Information and Communication Technologies

In recent years, behavior analysis by smartphone’s GPS data is being conducted. It is becoming clear that the routine behavior of city dwellers follows a certain pattern, based on preference and life rhythm, and it is possible to predict the traffic congestion, etc., by reading this behavior pattern [1]. Prediction, based on human behavior, is also possible for non-routine behavior such as sightseeing, traveling, etc. Although they are not routine activities, there are several action patterns, based on the purpose.

Many studies of digital communication use natural language processing (NLP) to find topics, assess sentiment, and describe user behaviour. In finding topics often the relationships between users who participate in the topic are neglected. We propose a method of describing and classifying online conversations using only the structure of the underlying temporal network and not the text content of individual messages (using the recently proposed temporal event graph). The temporal event graph utilises all available information in the temporal network, i.e.

Understanding the interplay between internal and external traffic across networks is an important issue in the analysis of dynamic processes on graphs, with applications across a range of disciplines, including the sociological contexts of models of online popularity, attention prediction for news events, and collective memory. Previous related research has used time series data to learn graphs [1], or typically applies multivariate forecasting techniques without explicit consideration of underlying network structure [2].

Online social media are information resources that can have a transformative power in society. While the Web was envisioned as an equalizing force that allows everyone to access information, the digital divide prevents large amounts of people from being present online. Online social media in particular are prone to gender inequality, an important issue given the link between social media use and employment. Understanding gender inequality in social media is a challenging task due to the necessity of data sources that can provide large-scale measurements across multiple countries.

The popularity of ride-sharing services, such as Uber and Lyft, is quickly transforming urban transportation systems. With the digitalization of such services and taxi dispatching systems, companies are now able to optimize their matching algorithms to maximize profit while reaching the best possible user satisfaction in terms of service availability or waiting time. Unfortunately, little attention is being paid to possible negative consequences incorporated into these matching systems from the drivers’ perspective.

Temporal inhomogeneities in event sequences of natural and social phenomena have been characterized in terms of interevent times and correlations between interevent times [1]. The inhomogeneities of interevent times have been extensively studied, while the correlations between interevent times, often called correlated bursts (CB), are far from being fully understood.

In [1] the Asymptotic Significance was proposed as a new optimization function for community detection: the main idea is to reduce the resolution limit intrinsically present in the modularity [2] by considering the distance between a null-model discounting the information of the number of internal (to the communities) links and one that does not. Nevertheless the chosen target function does not discount the information contained in the degree sequence.

The surprising outcomes of recent political events, such as the Brexit referendum and the latest US presidential election, are revealing how limited is our current understanding of social behavior in a highly interconnected world. A key challenge is to clarify how a massive information flow affects decision-making processes. As a contribution in addressing this issue, in this work we consider a social network where agents need to choose sequentially between two options, which could correspond to restaurants, brands or political parties.