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Engage Students in News Writing
... Um dos principais locais que deve ser responsável pela educação destes grupos neste contexto são as escolas onde, e apesar de algumas tentativas notáveis na criação de elementos como jornais escolares que procuram dar aos alunos um suporte para se expressarem, não se tem obtido resultados para garantir que os alunos têm as ferramentas e competências necessárias para desenvolverem pensamento crítico. A falta de soluções existentes nesta vertente de combate à desinformação apresenta-se assim como uma oportunidade para o desenvolvimento do projeto TRUE [1]. Este apresenta-se como uma iniciativa entre várias instituições, na criação de uma solução de escrita de jornais escolares que agrega uma ferramenta de apoio à redação de artigos, destinada a alunos de 2º, 3º ciclos e secundário (FIGURA 1). ...
With the evolution of media and the internet in general in the last few years, we see an increase in accessibility and ease of use of many platforms, services, social media, and other communication/media outlets. Even though a lot of what is being created and improved has many benefits to society, it also comes with negative aspects for many users with a big emphasis on the young public. With the rise of media creation through many different and new formats, there is a significant increase of audio-visual stimuli available for consumption. In teens, the effects of such stimuli result in negative behaviours like the decrease of self-learning capabilities, the decrease of interest in extracurricular activities, the decrease of critical thinking, among many other factors.
In this paper we will talk about a project that presents itself as a solution to combat disinformation and to encourage an educated and responsible cultural participation. This project is based on the development of an online writing tool that seeks to encourage critical thinking, selection of information sources and assessment of their credibility. It looks to impact the writing of school newspapers by creating a platform that allows students to easily develop stories by using the many features available. Connected to the project and considering the target audience being composed of a younger audience (12 to 18 years of age), during this paper there will be a big focus on the topic of gamification and how it can be a clever approach to keep users engaged and motivated.
This paper describes the creation of a Portuguese
corpus following the guidelines of
the Universal Dependencies Framework.
Instead of starting from scratch, we invested
in a conversion process from the existing
Portuguese corpus, called Bosque.
The conversion was done by applying a
context-sensitive set of Constraint Grammar
rules to its original deep linguistic
analysis, which was carried out by the
parser PALAVRAS, with some additional
manual corrections. Universal Dependencies
offer the promise of greater parallelism
between languages, a plus for researchers
in many areas. We report the
challenges of dealing with Portuguese, a
Romance language, hoping that our experience
will help others.
Fake news is a phenomenon which is having a significant impact on our social life, in particular in the political world. Fake news detection is an emerging research area which is gaining interest but involved some challenges due to the limited amount of resources (i.e., datasets, published literature) available. We propose in this paper, a fake news detection model that use n-gram analysis and machine learning techniques. We investigate and compare two different features extraction techniques and six different machine classification techniques. Experimental evaluation yields the best performance using Term Frequency-Inverted Document Frequency (TF-IDF) as feature extraction technique, and Linear Support Vector Machine (LSVM) as a classifier, with an accuracy of 92%.
Social media for news consumption is a double-edged sword. On the one hand, its low cost, easy access, and rapid dissemination of information lead people to seek out and consume news from social media. On the other hand, it enables the wide spread of "fake news", i.e., low quality news with intentionally false information. The extensive spread of fake news has the potential for extremely negative impacts on individuals and society. Therefore, fake news detection on social media has recently become an emerging research that is attracting tremendous attention. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. First, fake news is intentionally written to mislead readers to believe false information, which makes it difficult and nontrivial to detect based on news content; therefore, we need to include auxiliary information, such as user social engagements on social media, to help make a determination. Second, exploiting this auxiliary information is challenging in and of itself as users' social engagements with fake news produce data that is big, incomplete, unstructured, and noisy. Because the issue of fake news detection on social media is both challenging and relevant, we conducted this survey to further facilitate research on the problem. In this survey, we present a comprehensive review of detecting fake news on social media, including fake news characterizations on psychology and social theories, existing algorithms from a data mining perspective, evaluation metrics and representative datasets. We also discuss related research areas, open problems, and future research directions for fake news detection on social media.
In the last years, the popularity of smartphones and social networks has been contributing to the spread of fake news. Through these electronic media, this type of news can deceive thousands of people in a short time and cause great harm to individuals, companies, or society. Fake news has the potential to change a political scenario, to contribute to the spread of diseases, and even to cause deaths. Despite the efforts of several studies on fake news detection, most of them only cover English language news. There is a lack of labeled datasets of fake news in other languages and, moreover, important questions still remain open. For example, there is no consensus on what are the best classification strategies and sets of features to be used for automatic fake news detection. To answer this and other important open questions, we present a new public and real dataset of labeled true and fake news in Portuguese, and we perform a comprehensive analysis of machine learning methods for fake news detection. The experiments were performed using different sets of features and employing different types of classification methods. A careful analysis of the results provided sufficient evidence to respond appropriately to the open questions. The various evaluated scenarios and the drawn conclusions from the results shed light on the potentiality of the methods and on the challenges that fake news detection presents.
Following the 2016 US presidential election, many have expressed concern about the effects of false stories ("fake news"), circulated largely through social media. We discuss the economics of fake news and present new data on its consumption prior to the election. Drawing on web browsing data, archives of fact-checking websites, and results from a new online survey, we find: 1) social media was an important but not dominant source of election news, with 14 percent of Americans calling social media their "most important" source; 2) of the known false news stories that appeared in the three months before the election, those favoring Trump were shared a total of 30 million times on Facebook, while those favoring Clinton were shared 8 million times; 3) the average American adult saw on the order of one or perhaps several fake news stories in the months around the election, with just over half of those who recalled seeing them believing them; and 4) people are much more likely to believe stories that favor their preferred candidate, especially if they have ideologically segregated social media networks.
Natural language processing (NLP) is a theory-motivated range of computational techniques for the automatic analysis and representation of human language. NLP research has evolved from the era of punch cards and batch processing (in which the analysis of a sentence could take up to 7 minutes) to the era of Google and the likes of it (in which millions of webpages can be processed in less than a second). This review paper draws on recent developments in NLP research to look at the past, present, and future of NLP technology in a new light. Borrowing the paradigm of 'jumping curves' from the field of business management and marketing prediction, this survey article reinterprets the evolution of NLP research as the intersection of three overlapping curves-namely Syntactics, Semantics, and Pragmatics Curves- which will eventually lead NLP research to evolve into natural language understanding.
Reconhecimento de entidades mencionadas em português: Documentação e actas do HAREM, a primeira avaliação conjunta na área
- Santos
OpenNews is a REST API made in Python to extract news from Portuguese journals
- D Silva