Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks

Making big data work: smart, sustainable, and safe cities

The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ‘Telecom Italia Big Data Challenge 2014’ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness

Identifying Purpose Behind Electoral Tweets

Tweets pertaining to a single event, such as a national election, can number in the hundreds of millions. Automatically analyzing them is beneficial in many downstream natural language applications such as question answering and summarization. In this paper, we propose a new task: identifying the purpose behind electoral tweets--why do people post election-oriented tweets? We show that identifying purpose is correlated with the related phenomenon of sentiment and emotion detection, but yet significantly different. Detecting purpose has a number of applications including detecting the mood of the electorate, estimating the popularity of policies, identifying key issues of contention, and predicting the course of events. We create a large dataset of electoral tweets and annotate a few thousand tweets for purpose. We develop a system that automatically classifies electoral tweets as per their purpose, obtaining an accuracy of 43.56% on an 11-class task and an accuracy of 73.91% on a 3-class task (both accuracies well above the most-frequent-class baseline). Finally, we show that resources developed for emotion detection are also helpful for detecting purpose

The Digital Life of Walkable Streets

Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.Comment: 10 pages, 7 figures, Proceedings of International World Wide Web Conference (WWW 2015

A Framework Based on Semantic Spaces and Glyphs for Social Sensing on Twitter

Abstract In this paper we present a framework aimed at detecting emotions and sentiments in a Twitter stream. The approach uses the well-founded Latent Semantic Analysis technique, which can be seen as a bio-insipred cognitive architecture, to induce a semantic space where tweets are mapped and analysed by soft sensors. The measurements of the soft sensors are then used by a visualisation module which exploits glyphs to graphically present them. The result is an interactive map which makes easy the exploration of reactions and opinions in the whole globe regarding tweets retrieved from specific queries

Are you Charlie or Ahmed? Cultural pluralism in Charlie Hebdo response on Twitter

We study the response to the Charlie Hebdo shootings of January 7, 2015 on Twitter across the globe. We ask whether the stances on the issue of freedom of speech can be modeled using established sociological theories, including Huntington's culturalist Clash of Civilizations, and those taking into consideration social context, including Density and Interdependence theories. We find support for Huntington's culturalist explanation, in that the established traditions and norms of one's "civilization" predetermine some of one's opinion. However, at an individual level, we also find social context to play a significant role, with non-Arabs living in Arab countries using #JeSuisAhmed ("I am Ahmed") five times more often when they are embedded in a mixed Arab/non-Arab (mention) network. Among Arabs living in the West, we find a great variety of responses, not altogether associated with the size of their expatriate community, suggesting other variables to be at play.Comment: International AAAI Conference on Web and Social Media (ICWSM), 201

Emotions, Demographics and Sociability in Twitter Interactions

The social connections people form online affect the quality of information they receive and their online experience. Although a host of socioeconomic and cognitive factors were implicated in the formation of offline social ties, few of them have been empirically validated, particularly in an online setting. In this study, we analyze a large corpus of geo-referenced messages, or tweets, posted by social media users from a major US metropolitan area. We linked these tweets to US Census data through their locations. This allowed us to measure emotions expressed in the tweets posted from an area, the structure of social connections, and also use that area's socioeconomic characteristics in analysis. %We extracted the structure of online social interactions from the people mentioned in tweets from that area. We find that at an aggregate level, places where social media users engage more deeply with less diverse social contacts are those where they express more negative emotions, like sadness and anger. Demographics also has an impact: these places have residents with lower household income and education levels. Conversely, places where people engage less frequently but with diverse contacts have happier, more positive messages posted from them and also have better educated, younger, more affluent residents. Results suggest that cognitive factors and offline characteristics affect the quality of online interactions. Our work highlights the value of linking social media data to traditional data sources, such as US Census, to drive novel analysis of online behavior.Comment: International Conference on the Web and Social Media (ICWSM2016

Daily Stress Recognition from Mobile Phone Data, Weather Conditions and Individual Traits

Research has proven that stress reduces quality of life and causes many diseases. For this reason, several researchers devised stress detection systems based on physiological parameters. However, these systems require that obtrusive sensors are continuously carried by the user. In our paper, we propose an alternative approach providing evidence that daily stress can be reliably recognized based on behavioral metrics, derived from the user's mobile phone activity and from additional indicators, such as the weather conditions (data pertaining to transitory properties of the environment) and the personality traits (data concerning permanent dispositions of individuals). Our multifactorial statistical model, which is person-independent, obtains the accuracy score of 72.28% for a 2-class daily stress recognition problem. The model is efficient to implement for most of multimedia applications due to highly reduced low-dimensional feature space (32d). Moreover, we identify and discuss the indicators which have strong predictive power.Comment: ACM Multimedia 2014, November 3-7, 2014, Orlando, Florida, US

OSN Mood Tracking: Exploring the Use of Online Social Network Activity as an Indicator of Mood Changes

Online social networks (OSNs) have become an integral part of our everyday lives, where we share our thoughts and feelings. This study analyses the extent to which the changes of an individual’s real-world psychological mood can be inferred by tracking their online activity on Facebook and Twitter. By capturing activities from the OSNs and ground truth data via experience sampling, it was found that mood changes can be detected within a window of 7 days for 61% of the participants by using specific, combined online activity signals. The participants fall into three distinct groups: those whose mood correlates positively with their online activity, those who correlate negatively and those who display a weak correlation. We trained two classifiers to identify these groups using features from their online activity, which achieved precision of 95.2% and 84.4% respectively. Our results suggest that real-world mood changes can be passively tracked through online activity on OSNs