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

A hybrid multi-criteria decision-making approach to order natural gas consuming countries

Societies worldwide are committed to moving towards a low carbon economy, and natural gas is considered a transition fuel between fossil (such as gasoline and diesel) and renewable fuels. Based on the relevance of natural gas in this economic transition, this paper demonstrates the application of a hybrid multi-criteria decision-making approach to order the natural gas consuming countries. The aim is to support decision-making in the natural gas market, offering elements to prioritize the trade worldwide. The study observed three criteria: consumption variation for years 2014 to 2016; the volume of production in the same period; and proven natural gas reserves in 2016. The data to demonstrate the countries’ performance was obtained from a yearly statistic publication of the Brazilian National Agency of Petroleum, Natural Gas, and Biofuels (ANP), released in 2017. Finally, the decision-making methods adopted to assess the criteria were, first, the WINGS method, applied to generate the weights of each criterion. Second, the study adopted the TOPSIS method to pre-select the countries closest to becoming a global consumer of natural gas. After applying the TOPSIS method, a pre-analysis of dominance among alternatives (pre-selected countries) was conducted, excluding the dominated ones from the list obtained. Third, the PROMÉTHÉE II method was applied to establish the order of the natural gas-consuming countries

Towards Understanding Political Interactions on Instagram

Online Social Networks (OSNs) allow personalities and companies to communicate directly with the public, bypassing filters of traditional medias. As people rely on OSNs to stay up-to-date, the political debate has moved online too. We witness the sudden explosion of harsh political debates and the dissemination of rumours in OSNs. Identifying such behaviour requires a deep understanding on how people interact via OSNs during political debates. We present a preliminary study of interactions in a popular OSN, namely Instagram. We take Italy as a case study in the period before the 2019 European Elections. We observe the activity of top Italian Instagram profiles in different categories: politics, music, sport and show. We record their posts for more than two months, tracking "likes" and comments from users. Results suggest that profiles of politicians attract markedly different interactions than other categories. People tend to comment more, with longer comments, debating for longer time, with a large number of replies, most of which are not explicitly solicited. Moreover, comments tend to come from a small group of very active users. Finally, we witness substantial differences when comparing profiles of different parties.Comment: 5 pages, 8 figure