Computing the Kullback-Leibler Divergence between two Weibull Distributions

We derive a closed form solution for the Kullback-Leibler divergence between two Weibull distributions. These notes are meant as reference material and intended to provide a guided tour towards a result that is often mentioned but seldom made explicit in the literature

Contextualizing Internet Memes Across Social Media Platforms

Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG -- a recently released knowledge graph of internet memes. We leverage this grounding to highlight the potential of our proposed framework to study the prevalence of memes on different platforms, map them to IMKG, and provide context about memes on social media.Comment: 10 pages, 7 figures, 2 table

Modelação do interesse de vídeos de música medido pelo número de procuras na internet via Google Trends

Mestrado em Econometria Aplicada e PrevisãoO mercado da música mundial continua a se expandir em novos mercados e criar novos negócios, atraindo cada vez mais usuários para os serviços de música sob o formato digital. A receita gerada pela indústria da música digital apresentou um crescimento de 4,3% de 2012 para 2013 (de US$5.6 bi para US$ 5.9 bi), já representa 39% da receita total gerada no mercado mundial. Para uma melhor compreensão da natureza do ciclo de vida do formato digital da música emergente, buscou-se estudar os vídeos de música da internet, dado a sua importância na indústria da música, por ser um dispositivo de marketing destinado, principalmente, a promover as vendas de gravações de música, por ser um importante contributo para a comercialização da música popular e dado a ausência de literatura de caráter qualitativo e quantitativo subjacente. Esta dissertação pretende propor um modelo capaz de descrever a dinâmica dos vídeos de música ao longo do tempo, ou seja, de como se dá o interesse coletivo por um determinado vídeo de música. A base empírica deste estudo consiste em séries temporais de vídeos de música (dados semanais) relacionando frequências de busca disponíveis a partir do Google Trends. Empiricamente avaliou-se o desempenho do modelo proposto, usando métodos de estimação não lineares de séries temporais. Os resultados obtidos permitem distinguir os vídeos de música de internet de curta duração de outros mais duradores.The global music business continues to expand into new markets and create new business, attracting more and more users to digital format music services. The revenues generated by the digital music industry grew by 4.3% from 2012 to 2013 (US$5.6 billion to US$ 5.9 billion), already represents 39% of total revenues generated by the global music market. Internet music videos are a pervasive phenomenon on the Web, they typically consist in a short film made to advertise a popular song that spread through network. In order to contribute to a better understanding of the nature of the life cycle of internet music videos, given its importance in the music industry and in particular plausible models that would explain their temporal dynamics have not previously been reported. Our aim in this paper is thus to develop meaningful and interpretable model that describes the dynamics of music videos over time, i.e., how collective attention to internet music videos evolves over time, and how relate with their life cycle. The empirical basis of our study consists of time series of music videos relating frequencies available search from Google Trends. We conduct an empirical illustration to assess the performance of our model using nonlinear time series models. The results of the empirical illustration indicate to distinguish short and "long" life cycle's internet music videos

Revisit Behavior in Social Media: The Phoenix-R Model and Discoveries

How many listens will an artist receive on a online radio? How about plays on a YouTube video? How many of these visits are new or returning users? Modeling and mining popularity dynamics of social activity has important implications for researchers, content creators and providers. We here investigate the effect of revisits (successive visits from a single user) on content popularity. Using four datasets of social activity, with up to tens of millions media objects (e.g., YouTube videos, Twitter hashtags or LastFM artists), we show the effect of revisits in the popularity evolution of such objects. Secondly, we propose the Phoenix-R model which captures the popularity dynamics of individual objects. Phoenix-R has the desired properties of being: (1) parsimonious, being based on the minimum description length principle, and achieving lower root mean squared error than state-of-the-art baselines; (2) applicable, the model is effective for predicting future popularity values of objects.Comment: To appear on European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 201

Digital Propaganda: The Tyranny of Ignorance

© The Author(s) 2018. The existence of propaganda is inexorably bound to the nature of communication and communications technology. Mass communication by citizens in the digital age has been heralded as a means to counter elite propaganda; however, it also provides a forum for misinformation, aggression and hostility. The extremist group Britain First has used Facebook as a way to propagate hostility towards Muslims, immigrants and social security claimants in the form of memes, leading to a backlash from sites antithetical to their message. This article provides a memetic analysis, which addresses persuasion, organisation, political echo chambers and self-correcting online narratives; arguing that propaganda can be best understood as an evolving set of techniques and mechanisms which facilitate the propagation of ideas and actions. This allows the concept to be adapted to fit a changing political and technological landscape and to encompass both propaganda and counter-propaganda in the context of horizontal communications networks