From the Hands of an Early Adopter's Avatar to Virtual Junkyards: Analysis of Virtual Goods' Lifetime Survival

One of the major questions in the study of economics, logistics, and business forecasting is the measurement and prediction of value creation, distribution, and lifetime in the form of goods. In "real" economies, a perfect model for the circulation of goods is impossible. However, virtual realities and economies pose a new frontier for the broad study of economics, since every good and transaction can be accurately tracked. Therefore, models that predict goods' circulation can be tested and confirmed before their introduction to "real life" and other scenarios. The present study is focused on the characteristics of early-stage adopters for virtual goods, and how they predict the lifespan of the goods. We employ machine learning and decision trees as the basis of our prediction models. Results provide evidence that the prediction of the lifespan of virtual objects is possible based just on data from early holders of those objects. Overall, communication and social activity are the main drivers for the effective propagation of virtual goods, and they are the most expected characteristics of early adopters.Comment: 28 page

Life in the "Matrix": Human Mobility Patterns in the Cyber Space

With the wide adoption of the multi-community setting in many popular social media platforms, the increasing user engagements across multiple online communities warrant research attention. In this paper, we introduce a novel analogy between the movements in the cyber space and the physical space. This analogy implies a new way of studying human online activities by modelling the activities across online communities in a similar fashion as the movements among locations. First, we quantitatively validate the analogy by comparing several important properties of human online activities and physical movements. Our experiments reveal striking similarities between the cyber space and the physical space. Next, inspired by the established methodology on human mobility in the physical space, we propose a framework to study human "mobility" across online platforms. We discover three interesting patterns of user engagements in online communities. Furthermore, our experiments indicate that people with different mobility patterns also exhibit divergent preferences to online communities. This work not only attempts to achieve a better understanding of human online activities, but also intends to open a promising research direction with rich implications and applications.Comment: To appear at The International AAAI Conference on Web and Social Media (ICWSM) 201

Homofilia por tópicos no espalhamento de memes em redes sociais online

Orientador: André SantanchèDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Um dos problemas centrais na ciência social computacional é entender como a informação se espalha em redes sociais online. Alguns trabalhos afirmam que pessoas que usam estas redes podem não ser capazes de lidar com a quantidade de informação devido às restrições cognitivas, o que resulta em um limite de atenção gasta para ler e compartilhar mensagens. Disso emerge um cenário de competição, em que memes das mensagens visam ser lembrados e compartilhados para que durem mais do que os outros. Esta pesquisa está preocupada em construir uma evidência empírica de que a homofilia desempenha um papel no sucesso de cada meme na competição. A homofilia é um efeito observado quando pessoas preferem interagir com aqueles com os quais se identificam. Coletando dados no Twitter, nós aglomeramos memes em tópicos que são usados para a caracterização da homofilia. Executamos um experimento computacional, baseado num modelo simplificado de memória para adoção de memes, e verificamos que a adoção é influenciada pela homofilia por tópicosAbstract: One of the central problems in the computational social science is to understand how information spreads in online social networks. Some works state that people using these networks may not cope with the amount of information due to cognitive restrictions, resulting in a limit of attention spent reading and sharing messages. A competition scenario emerges, where memes of messages want to be remembered and shared in order to outlast others. This research is concerned with building empirical evidence that homophily plays a role in the success of each meme over the competition. Homophily is an effect observed when people prefer to interact with those they identify with. By gathering data from Twitter, we clustered memes into topics that are used to characterize the homophily. We executed a computational experiment, based on a simplified memory model of meme adoption, and verified that the adoption is influenced by topical homophilyMestradoCiência da ComputaçãoMestre em Ciência da Computação131090/2017-8CNP

Mapping networks of influence: tracking Twitter conversations through time and space

The increasing use of social media around global news events, such as the London Olympics in 2012, raises questions for international broadcasters about how to engage with users via social media in order to best achieve their individual missions. Twitter is a highly diverse social network whose conversations are multi-directional involving individual users, political and cultural actors, athletes and a range of media professionals. In so doing, users form networks of influence via their interactions affecting the ways that information is shared about specific global events. This article attempts to understand how networks of influence are formed among Twitter users, and the relative influence of global news media organisations and information providers in the Twittersphere during such global news events. We build an analysis around a set of tweets collected during the 2012 London Olympics. To understand how different users influence the conversations across Twitter, we compare three types of accounts: those belonging to a number of well-known athletes, those belonging to some well-known commentators employed by the BBC, and a number of corporate accounts belonging to the BBC World Service and the official London Twitter account. We look at the data from two perspectives. First, to understand the structure of the social groupings formed among Twitter users, we use a network analysis to model social groupings in the Twittersphere across time and space. Second, to assess the influence of individual tweets, we investigate the ageing factor of tweets, which measures how long users continue to interact with a particular tweet after it is originally posted. We consider what the profile of particular tweets from corporate and athletes’ accounts can tell us about how networks of influence are forged and maintained. We use these analyses to answer the questions: How do different types of accounts help shape the social networks? and, What determines the level and type of influence of a particular account

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors

Renewable energy in remote communities

This article is the result of a competitively tendered University-funded project, this brings together two major Government Policy areas: sustainable communities and use of carbon fuels, and is aimed at influencing the policy debate on the difficulties of linking remote communities to renewable energy production because of poor distribution networks. Linkage with the Sustainable Communities agenda is an essential ingredient, as the proposal is that the renewable energy technologies will be installed and maintained by the communities themselves

Real-time classification of malicious URLs on Twitter using Machine Activity Data

Massive online social networks with hundreds of millions of active users are increasingly being used by Cyber criminals to spread malicious software (malware) to exploit vulnerabilities on the machines of users for personal gain. Twitter is particularly susceptible to such activity as, with its 140 character limit, it is common for people to include URLs in their tweets to link to more detailed information, evidence, news reports and so on. URLs are often shortened so the endpoint is not obvious before a person clicks the link. Cyber criminals can exploit this to propagate malicious URLs on Twitter, for which the endpoint is a malicious server that performs unwanted actions on the person’s machine. This is known as a drive-by-download. In this paper we develop a machine classification system to distinguish between malicious and benign URLs within seconds of the URL being clicked (i.e. ‘real-time’). We train the classifier using machine activity logs created while interacting with URLs extracted from Twitter data collected during a large global event – the Superbowl – and test it using data from another large sporting event – the Cricket World Cup. The results show that machine activity logs produce precision performances of up to 0.975 on training data from the first event and 0.747 on a test data from a second event. Furthermore, we examine the properties of the learned model to explain the relationship between machine activity and malicious software behaviour, and build a learning curve for the classifier to illustrate that very small samples of training data can be used with only a small detriment to performance