carefully considering the biases and ethical issues, social media data can provide a complementary and cost-efficient information source for addressing the grand challenges of biodiversity conservation in the Anthropocene epoch.Peer reviewe