A Simple Generative Model of Collective Online Behaviour

Human activities increasingly take place in online environments, providing novel opportunities for relating individual behaviours to population-level outcomes. In this paper, we introduce a simple generative model for the collective behaviour of millions of social networking site users who are deciding between different software applications. Our model incorporates two distinct components: one is associated with recent decisions of users, and the other reflects the cumulative popularity of each application. Importantly, although various combinations of the two mechanisms yield long-time behaviour that is consistent with data, the only models that reproduce the observed temporal dynamics are those that strongly emphasize the recent popularity of applications over their cumulative popularity. This demonstrates---even when using purely observational data without experimental design---that temporal data-driven modelling can effectively distinguish between competing microscopic mechanisms, allowing us to uncover new aspects of collective online behaviour.Comment: Updated, with new figures and Supplementary Informatio

Online behaviour of luxury brand advocates: differences between active advocates and passive loyalists

The study aims to identify online behaviours of luxury brand advocates referring to differentiation between active and passive loyalists. A netnographic approach was used to observe groups of luxury handbag advocates. Key findings include an identification of engagement manifested in positive word of mouth and enthusiastic brand recommendation. Advocates routinely share their love of particular brands, openly expressing joy and sharing heightened levels of self-esteem. Engaged passive loyalists tend to share less with peers, but instead celebrate their purchases more personally

Web log file analysis: backlinks and queries

As has been described else where, web log files are a useful source of information about visitor site use, navigation behaviour, and, to some extent, demographics. But log files can also reveal the existence of both web pages and search engine queries that are sources of new visitors.This study extracts such information from a single web log files and uses it to illustrate its value, not only to th site owner but also to those interested in investigating the online behaviour of web users

Children’s Online Behaviour and Safety Policy and Rights Challenges

This book explores the use of technology in young people’s social lives against a backdrop of “online safety measures” put in place by the UK government to ensure safe and risk free engagement with online services

#any use? What do we know about how teachers and doctors learn through social media use?

This scoping literature review describes the landscape of recent publications (2007-2016) about how teachers and doctors learn through social media to identify whether learning was being considered and, if so, how evidence was collected (N=162). Sixty-seven percent (N=108) were teacher-related and thirty-three percent (N=54) doctor-related, covering empirical studies, literature reviews, position articles and letters to academic journals. Empirical studies were dominant – ninety-one percent (N=98) of teacher-related and sixty-one percent (N=33) of doctor-related – with both fields dominated by in-course evaluations and use/attitude studies. Although doctor-related articles focused on professional online behaviour, rather than professional learning, conference communication and information evaluation were interesting areas of enquiry. Despite professional interest in social media in these professions, there is a dearth of academic studies about their benefits for teacher and doctor learning

Modelling student online behaviour in a virtual learning environment

In recent years, distance education has enjoyed a major boom. Much work at The Open University (OU) has focused on improving retention rates in these modules by providing timely support to students who are at risk of failing the module. In this paper we explore methods for analysing student activity in online virtual learning environment (VLE) -- General Unary Hypotheses Automaton (GUHA) and Markov chain-based analysis -- and we explain how this analysis can be relevant for module tutors and other student support staff. We show that both methods are a valid approach to modelling student activities. An advantage of the Markov chain-based approach is in its graphical output and in the possibility to model time dependencies of the student activities.Comment: In Proceedings of the 2014 Workshop on Learning Analytics and Machine Learning at the 2014 International Conference on Learning Analytics and Knowledge (LAK 2014

Security awareness and affective feedback:categorical behaviour vs. reported behaviour

A lack of awareness surrounding secure online behaviour can lead to end-users, and their personal details becoming vulnerable to compromise. This paper describes an ongoing research project in the field of usable security, examining the relationship between end-user-security behaviour, and the use of affective feedback to educate end-users. Part of the aforementioned research project considers the link between categorical information users reveal about themselves online, and the information users believe, or report that they have revealed online. The experimental results confirm a disparity between information revealed, and what users think they have revealed, highlighting a deficit in security awareness. Results gained in relation to the affective feedback delivered are mixed, indicating limited short-term impact. Future work seeks to perform a long-term study, with the view that positive behavioural changes may be reflected in the results as end-users become more knowledgeable about security awareness

Segmentation of online behaviour : the website & the social network

Effective marketing communication activities require companies to identify and target the right customer segments. This dissertation explores the potential of social network analysis as a tool for online behaviour segmentation. To this end, the patterns of user interactions in the Facebook page of a Portuguese company, alongside clickstream data from its website, were cluster analysed. The cluster analysis of the interaction patterns yielded four clusters, mainly based on differences in content of the posts on Facebook. These clusters were the Photo-fans, Route-lovers, Promo-people and Video-viewers. The SNA metrics were able to provide concrete insights to characterize these segments. The analysis of clickstream data also yielded four clusters: Prospect, Info Seekers, Curious and Scanners. These consumer segments differ in terms of search detail, which could be attributed to their relative level in the purchase process. A field study on the Facebook page was conducted to link the interaction patterns to the browsing behaviour on the website. For the content of the posts during this field study, the clickstream data of the website did not show substantial differences. This dissertation concludes by noting that SNA tools can be useful and provide insights for marketers that attempt to segment social network audiences. Also, the link between the behaviour of social network audience and website visitors potentially leads to useful and actionable insightsAs actividades de marketing eficazes requerem que as empresas sejam capazes de identificar e comunicar aos públicos alvo adequados. Esta dissertação explora o potencial da análise de redes sociais (SNA) como ferramenta de segmentação do comportamento digital. Para este fim, este estudo analisa em clusters os padrões de interacção entre utilizadores da página de Facebook de uma empresa portuguesa, juntamente com os dados das visitas ao website da empresa. A análise de clusters dos padrões de interacção resultou em quatro clusters baseados nas diferenças de conteúdo das publicações no Facebook. Este clusters foram denominados os “Fãs de fotografia”, “Amantes de rotas”, “Pessoas de promoção” e os “Visualizadores de vídeos”. As métricas de SNA forneceram uma visão concreta que caracterizasse estes segmentos. A análise dos dados das visitas ao website gerou também quatro clusters: “Pretendentes”, “Requerentes de informação”, “Curiosos” e os “Scanners”. Estes quatro segmentos diferem em termos de detalhe de pesquisa, o que pode ser atribuído ao seu nível relativo no processo de compra. Foi realizado um estudo de campo na página de Facebook para ligar os padrões de interacção com o comportamento de navegação no website. No caso do tipo de conteúdo publicado durante o estudo, os dados das visitas no website não variaram substancialmente. Esta dissertação conclui que as ferramentas de SNA podem ser úteis na segmentação de audiências nas redes sociais. Contudo, a ligação entre o comportamento nas redes sociais e o comportamento no website pode levar a insights úteis e práticos