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As cities adopt sustainable mobility solutions, electric scooters (e-scooters) offer both challenges and opportunities for public transportation systems. This study, the first in Latin America, examines the effects of e-scooters on public transport demand in Santiago, Chile, focusing on two scenarios: "generation" of trips (trips starting in study zones) and "attraction" of trips (trips ending in study zones). A negative binomial regression model was applied to data from public transport smart cards and e-scooter GPS. The methodology included urban area clustering and a differences-in-differences approach. The findings reveal significant regional differences: in the Central Region, public transport trips decreased by 21.38% in the generation scenario, while bus trips increased by 76.39%. In the Intermediate Region, metro trips increased by 70.05%, and in the Peripheral Region, bus trips increased by 84.64%. These results suggest that e-scooters reduce public transport usage in highly accessible areas but increase it in less accessible regions., Comment: 62 pages, 12 figures. Submitted to Transport Research Part D

Work-related transportation incidents significantly impact urban mobility and productivity. These incidents include traffic crashes, collisions between vehicles, and falls that occurred during commuting or work-related transportation (e.g., falling while getting off a bus during the morning commute or while riding a bicycle for work). This study analyzes a decade of work-related transportation incident data (2012--2021) in Santiago, Chile, using records from a major worker's insurance company. Using negative binomial regression, we assess the impact of a 2018 urban speed limit reduction law on incident injury severity. We also explore broader temporal, spatial, and demographic patterns in these incidents in urban and rural areas. The urban speed limit reduction is associated with a decrease of 4.26 days in prescribed medical leave for incidents in urban areas, suggesting that lower speed limits contribute to reduced injury severity. Our broader analysis reveals distinct incident patterns across different groups. Workers traveling by motorcycle and bicycle experience more severe injuries when involved in traffic incidents, with marginal effects of 26.94 and 13.06 additional days of medical leave, respectively, compared to motorized vehicles. Women workers tend to have less severe injuries, with an average of 7.57 fewer days of medical leave. Age is also a significant factor, with older workers experiencing more severe injuries -- each additional year of age is associated with 0.57 more days of medical leave. Our results provide insights for urban planning, transportation policy, and workplace safety initiatives., Comment: 18 pages, 9 figures. Second version after reviews

The COVID-19 outbreak implied many changes in the daily life of most of the world's population for a long time, prompting severe restrictions on sociality. The Behavioral Immune System (BIS) suggests that when facing pathogens, a psychological mechanism would be activated that, among other things, would generate an increase in prejudice and discrimination towards marginalized groups, including immigrants. This study aimed to test if people tend to enhance their rejection of minorities and foreign groups under the threat of contagious diseases, using the users' attitudes towards migrants in Twitter data from Chile, for pre-pandemic and pandemic contexts. Our results only partially support the BIS hypothesis, since threatened users increased their tweet production in the pandemic period, compared to empathetic users, but the latter grew in number and also increased the reach of their tweets between the two periods. We also found differences in the use of language between these types of users. Alternative explanations for these results may be context-dependent., Comment: 18 pages, 6 figures, under review

Up-to-date information on different modes of travel to monitor transport traffic and evaluate rapid urban transport planning interventions is often lacking. Transport systems typically rely on traditional data sources providing outdated mode-of-travel data due to their data latency, infrequent data collection and high cost. To address this issue, we propose a method that leverages mobile phone data as a cost-effective and rich source of geospatial information to capture current human mobility patterns at unprecedented spatiotemporal resolution. Our approach employs mobile phone application usage traces to infer modes of transportation that are challenging to identify (bikes and ride-hailing/taxi services) based on mobile phone location data. Using data fusion and matrix factorization techniques, we integrate official data sources (household surveys and census data) with mobile phone application usage data. This integration enables us to reconstruct the official data and create an updated dataset that incorporates insights from digital footprint data from application usage. We illustrate our method using a case study focused on Santiago, Chile successfully inferring four modes of transportation: mass-transit, motorised, active, and taxi. Our analysis revealed significant changes in transportation patterns between 2012 and 2020. We quantify a reduction in mass-transit usage across municipalities in Santiago, except where metro/rail lines have been more recently introduced, highlighting added resilience to the public transport network of these infrastructure enhancements. Additionally, we evidence an overall increase in motorised transport throughout Santiago, revealing persistent challenges in promoting urban sustainable transportation. We validate our findings comparing our updated estimates with official smart card transaction data., Comment: 31 pages, 14 figures. New version after first round of peer-review

The Web contains several social media platforms for discussion, exchange of ideas, and content publishing. These platforms are used by people, but also by distributed agents known as bots. Although bots have existed for decades, with many of them being benevolent, their influence in propagating and generating deceptive information in the last years has increased. Here we present a characterization of the discussion on Twitter about the 2020 Chilean constitutional referendum. The characterization uses a profile-oriented analysis that enables the isolation of anomalous content using machine learning. As result, we obtain a characterization that matches national vote turnout, and we measure how anomalous accounts (some of which are automated bots) produce content and interact promoting (false) information., Comment: 5 pages, 9 figures

Web platforms have allowed political manifestation and debate for decades. Technology changes have brought new opportunities for expression, and the availability of longitudinal data of these debates entice new questions regarding who participates, and who updates their opinion. The aim of this work is to provide a methodology to measure these phenomena, and to test this methodology on a specific topic, abortion, as observed on one of the most popular micro-blogging platforms. To do so, we followed the discussion on Twitter about abortion in two Spanish-speaking countries from 2015 to 2018. Our main insights are two fold. On the one hand, people adopted new technologies to express their stances, particularly colored variations of heart emojis ([green heart] & [purple heart]) in a way that mirrored physical manifestations on abortion. On the other hand, even on issues with strong opinions, opinions can change, and these changes show differences in demographic groups. These findings imply that debate on the Web embraces new ways of stance adherence, and that changes of opinion can be measured and characterized., Comment: Accepted at WebSci'20

Urban population grows constantly. By 2050 two thirds of the world population will reside in urban areas. This growth is faster and more complex than the ability of cities to measure and plan for their sustainability. To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions: areas with over- and under-representation of specific population groups, named hot and cold spots respectively. Using aggregated mobile phone data, we apply this methodology to the city of Barcelona to assess the mobility of three groups of people: women, elders, and tourists. We find that, within the three groups, cold spots have a lower diversity of amenities and services than hot spots. Also, cold spots of women and tourists tend to have lower population income. These insights apply to the floating population of Barcelona, thus augmenting the scope of how inclusiveness can be analyzed in the city., Comment: 10 pages, 10 figures. To be presented at the Data Science for Social Good workshop at The Web Conference 2020

Migration is a worldwide phenomenon that may generate different reactions in the population. Attitudes vary from those that support multiculturalism and communion between locals and foreigners, to contempt and hatred toward immigrants. Since anti-immigration attitudes are often materialized in acts of violence and discrimination, it is important to identify factors that characterize these attitudes. However, doing so is expensive and impractical, as traditional methods require enormous efforts to collect data. In this paper, we propose to leverage Twitter to characterize local attitudes toward immigration, with a case study on Chile, where immigrant population has drastically increased in recent years. Using semi-supervised topic modeling, we situated 49K users into a spectrum ranging from in-favor to against immigration. We characterized both sides of the spectrum in two aspects: the emotions and lexical categories relevant for each attitude, and the discussion network structure. We found that the discussion is mostly driven by Haitian immigration; that there are temporal trends in tendency and polarity of discussion; and that assortative behavior on the network differs with respect to attitude. These insights may inform policy makers on how people feel with respect to migration, with potential implications on communication of policy and the design of interventions to improve inter-group relations., Comment: 8 pages, accepted at Latin American Web Congress 2019 (co-located with The Web Conference)

In centralized countries, not only population, media and economic power are concentrated, but people give more attention to central locations. While this is not inherently bad, this behavior extends to micro-blogging platforms: central locations get more attention in terms of information flow. In this paper we study the effects of an information filtering algorithm that decentralizes content in such platforms. Particularly, we find that users from non-central locations were not able to identify the geographical diversity on timelines generated by the algorithm, which were diverse by construction. To make users see the inherent diversity, we define a design rationale to approach this problem, focused on an already known visualization technique: treemaps. Using interaction data from an "in the wild" deployment of our proposed system, we find that, even though there are effects of centralization in exploratory user behavior, the treemap was able to make users see the inherent geographical diversity of timelines, and engage with user generated content. With these results in mind, we propose practical actions for micro-blogging platforms to account for the differences and biased behavior induced by centralization., Comment: 12 pages. Under review. Please contact authors before citing / distributing