Live monitoring 4chan discussion threads

The 4chan portal has been known for several years as a ``fringe'' internet service for sharing and commenting pictures. Thanks to the possibility to post anonymously, guaranteed by the total lack of a registration/identification mechanism, the portal has somewhat evolved to a global, if mostly US-centred, locus for the posting of extreme views, including racism and all sorts of hate speech. A pivotal role in the emergence of the website as a bastion of ``free speech" has been played by the /pol/ board (https://boards.4chan.org/pol/), which declares its commitment to host ``politically incorrect'' discussions. Several research groups have intensively studied 4chan structure, dynamics and contents. Thanks to works such as[4, 12], we now have a fairly clear description of how 4chan works and what type of discussion dynamics the site supports. In particular, the latter work shed light on the extremely ephemeral nature of discussions, with threads lasting on the website for a few hours at most, and often just for minutes - depending on the traffic they generate - before being removed to make room for new discussion. Given the fast-paced nature of the evolution of the content of the boards, and especially given how such ephemerality shapes the tone and the content of the discussion itself [4, 14], it is of extreme importance for researchers to be able to capture the content of the threads at various points over the course of their short lives. To the best of our knowledge, the existing 4chan literature has relied either on autoptic exploration by the scholars [14], or on large scale data collection campaigns that drew their content from the archived versions of the threads [12], i.e. on copies of the threads as they appeared at the time of their closure, and at that time only. In order to observe at a more fine-grained level the content on the website, we devised a ``scraping'' architecture, summarised in Figure 2, which based on the OXPath platform [9]. It enables the retrieval of the threads posted on a board at various points while they are still live

Network-based indicators of Bitcoin bubbles

The functioning of the cryptocurrency Bitcoin relies on the open availability of the entire history of its transactions. This makes it a particularly interesting socio-economic system to analyse from the point of view of network science. Here we analyse the evolution of the network of Bitcoin transactions between users. We achieve this by using the complete transaction history from December 5th 2011 to December 23rd 2013. This period includes three bubbles experienced by the Bitcoin price. In particular, we focus on the global and local structural properties of the user network and their variation in relation to the different period of price surge and decline. By analysing the temporal variation of the heterogeneity of the connectivity patterns we gain insights on the different mechanisms that take place during bubbles, and find that hubs (i.e., the most connected nodes) had a fundamental role in triggering the burst of the second bubble. Finally, we examine the local topological structures of interactions between users, we discover that the relative frequency of triadic interactions experiences a strong change before, during and after a bubble, and suggest that the importance of the hubs grows during the bubble. These results provide further evidence that the behaviour of the hubs during bubbles significantly increases the systemic risk of the Bitcoin network, and discuss the implications on public policy interventions

Geo-referencing as a connector between user reviews and urban environment quality

The primary objective of this research is to understand the relationship between the positivity of user-generated content, namely Airbnb reviews and the “attractiveness” of the neighbourhood of the listing. Focussing on London wards and their Airbnb listings, we could identify some features which consistently signal positive sentiment and used best-subset selection to identify an overall relationship. We identified the types of features most commonly associated with positive sentiment before discussing how this information be used to influence urban development. Whilst analysis of the Airbnb market has been a popular area of study lately, to the best of our knowledge this specific research question has not been considered

Epidemic spreading on activity-driven networks with attractiveness

We study SIS epidemic spreading processes unfolding on a recent generalisation of the activity-driven modelling framework. In this model of time-varying networks each node is described by two variables: activity and attractiveness. The first, describes the propensity to form connections. The second, defines the propensity to attract them. We derive analytically the epidemic threshold considering the timescale driving the evolution of contacts and the contagion as comparable. The solutions are general and hold for any joint distribution of activity and attractiveness. The theoretical picture is confirmed via large-scale numerical simulations performed considering heterogeneous distributions and different correlations between the two variables. We find that heterogeneous distributions of attractiveness alter the contagion process. In particular, in case of uncorrelated and positive correlations between the two variables, heterogeneous attractiveness facilitates the spreading. On the contrary, negative correlations between activity and attractiveness hamper the spreading. The results presented contribute to the understanding of the dynamical properties of time-varying networks and their effects on contagion phenomena unfolding on their fabric

EPIDEMIC PROCESSES ON ACTIVITY-DRIVEN NETWORKS WITH ATTRACTIVENESS

Complexity science is today a well-established field of study, in which physicists have long been playing a significant role. Many complex systems can be represented as networks: the Internet, social networks (both in the real and in the virtual world), airplane routes, scientific papers citations, neurons, road maps, and many others. Complexity in networks notably manifests itself in the high diversity of nodes' connectivity patterns, as for what concerns both the heterogeneity and the intensity of connections - a feature common to most real networks. Physicists have been contributing a great deal to the analysis of large-scale networks, where many of the tools developed in statistical physics have found an application. The study of complex networks is often coupled with the study of dynamical processes taking place over them, such as epidemic processes, random walks, and others. In this context, the time-evolution of the onsidered network itself may play a significant role and affect the outcome of the process studied. For this reason, recent years have seen an increasing interest in the subject of time-varying networks, also called temporal networks or dynamical networks in the literature. The activity driven model represents one attempt at providing an analytical framework simple enough to find application in a variety of contexts, particularly in social systems, and capable to connect the topological structure of a network to its temporal dynamics. The activity of a node in a time-varying network is a measure of its propensity to form connections, and networks of human agents tend to share the same activity distribution: a power-law. An analytical model aimed at representing different kinds of social networks (and possibly other species of networks) could be constructed starting indeed from the activity distribution, and this principle, as the name suggests, is precisely what underlies the activity-driven model. In our thesis we propose a new version of the model where, beyond the activity distribution, we let the network be characterized by an attractiveness distribution. The attractiveness of a node is a measure of its propensity to receive interactions from others, therefore it is to some extent the reciprocal of the activity, and a natural complement to it within the model; a non-constant attractiveness is observed in many real social networks, and the analysis of this feature will provide us with some insight on how dynamical process unfold on such kind of systems. The introduction of this new property leads to an appreciable modification of the contact dynamics, which we study by considering an SIS/SIR epidemic process taking place on the network, calculating the general expression of the epidemic threshold holding for any joint distribution of activity and attractiveness in the thermodynamic limit; we study in particular the case of power-law uncorrelated distributions, and the case of identical activity-attractiveness correlation. Analytical calculations, corroborated by simulations on synthetic networks, show how a high heterogeneity in the distributions (i.e. a large variance) makes it easier for the disease to propagate throughout the network, with respect to the case of the same epidemic process considered while taking place on the original activity-driven model. These findings fit nicely in the picture we already have of epidemic processes on networks, where the fact that heterogeneity facilitates the spreading is a well known and general feature common to both static and temporal representations. In general, our results confirm the importance of the role played by time structure in the formation of connectivity patterns. To equip the reader with the necessary tools to appreciate the results presented, the first part of the thesis is devoted to an introduction on the subject of dynamical processes on complex networks, both static and time-varying. The most important measures, properties, models, and their impact on different kind of processes are described

Activity networks determine project performance

Abstract Projects are characterised by activity networks with a critical path, a sequence of activities from start to end, that must be finished on time to complete the project on time. Watching over the critical path is the project manager’s strategy to ensure timely project completion. This intense focus on a single path contrasts the broader complex structure of the activity network, and is due to our poor understanding on how that structure influences this critical path. Here, we use a generative model and detailed data from 77 real world projects (+ $10 bn total budget) to demonstrate how this network structure forces us to look beyond the critical path. We introduce a duplication-split model of project schedules that yields (i) identical power-law in- and-out degree distributions and (ii) a vanishing fraction of critical path activities with schedule size. These predictions are corroborated in real projects. We demonstrate that the incidence of delayed activities in real projects is consistent with the expectation from percolation theory in complex networks. We conclude that delay propagation in project schedules is a network property and it is not confined to the critical path