In this paper, we present a study about the most popular information sources available on the Web (e.g., Google, Facebook, Twitter) and the available methods to verify their publications. We propose a generic credibility analysis model for social information sources, which is instantiated for Twitter. We show a proof of concepts through the development of World White Web, a Google Chrome extension application that implements the model to analyze tweets in real time, using web scraping. Our study demonstrates the feasibility and suitability of creating efficient methods to analyze the credibility of publication on social networks and points out the challenges and open problems that should be overcome in future solutions in this area.

Emotion recognition is a strategy for social robots used to implement better Human-Robot Interaction and model their social behaviour. Since human emotions can be expressed in different ways (e.g., face, gesture, voice), multimodal approaches are useful to support the recognition process. However, although there exist studies dealing with multimodal emotion recognition for social robots, they still present limitations in the fusion process, dropping their performance if one or more modalities are not present or if modalities have different qualities. This is a common situation in social robotics, due to the high variety of the sensory capacities of robots; hence, more flexible multimodal models are needed. In this context, we propose an adaptive and flexible emotion recognition architecture able to work with multiple sources and modalities of information and manage different levels of data quality and missing data, to lead robots to better understand the mood of people in a given environment and accordingly adapt their behaviour. Each modality is analyzed independently to then aggregate the partial results with a previous proposed fusion method, called EmbraceNet+, which is adapted and integrated to our proposed framework. We also present an extensive review of state-of-the-art studies dealing with fusion methods for multimodal emotion recognition approaches. We evaluate the performance of our proposed architecture by performing different tests in which several modalities are combined to classify emotions using four categories (i.e., happiness, neutral, sadness, and anger). Results reveal that our approach is able to adapt to the quality and presence of modalities. Furthermore, results obtained are validated and compared with other similar proposals, obtaining competitive performance with state-of-the-art models.

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The household travel survey (HTS) finds itself in the midst of rapid technological change. Traditional methods are increasingly being sidelined by digital devices and computational powerfor tracking movements, automatically detecting modes and activities, facilitating data collection, etc. Smartphones have recently emerged as the latest technological enhancement. FMS is a smartphone-based prompted-recall HTS platform, consisting of an app for sensor data collection, a backend for data processing and inference, and a user interface for verification of inferences (e.g., modes, activities, times, etc.). FMS, has been deployed in several cities of the global north, including Singapore. This paper assesses the first use of FMS in a city of the global south, Dar es Salaam. FMS in Dar was implemented over a one-month period, among 581 adults chosen from 300 randomly selected households. Individuals were provided phones with data plans and the FMS app preloaded. Verification of the collected data occurred every three days, via a phone interview. The experiment reveals various social and technical challenges. Models of individual likelihood to participate suggest little bias. Several socioeconomic and demographic characteristics apparently do influence, however, the number of days fully verified per individual. Similar apparent biases emerge when predicting the likelihood of a given day being verified. Some risk of non-random, non-response is, thus, evident.

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