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

Evolution of Ego-networks in Social Media with Link Recommendations

Ego-networks are fundamental structures in social graphs, yet the process of their evolution is still widely unexplored. In an online context, a key question is how link recommender systems may skew the growth of these networks, possibly restraining diversity. To shed light on this matter, we analyze the complete temporal evolution of 170M ego-networks extracted from Flickr and Tumblr, comparing links that are created spontaneously with those that have been algorithmically recommended. We find that the evolution of ego-networks is bursty, community-driven, and characterized by subsequent phases of explosive diameter increase, slight shrinking, and stabilization. Recommendations favor popular and well-connected nodes, limiting the diameter expansion. With a matching experiment aimed at detecting causal relationships from observational data, we find that the bias introduced by the recommendations fosters global diversity in the process of neighbor selection. Last, with two link prediction experiments, we show how insights from our analysis can be used to improve the effectiveness of social recommender systems.Comment: Proceedings of the 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK. 10 pages, 16 figures, 1 tabl

Modeling Content Lifespan in Online Social Networks Using Data Mining

Online Social Networks (OSNs) are integrated into business, entertainment, politics, and education; they are integrated into nearly every facet of our everyday lives. They have played essential roles in milestones for humanity, such as the social revolutions in certain countries, to more day-to-day activities, such as streaming entertaining or educational materials. Not surprisingly, social networks are the subject of study, not only for computer scientists, but also for economists, sociologists, political scientists, and psychologists, among others. In this dissertation, we build a model that is used to classify content on the OSNs of Reddit, 4chan, Flickr, and YouTube according the types of lifespan their content have and the popularity tiers that the content reaches. The proposed model is evaluated using 10-fold cross-validation, using data mining techniques of Sequential Minimal Optimization (SMO), which is a support vector machine algorithm, Decision Table, Naïve Bayes, and Random Forest. The run times and accuracies are compared across OSNs, models, and data mining algorithms. The peak/death category of Reddit content can be classified with 64% accuracy. The peak/death category of 4Chan content can be classified with 76% accuracy. The peak/death category of Flickr content can classified with 65% accuracy. We also used 10-fold cross-validation to measure the accuracy in which the popularity tier of content can be classified. The popularity tier of content on Reddit can be classified with 84% accuracy. The popularity tier of content on 4chan can be classified with 70% accuracy. The popularity tier of content on Flickr can be classified with 66% accuracy. The popularity tier of content on YouTube can be classified with only 48% accuracy. Our experiments compared the runtimes and accuracy of SMO, Naïve Bayes, Decision Table, and Random Forest to classify the lifespan of content on Reddit, 4chan, and Flickr as well as classify the popularity tier of content on Reddit, 4chan, Flickr, and YouTube. The experimental results indicate that SMO is capable of outperforming the other algorithms in runtime across all OSNs. Decision Table has the longest observed runtimes, failing to complete analysis before system crashes in some cases. The statistical analysis indicates, with 95% confidence, there is no statistically significant difference in accuracy between the algorithms across all OSNs. Reddit content was shown, with 95% confidence, to be the OSN least likely to be misclassified. All other OSNs, were shown to have no statistically significant difference in terms of their content being more or less likely to be misclassified when compared pairwise with each other

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

The International Affiliation Network of YouTube Trends

Online video, a ubiquitous, visual, and highly shareable medium, is well-suited to crossing geographic, cultural, and linguistic barriers. Trending videos in particular, by virtue of reaching a large number of viewers in a short span of time, are powerful as both influencers and indicators of international communication flows. In this work, we study a large set of videos trending across 57 nations, collected from YouTube over a 7-month period. We consider the set as a network of content flowing between nations, then develop conditional co-affiliation, a nation-nation co-affiliation index that enables a meaningful interpretation of network path length and the application of betweenness centrality. We observe a highly-interlinked network with remarkably similar co-affiliation levels between very different nations. However, Arabic-speaking nations appear more isolated, with the U.A.E. emerging as a key bridge. By analyzing video trend lifespans, we show that nations having many globally-popular video trends are reliably not the nation where those trends are strongest: we see no evidence to support the widely discussed idea of cultural exporter or trendsetter nations. We model correlations between co-affiliation and a selection of contextual factors. We note a surprisingly complex interaction between migration and shared video trends. Consistent with existing work on video popularity, we find that long trending times within one nation do not necessarily translate to reaching a wide global audience. This work expands on previous studies of the geographic popularity of videos by incorporating trending data and extending our analysis from video-nation affiliations to nation-nation co-affiliations. Characterizing these relationships is key to understanding the international cultural impact and potential of online video

Who Contributes to the Knowledge Sharing Economy?

Information sharing dynamics of social networks rely on a small set of influencers to effectively reach a large audience. Our recent results and observations demonstrate that the shape and identity of this elite, especially those contributing \emph{original} content, is difficult to predict. Information acquisition is often cited as an example of a public good. However, this emerging and powerful theory has yet to provably offer qualitative insights on how specialization of users into active and passive participants occurs. This paper bridges, for the first time, the theory of public goods and the analysis of diffusion in social media. We introduce a non-linear model of \emph{perishable} public goods, leveraging new observations about sharing of media sources. The primary contribution of this work is to show that \emph{shelf time}, which characterizes the rate at which content get renewed, is a critical factor in audience participation. Our model proves a fundamental \emph{dichotomy} in information diffusion: While short-lived content has simple and predictable diffusion, long-lived content has complex specialization. This occurs even when all information seekers are \emph{ex ante} identical and could be a contributing factor to the difficulty of predicting social network participation and evolution.Comment: 15 pages in ACM Conference on Online Social Networks 201