PopRank: Ranking pages' impact and users' engagement on Facebook

Users online tend to acquire information adhering to their system of beliefs and to ignore dissenting information. Such dynamics might affect page popularity. In this paper we introduce an algorithm, that we call PopRank, to assess both the Impact of Facebook pages as well as users' Engagement on the basis of their mutual interactions. The ideas behind the PopRank are that i) high impact pages attract many users with a low engagement, which means that they receive comments from users that rarely comment, and ii) high engagement users interact with high impact pages, that is they mostly comment pages with a high popularity. The resulting ranking of pages can predict the number of comments a page will receive and the number of its posts. Pages impact turns out to be slightly dependent on pages' informative content (e.g., science vs conspiracy) but independent of users' polarization.Comment: 10 pages, 5 figure

Structural Patterns of the Occupy Movement on Facebook

In this work we study a peculiar example of social organization on Facebook: the Occupy Movement -- i.e., an international protest movement against social and economic inequality organized online at a city level. We consider 179 US Facebook public pages during the time period between September 2011 and February 2013. The dataset includes 618K active users and 753K posts that received about 5.2M likes and 1.1M comments. By labeling user according to their interaction patterns on pages -- e.g., a user is considered to be polarized if she has at least the 95% of her likes on a specific page -- we find that activities are not locally coordinated by geographically close pages, but are driven by pages linked to major US cities that act as hubs within the various groups. Such a pattern is verified even by extracting the backbone structure -- i.e., filtering statistically relevant weight heterogeneities -- for both the pages-reshares and the pages-common users networks

Everyday the Same Picture: Popularity and Content Diversity

Facebook is flooded by diverse and heterogeneous content, from kittens up to music and news, passing through satirical and funny stories. Each piece of that corpus reflects the heterogeneity of the underlying social background. In the Italian Facebook we have found an interesting case: a page having more than $40K$ followers that every day posts the same picture of a popular Italian singer. In this work, we use such a page as a control to study and model the relationship between content heterogeneity on popularity. In particular, we use that page for a comparative analysis of information consumption patterns with respect to pages posting science and conspiracy news. In total, we analyze about $2M$ likes and $190K$ comments, made by approximately $340K$ and $65K$ users, respectively. We conclude the paper by introducing a model mimicking users selection preferences accounting for the heterogeneity of contents

Debunking in a World of Tribes

Recently a simple military exercise on the Internet was perceived as the beginning of a new civil war in the US. Social media aggregate people around common interests eliciting a collective framing of narratives and worldviews. However, the wide availability of user-provided content and the direct path between producers and consumers of information often foster confusion about causations, encouraging mistrust, rumors, and even conspiracy thinking. In order to contrast such a trend attempts to \textit{debunk} are often undertaken. Here, we examine the effectiveness of debunking through a quantitative analysis of 54 million users over a time span of five years (Jan 2010, Dec 2014). In particular, we compare how users interact with proven (scientific) and unsubstantiated (conspiracy-like) information on Facebook in the US. Our findings confirm the existence of echo chambers where users interact primarily with either conspiracy-like or scientific pages. Both groups interact similarly with the information within their echo chamber. We examine 47,780 debunking posts and find that attempts at debunking are largely ineffective. For one, only a small fraction of usual consumers of unsubstantiated information interact with the posts. Furthermore, we show that those few are often the most committed conspiracy users and rather than internalizing debunking information, they often react to it negatively. Indeed, after interacting with debunking posts, users retain, or even increase, their engagement within the conspiracy echo chamber

Identificación y evaluación de sequías en cuencas seleccionadas de la Región Centro de Argentina

Tesis (DCI)--FCEFN-UNC, 2017La escasez de precipitaciones durante un tiempo considerable puede adoptarse como una definición conceptual del fenómeno de la sequía, el cual constituye un proceso hidrológico extremo y puede ser clasificado según la variable utilizada o el objetivo de estudio. La ocurrencia y efectos de las sequías evolucionan de manera incierta en el tiempo y en el espacio. Esto hace compleja su cuantificación y caracterización por lo que todavía son poco predecibles, de tal manera que es un problema que debe ser abordado desde varios enfoques. La Región Centro de la Argentina constituye un área de gran importancia socioeconómica en el país donde los estudios sobre distintos aspectos de las sequías son escasos, por lo cual se considera necesario e indispensable estudiar y avanzar en el área de conocimiento de esta temática, abordándolo de manera integral para favorecer la toma de decisiones en la gestión y planificación de los recursos hídricos en una comunidad afectada y su entorno. Para esta tesis, dentro de dicha región se seleccionaron cuencas de diferentes características, fundamentalmente en lo que se refiere al tipo de sistema de la unidad hidrográfica y al tamaño. Las mismas se encuentran, en el caso de la cuenca alta del río Suquía y la del río San Antonio dentro de la región serrana de la provincia de Córdoba y en el caso de la cuenca del río Carcarañá dentro, principalmente, de la llanura pampeana que abarca parte de las provincias de Córdoba y Santa Fe. Se utilizaron datos de estaciones de superficie y de tecnología remota. Se aplicaron metodologías estadísticas básicas y complejas, índices representativos de distintos tipos de sequías y técnica de análisis de frecuencia. En este trabajo fue posible generar avances en el conocimiento, tanto de los aspectos técnicos-científicos asociados a las sequías meteorológicas e hidrológicas, como así también sobre la relación de este fenómeno con procesos de carácter macroclimático, en cuencas seleccionadas de la Región Centro de la República Argentina

Creación y utilización de un edublog en francés para 5º y 6º de Primaria

El presente Trabajo de Fin de Grado trata sobre una propuesta de intervención educativa consistente en el diseño y utilización de un blog que ayude a mejorar las competencias comunicativas de la Lengua Francesa en los cursos de 5º y 6º de Educación Primaria. Primeramente, se realizará una aproximación conceptual sobre las características de las TIC en Educación y, más concretamente, en la enseñanza de una Lengua Extranjera. A partir de este análisis, se presentará un proyecto desarrollado en el C.E.I.P. “Los Vadillos” de Burgos, describiendo los recursos y herramientas empleadas, los pasos seguidos y la evaluación de la propuesta. Finalmente, se expondrán los resultados y conclusiones de la experiencia, destacando los logros y limitaciones.The present Final Degree Project deals with an offer of educational intervention consisting of the design and use of a blog that helps to improve the communicative skills of the French Language in the 5th and 6th years of Primary Education. First, there will be a conceptual approach on the characteristics of ICT in Education and, more specifically, in the teaching of a Foreign Language. Based on this analysis, a project developed at C.E.I.P. "Los Vadillos" of Burgos will be presented, describing the resources and tools used, the steps followed and the evaluation of the proposal. Finally, the results and conclusions of the experience will be exposed, highlighting the achievements and limitations

Homophily and Triadic Closure in Evolving Social Networks

We present a new network model accounting for homophily and triadic closure in the evolution of social networks. In particular, in our model, each node is characterized by a number of features and the probability of a link between two nodes depends on common features. The bipartite network of the actors and features evolves according to a dynamics that depends on three parameters that respectively regulate the preferential attachment in the transmission of the features to the nodes, the number of new features per node, and the power-law behavior of the total number of observed features. We provide theoretical results and statistical estimators for the parameters of the model. We validate our approach by means of simulations and an empirical analysis of a network of scientifc collaborations

Public discourse and news consumption on online social media: A quantitative, cross-platform analysis of the Italian Referendum

The rising attention to the spreading of fake news and unsubstantiated rumors on online social media and the pivotal role played by confirmation bias led researchers to investigate different aspects of the phenomenon. Experimental evidence showed that confirmatory information gets accepted even if containing deliberately false claims while dissenting information is mainly ignored or might even increase group polarization. It seems reasonable that, to address misinformation problem properly, we have to understand the main determinants behind content consumption and the emergence of narratives on online social media. In this paper we address such a challenge by focusing on the discussion around the Italian Constitutional Referendum by conducting a quantitative, cross-platform analysis on both Facebook public pages and Twitter accounts. We observe the spontaneous emergence of well-separated communities on both platforms. Such a segregation is completely spontaneous, since no categorization of contents was performed a priori. By exploring the dynamics behind the discussion, we find that users tend to restrict their attention to a specific set of Facebook pages/Twitter accounts. Finally, taking advantage of automatic topic extraction and sentiment analysis techniques, we are able to identify the most controversial topics inside and across both platforms. We measure the distance between how a certain topic is presented in the posts/tweets and the related emotional response of users. Our results provide interesting insights for the understanding of the evolution of the core narratives behind different echo chambers and for the early detection of massive viral phenomena around false claims

Learning Opinion Dynamics From Social Traces

Opinion dynamics - the research field dealing with how people's opinions form and evolve in a social context - traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives the opinion formation process, and have the advantage of being easy to interpret. However, as they do not exploit the availability of data, their predictive power is limited. Moreover, parameter calibration and model selection are manual and difficult tasks. In this work we propose an inference mechanism for fitting a generative, agent-like model of opinion dynamics to real-world social traces. Given a set of observables (e.g., actions and interactions between agents), our model can recover the most-likely latent opinion trajectories that are compatible with the assumptions about the process dynamics. This type of model retains the benefits of agent-based ones (i.e., causal interpretation), while adding the ability to perform model selection and hypothesis testing on real data. We showcase our proposal by translating a classical agent-based model of opinion dynamics into its generative counterpart. We then design an inference algorithm based on online expectation maximization to learn the latent parameters of the model. Such algorithm can recover the latent opinion trajectories from traces generated by the classical agent-based model. In addition, it can identify the most likely set of macro parameters used to generate a data trace, thus allowing testing of sociological hypotheses. Finally, we apply our model to real-world data from Reddit to explore the long-standing question about the impact of backfire effect. Our results suggest a low prominence of the effect in Reddit's political conversation.Comment: Published at KDD202