Eduardo Graells-Garrido

First Women, Second Sex: Gender Bias in Wikipedia

Contributing to the writing of history has never been as easy as it is today. Anyone with access to the Web is able to play a part on Wikipedia, an open and free encyclopedia, and arguably one of the primary sources of knowledge on the Web. In this paper, we study gender bias in Wikipedia in terms of how women and men are characterized in their biographies. To do so, we analyze biographical content in three aspects: meta-data, language, and network structure. Our results show that, indeed, there are differences in characterization and structure. Some of these differences are reflected from the off-line world documented by Wikipedia, but other differences can be attributed to gender bias in Wikipedia content. We contextualize these differences in social theory and discuss their implications for Wikipedia policy.

Sensing Urban Patterns with Antenna Mappings: The Case of Santiago, Chile

Mobile data has allowed us to sense urban dynamics at scales and granularities not known before, helping urban planners to cope with urban growth. A frequently used kind of dataset are Call Detail Records (CDR), used by telecommunication operators for billing purposes. Being an already extracted and processed dataset, it is inexpensive and reliable. A common assumption with respect to geography when working with CDR data is that the position of a device is the same as the Base Transceiver Station (BTS) it is connected to. Because the city is divided into a square grid, or by coverage zones approximated by Voronoi tessellations, CDR network events are assigned to corresponding areas according to BTS position. This geolocation may suffer from non negligible error in almost all cases. In this paper we propose “Antenna Virtual Placement” (AVP), a method to geolocate mobile devices according to their connections to BTS, based on decoupling antennas from its corresponding BTS according to its physical configuration (height, downtilt, and azimuth). We use AVP applied to CDR data as input for two different tasks: first, from an individual perspective, what places are meaningful for them? And second, from a global perspective, how to cluster city areas to understand land use using floating population flows? For both tasks we propose methods that complement or improve prior work in the literature. Our proposed methods are simple, yet not trivial, and work with daily CDR data from the biggest telecommunication operator in Chile. We evaluate them in Santiago, the capital of Chile, with data from working days from June 2015. We find that: (1) AVP improves city coverage of CDR data by geolocating devices to more city areas than using standard methods; (2) we find important places (home and work) for a 10% of the sample using just daily information, and recreate the population distribution as well as commuting trips; (3) the daily rhythms of floating population allow to cluster areas of the city, and explain them from a land use perspective by finding signature points of interest from crowdsourced geographical information. These results have implications for the design of applications based on CDR data like recommendation of places and routes, retail store placement, and estimation of transport effects from pollution alerts.

People of opposing views can share common interests

In online social networks, people tend to connect with like-minded people and read agreeable information. Direct recommendation of challenging content has not worked well because users do not value diversity and avoid challenging content. In this poster, we investigate the possibility of an indirect approach by introducing intermediary topics, which are topics that are common to people having opposing views on sensitive issues, i.e., those issues that tend to divide people. Through a case study about a sensitive issue discussed in Twitter, we show that such intermediary topics exist, opening a path for future work in recommendation promoting diversity of content to be shared.

Visual Exploration of Urban Dynamics Using Mobile Data

In this paper we present methods to model citizen movement according to mobile network connectivity, and a set of visualization widgets to display and analyze the results of those methods. In particular, citizen movement is analyzed in terms of Origin/Destiny trips in workable days, as well as classification of city areas into dormitory, non-dormitory, and mixed. We demonstrate our proposal with a case study of the city of Santiago, Chile, and briefly discuss our results in terms of the design of Ambient Intelligence and Urban Design applications.

A Day of Your Days: Estimating Individual Daily Journeys Using Mobile Data to Understand Urban Flow

Travel surveys provide rich information about urban mobility and commuting patterns. But, at the same time, they have drawbacks: they are static pictures of a dynamic phenomena, are expensive to make, and take prolonged periods of time to finish. Nowadays, the availability of mobile usage data (Call Detail Records) makes the study of urban mobility possible at spatiotemporal granularity levels that surveys do not reach. This has been done in the past with good results -- mobile data makes possible to find and understand aggregated mobility patterns. In this paper, we propose to analyze mobile data at individual level by estimating daily journeys, and use those journeys to build Origin-Destiny matrices to understand urban flow. We evaluate this approach with large anonymized CDRs from Santiago, Chile, and find that our method has a high correlation (ρ = 0.89) with the current travel survey, and that it captures external anomalies in daily travel patterns, making our method suitable for inclusion into urban computing applications.

The effect of Pokémon Go on the pulse of the city: a natural experiment

Pokémon Go, a location-based game that uses augmented reality techniques, received unprecedented media coverage due to claims that it allowed for greater access to public spaces, increasing the number of people out on the streets, and generally improving health, social, and security indices. However, the true impact of Pokémon Go on people’s mobility patterns in a city is still largely unknown. In this paper, we perform a natural experiment using data from mobile phone networks to evaluate the effect of Pokémon Go on the pulse of a big city: Santiago, capital of Chile. We found significant effects of the game on the floating population of Santiago compared to movement prior to the game’s release in August 2016: in the following week, up to 13.8% more people spent time outside at certain times of the day, even if they do not seem to go out of their usual way. These effects were found by performing regressions using count models over the states of the cellphone network during each day under study. The models used controlled for land use, daily patterns, and points of interest in the city.Our results indicate that, on business days, there are more people on the street at commuting times, meaning that people did not change their daily routines but slightly adapted them to play the game. Conversely, on Saturday and Sunday night, people indeed went out to play, but favored places close to where they live.Even if the statistical effects of the game do not reflect the massive change in mobility behavior portrayed by the media, at least in terms of expanse, they do show how ‘the street’ may become a new place of leisure. This change should have an impact on long-term infrastructure investment by city officials, and on the drafting of public policies aimed at stimulating pedestrian traffic.

#Santiago is not #Chile, or is it?: a model to normalize social media impact

Online social networks are known to be demographically biased. Currently there are questions about what degree of representativity of the physical population they have, and how population biases impact user-generated content. In this paper we focus on centralism, a problem affecting Chile. Assuming that local differences exist in a country, in terms of vocabulary, we built a methodology based on the vector space model to find distinctive content from different locations, and used it to create classifiers to predict whether the content of a micro-post is related to a particular location, having in mind a geographically diverse selection of micro-posts. We evaluate them in a case study where we analyze the virtual population of Chile that participated in the Twitter social network during an event of national relevance: the municipal (local governments) elections held in 2012. We observe that the participating virtual population is spatially representative of the physical population, implying that there is centralism in Twitter. Our classifiers out-perform a non geographically-diverse baseline at the regional level, and have the same accuracy at a provincial level. However, our approach makes assumptions that need to be tested in multi-thematic and more general datasets. We leave this for future work.

Balancing diversity to counter-measure geographical centralization in microblogging platforms

We study whether geographical centralization is reflected in the virtual population of microblogging platforms. A consequence of centralization is the decreased visibility and findability of content from less central locations.We propose to counteract geographical centralization in microblogging timelines by promoting geographical diversity through: 1) a characterization of imbalance in location interaction centralization over a graph of geographical interactions from user generated content; 2) geolocation of microposts using imbalance-aware content features in text classifiers, and evaluation of those classifiers according to their diversity and accuracy; 3) definition of a two-step information filtering algorithm to ensure diversity in summary timelines of events. We study our proposal through an analysis of a dataset of Twitter in Chile, in the context of the 2012 municipal political elections.

Enhancing web activities with information visualization

Many activities people perform on the Web are biased, including activities like reading news, searching for information and connecting with people. Sometimes these biases are inherent in social behavior (like homophily), and sometimes they are external as they affect the system (like media bias). In this thesis proposal, we describe our approach to use information visualization to enhance Web activities performed by regular people (i.e., non-experts) We understand enhancing as reducing bias effects and generating an engaging response from users. Our methodology is based on case studies. We select a Web activity, identify the biases that affect it, and evaluate how the biases affect a population from online social networks using web mining techniques, and then, we design a visualization following an interactive and playful design approach to diminish the previously identified biases. We propose to evaluate the effect of our visualization designs in user studies by comparing them with state-of-the-art techniques considering a playful experiences framework.

Shopping mall attraction and social mixing at a city scale

In Latin America, shopping malls seem to offer an open, safe and democratic version of the public space. However, it is often difficult to quantitatively measure whether they indeed foster, hinder, or are neutral with respect to social inclusion. In this work, we investigate if, and by how much, people from different social classes are attracted by the same malls. Using a dataset of mobile phone network records from 387,152 devices identified as customers of 16 malls in Santiago de Chile, we performed several analyses to study whether malls with higher social mixing attract more people. Our pipeline, which starts with the socio-economic characterization of mall visitors, includes the estimation of social mixing and diversity of malls, the application of the gravity model of mobility, and the definition of a co-visitation model. Results showed that people tend to choose a profile of malls more in line with their own socio-economic status and the distance from their home to the mall, and that higher mixing does positively contribute to the process of choosing a mall. We conclude that (a) there is social mixing in malls, and (b) that social mixing is a factor at the time of choosing which mall to go to. Thus, the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility to make some malls strong social inclusion hubs.