Abstract
Forests assume a great socioeconomic and environmental importance, requiring good management decisions to value and care for these natural resources. In Portugal, forest land use accounts for 34.5% of the continental area. The softwood species with the highest representation is maritime pine (Pinus pinaster Ait.). Traditionally, the species is managed as pure and even-aged stands for timber production, with a rotation age of 45 to 50 years. Depending on the initial stand density, the stands are thinned 2 to 4 times during the rotation period. Disturbances associated with forest fires have a negative impact on the age structure of stands over time, as they result in a narrow range of stand ages. This age homogenization over large forest areas increases with the recurrence and size of forest fires, bringing new challenges to forest management, namely the difficulty in ensuring the long-term sustainability of the wood supply. The problem aggravates with the increasing demand pressure on pine wood. This article aims to suggest a framework of DSS for Pinus pinaster that can effectively support the management of forest areas under these circumstances, i.e., narrow age ranges and high demand of harvested timber volume. A communal woodland area in the Northern region of Portugal affected by forest fires was selected as a study case. The Modispinaster model was used as the basis of the DSS, to simulate growth scenarios and interventions along the optional rotation period. Two clear-cut ages were considered: 25 and 40 years. The results obtained were the input data for an integer linear programming (ILP) model to obtain the plan that maximizes the volume of timber harvested in the study area, during the planning horizon. The ILP model has constraints bounding the area of clearings, and sustainability, operational and forestry restrictions. The computational results are a powerful tool for guidance in the decision-making of scheduling and forecasting the execution of interventions determining the set of stands that are exploited according to the different scenarios and the period in which the clear-cut is made throughout the planning horizon. Considering all constraints, the solution allows a balanced extraction of a total of 685 m(3)center dot ha(-1), over the 50-year horizon, as well as the representation of all age classes at the end of the planning period.

Abstract
Twitter has become a major platform to share ideas and promoting discussion on relevant topics. However, with a large number of users to resort to it as their primary source of information and with an increasing number of accounts spreading newsworthy content, a characterization of the political bias associated with the social network ecosystem becomes necessary. In this work, we aim at analyzing accounts spreading or publishing content from five different classes of the political spectrum. We also look further and study accounts who spread content from both right and left sides. Conclusions show that there is a large presence of accounts which disseminate right bias content although it is the more central classes that have a higher influence on the network. In addition, users who spread content from both sides are more actively spreading right content with opposite content associated with criticism towards left political parties or promoting right political decisions.

Abstract

Abstract
The negative impact of false information on social networks is rapidly growing. Current research on the topic focused on the detection of fake news in a particular context or event (such as elections) or using data from a short period of time. Therefore, an evaluation of the current proposals in a long-term scenario where the topics discussed may change is lacking. In this work, we deviate from current approaches to the problem and instead focus on a longitudinal evaluation using social network publications spanning an 18-month period. We evaluate different combinations of features and supervised models in a long-term scenario where the training and testing data are ordered chronologically, and thus the robustness and stability of the models can be evaluated through time. We experimented with 3 different scenarios where the models are trained with 15-, 30-, and 60-day data periods. The results show that detection models trained with word-embedding features are the ones that perform better and are less likely to be affected by the change of topics (for example, the rise of COVID-19 conspiracy theories). Furthermore, the additional days of training data also increase the performance of the best feature/model combinations, although not very significantly (around 2%). The results presented in this paper build the foundations towards a more pragmatic approach to the evaluation of fake news detection models in social networks.

Abstract
IntroductionPeople with schizophrenia experience higher levels of stigma compared with other diseases. The analysis of social media content is a tool of great importance to understand the public opinion toward a particular topic.ObjectivesThe aim of this study is to analyse the content of social media on schizophrenia and the most prevalent sentiments towards this disorder.MethodsTweets were retrieved using Twitter’s Application Programming Interface and the keyword “schizophrenia”. Parameters were set to allow the retrieval of recent and popular tweets on the topic and no restrictions were made in terms of geolocation. Analysis of 8 basic emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, and trust) was conducted automatically using a lexicon-based approach and the NRC Word-Emotion Association Lexicon.ResultsTweets on schizophrenia were heterogeneous. The most prevalent sentiments on the topic were mainly negative, namely anger, fear, sadness and disgust. Qualitative analyses of the most retweeted posts added insight into the nature of the public dialogue on schizophrenia.ConclusionsAnalyses of social media content can add value to the research on stigma toward psychiatric disorders. This tool is of growing importance in many fields and further research in mental health can help the development of public health strategies in order to decrease the stigma towards psychiatric disorders.

Abstract