Modelado y difusión de temas noticiosos en medios sociales: características y factores de la emergencia de noticias en un canal informativo de Twitter

This study aims to characterize the modeling and diffusion of news topics in social media and determine the factors that influenced them. With Big Data analysis methods, such as topic modeling and sentiment analysis, we analyzed one year of tweets from Colombian newspaper El Tiempo. We found that the appearance of long-term topics was related to the message's attributes. Theoretical implications and contributions considering Diffusion of Innovations' model are mentioned. © 2018 Universidad de Guadalajara. All rights reserved

Classification and prediction of whereabouts patterns from the Reality Mining dataset

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Queries to Google Search as Predictors of Migration Flows from Latin America to Spain

This study evaluates the relationship between the changes in proportion of migration-related queries reported by Google Trends and changes in volume of migration flows between origin and destination countries. The study assesses if cost-free Google Trends improves the prediction of international migratory flows, and whether it could be proposed as a tool for organizations and policymakers. Previous research has used the activity of email users and other online services to track human mobility. At the same time, IP geolocation linked to Google Search has proven to be efficient in geographically tracking outbreaks of illnesses, as well as predicting changes in economic indicators and travel patterns. This research draws from both experiences. It uses a regression analysis of time series data to compare the popularity of migration related queries introduced to Google Search in Colombia, Argentina and Peru, to changes in a quantity of residents’ registrations in Spain, performed by immigrants proceeding from these countries between the years 2005 and 2010. The results show a significant correlation and weak to moderate predictability for the lags of several months depending on the particular country. The findings demonstrate that trends in queries to Google Search provided by Google Trends might constitute a useful predictor of migration flows. At the same time, it indicates the need for further technological developments to improve analytical capacities

Mining and correlating traffic events from human sensor observations with official transport data using self-organizing-maps

Cities are complex systems, where related Human activities are increasingly difficult to explore within. In order to understand urban processes and to gain deeper knowledge about cities, the potential of location-based social networks like Twitter could be used a promising example to explore latent relationships of underlying mobility patterns. In this paper, we therefore present an approach using a geographic self-organizing map (Geo-SOM) to uncover and compare previously unseen patterns from social media and authoritative data. The results, which we validated with Live Traffic Disruption (TIMS) feeds from Transport for London, show that the observed geospatial and temporal patterns between special events (r = 0.73), traffic incidents (r = 0.59) and hazard disruptions (r = 0.41) from TIMS, are strongly correlated with traffic-related, georeferenced tweets. Hence, we conclude that tweets can be used as a proxy indicator to detect collective mobility events and may help to provide stakeholders and decision makers with complementary information on complex mobility processes

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion