6 research outputs found

    Desenvolvendo um classificador de clickbait para tweets com word embeddings

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    TCC(graduação) - Universidade Federal de Santa Catarina. Centro Tecnológico. Sistemas de Informação.Clickbaits são uma forma de título de notícia vago, porém intrigante, com objetivo de fazer o leitor clicar na notícia e acessar algum site. Com a propagação recente deste tipo de manchete, uma busca por uma maneira automática de detecta-los vem se tornando cada vez mais relevante. A tarefa compartilhada Clickbait Challenge ajudou a avançar os estudos desta área, com diversos trabalhos competindo para obter os melhores resultados para um conjunto de dados fornecido. Em um desses, foram utilizados word embeddings para realizar a classificação. Este TCC faz um estudo de propostas para classificação de clickbaits e propõe melhorias no trabalho do Clickbait Challenge que usa word embedding, usando Short Semantic Patterns num modelo de machine learining treinado com regressão linear. Nosso modelo atinge um F1 score de 0,793, melhor que o modelo base, e tem um erro médio quadrático de 0,113, melhor que o modelo base sobre a mesmo subconjunto de dados utilizados. Em conclusão, o modelo descrito neste trabalho comprova que características (features) extraídas mediante análise semântica, tais como padrões SSP, contribuem para a melhoria dos resultados do classificador de clickbaitsClickbaits are a type of headlines that are empty but intriguing, with the objective of making the reader click on the article and access some website. With the recent propagation of this headlines, a search for some way of identifying them has been becoming more relevant. The shared task of the Clickbait challenge has helped advance the studies on this area, with many works competing to obtain the best results for a data set provided. In one of those, word embeddings are utilized to make the classifier. This thesis studies proposals for clickbait classification and proposes improvements on the Clickbait Challenge work that uses word embeddings, using the text's semantics on a machine learning model trained with linear regression. Our model reached a F1 Score of 0,793, better than the base model, and has a MSE of 0,113, better than the base model over the same subset of data. In conclusion, the model described in this work proves that features extracted from semantic analysis, like SSP patterns, contribute to the improvement of results of clickbait classifiers

    The Psychology of Fake News

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    This volume examines the phenomenon of fake news by bringing together leading experts from different fields within psychology and related areas, and explores what has become a prominent feature of public discourse since the first Brexit referendum and the 2016 US election campaign. Dealing with misinformation is important in many areas of daily life, including politics, the marketplace, health communication, journalism, education, and science. In a general climate where facts and misinformation blur, and are intentionally blurred, this book asks what determines whether people accept and share (mis)information, and what can be done to counter misinformation? All three of these aspects need to be understood in the context of online social networks, which have fundamentally changed the way information is produced, consumed, and transmitted. The contributions within this volume summarize the most up-to-date empirical findings, theories, and applications and discuss cutting-edge ideas and future directions of interventions to counter fake news. Also providing guidance on how to handle misinformation in an age of “alternative facts”, this is a fascinating and vital reading for students and academics in psychology, communication, and political science and for professionals including policy makers and journalists
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