Abstract
In the early twenty-first century, social networks served only to let the world know our tastes, share our photos and share some thoughts. A decade later, these services are filled with an enormous amount of information. Now, the industry and the academia are exploring this information, in order to extract implicit patterns. TwitterJam is a tool that analyses the contents of the social network Twitter to extract events related to road traffic. To reach this goal, we started by analysing tweets to know those which really contains road traffic information. The second step was to gather official information to confirm the extracted information. With these two types of information (official and general), we correlated them in order to verify the credibility of public tweets. The correlation between the two types of information was done separately in two ways: the first one concerns the amount of tweets in a certain time of day and the second on the localization of these tweets. Two hypothesis were also devised concerning these correlations. The results were not perfect but where reasonable enough. We also analysed tools suitable for the visualization of data to decide what is the best strategy to follow. At the end we developed a web application that shows the results, to help the analysis of results.

Abstract
Trip duration is an important metric for the management of taxi companies, as it affects operational efficiency, driver satisfaction and, above all, customer satisfaction. In particular, the ability to predict trip duration in advance can be very useful for allocating taxis to stands and finding the best route for trips. A data mining approach can be used to generate models for trip time prediction. In fact, given the amount of data available, different models can be generated for different taxis. Given the difference between the data collected by different taxis, the best model for each one can be obtained with different algorithms and/or parameter settings. However, finding the configuration that generates the best model for each taxi is computationally very expensive. In this paper, we propose the use of metalearning to address the problem of selecting the algorithm that generates the model with the most accurate predictions for each taxi. The approach is tested on data collected in the Drive-In project. Our results show that metalearning can help to select the algorithm with the best accuracy.

Abstract
A new weighted rank correlation coefficient r(W2) has been introduced in Pinto da Costa (2011), following the coefficient r(W) introduced in Pinto Da Costa and Soares (2005); Soares et al. (2001); Pinto Da Costa et al. (2001). We give the expression of r(W2) in the case of ties and also present some simulations to study the behaviour of the coefficient.

Abstract
The automatic content analysis of mass media in the social sciences has become necessary and possible with the raise of social media and computational power. One particularly promising avenue of research concerns the use of opinion mining. We design and implement the POPmine system which is able to collect texts from web-based conventional media (news items in mainstream media sites) and social media (blogs and Twitter) and to process those texts, recognizing topics and political actors, analyzing relevant linguistic units, and generating indicators of both frequency of mention and polarity (positivity/negativity) of mentions to political actors across sources, types of sources, and across time.

Abstract

Abstract
The road transportation sector is responsible for 87% of the human CO2 emissions. The estimation and prediction of fuel consumption plays a key role in the development of systems that foster the reduction of those emissions through trip planing. In this paper, we present a predictive regression model of instantaneous fuel consumption for diesel and gasoline light-duty vehicles, based on their instantaneous speed and acceleration and on road inclination. The parameters are extracted from GPS data, thus the models do not require data from dedicated vehicle sensors. We use data collected by 17 drivers during their daily commutes using the SenseMyCity crowdsensor. We perform an empyrical comparison of several regression algorithms for prediction across trips of the same vehicle and for prediction across vehicles. The results show that models trained for a vehicle show similar RMSE when are applied to other vehicles with similar characteristics. Relying on these results, we propose fuel type specific models that provide an accurate prediction for vehicles with similar characteristics to those on which the models were trained.