Efficiency of Human Activity on Information Spreading on Twitter

Understanding the collective reaction to individual actions is key to effectively spread information in social media. In this work we define efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations. We found that some influential users efficiently cause remarkable collective reactions by each message sent, while the majority of users must employ extremely larger efforts to reach similar effects. Next we propose a model that reproduces the retweet cascades occurring on Twitter to explain the emergent distribution of the user efficiency. The model shows that the dynamical patterns of the conversations are strongly conditioned by the topology of the underlying network. We conclude that the appearance of a small fraction of extremely efficient users results from the heterogeneity of the followers network and independently of the individual user behavior.Comment: 29 pages, 10 figure

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

Can Cascades be Predicted?

On many social networking web sites such as Facebook and Twitter, resharing or reposting functionality allows users to share others' content with their own friends or followers. As content is reshared from user to user, large cascades of reshares can form. While a growing body of research has focused on analyzing and characterizing such cascades, a recent, parallel line of work has argued that the future trajectory of a cascade may be inherently unpredictable. In this work, we develop a framework for addressing cascade prediction problems. On a large sample of photo reshare cascades on Facebook, we find strong performance in predicting whether a cascade will continue to grow in the future. We find that the relative growth of a cascade becomes more predictable as we observe more of its reshares, that temporal and structural features are key predictors of cascade size, and that initially, breadth, rather than depth in a cascade is a better indicator of larger cascades. This prediction performance is robust in the sense that multiple distinct classes of features all achieve similar performance. We also discover that temporal features are predictive of a cascade's eventual shape. Observing independent cascades of the same content, we find that while these cascades differ greatly in size, we are still able to predict which ends up the largest

Efficiency of Human Activity on Information Spreading on Twitter

Understanding the collective reaction to individual actions is key to effectively spread information in social media. In this work we define efficiency on Twitter, as the ratio between the emergent spreading process and the activity employed by the user. We characterize this property by means of a quantitative analysis of the structural and dynamical patterns emergent from human interactions, and show it to be universal across several Twitter conversations. We found that some influential users efficiently cause remarkable collective reactions by each message sent, while the majority of users must employ extremely larger efforts to reach similar effects. Next we propose a model that reproduces the retweet cascades occurring on Twitter to explain the emergent distribution of the user efficiency. The model shows that the dynamical patterns of the conversations are strongly conditioned by the topology of the underlying network. We conclude that the appearance of a small fraction of extremely efficient users results from the heterogeneity of the followers network and independently of the individual user behavior.Comment: 29 pages, 10 figure