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Dissertação de Jonathas Gabriel Dipp Harb

Detalhes do Evento

Aluno: Jonathas Gabriel Dipp Harb
Orientadora: Profª Drª Karin Becker

Título: Using a convolutional neural network to compare emotional reactions on Twitter to mass violent events
Linha de Pesquisa: Mineração, Integração e Análise de Dados

Data: 15/03/2019
Hora: 13h30
Local: Prédio 43424 – Auditório Prof. Castilho do Instituto de Informática da UFRGS

Banca Examinadora:
Prof. Dr. Duncan Dubugras Alcoba Ruiz (PUCRS)
Profª. Drª. Renata de Matos Galante (UFRGS)
Profª. Drª. Viviane Pereira Moreira (UFRGS)

Presidente da Banca: Profª Drª Karin Becker

Abstract: The number of mass violent attacks, particularly mass shooting and terrorism events, has increased in recent years. Understanding the emotional reaction of the population is very important to help them to cope with the constant sense of threat and fear effectively. In this paper, we apply deep learning techniques to classify emotions expressed by Twitter users, and develop a comparative analysis of emotional reactions to twelve mass violent events, detailed using demographics information extracted from the users profiles. To classify the emotions, we trained a Convolutional Neural Network combining sets of automatically filtered training seeds and pre-trained word embeddings. Our study compared four terrorism events and eight mass shooting incidents in terms of emotional shift and prevalent emotions; influence on emotions of gender, age, closeness to the event and number/type of victims; and terms used to express reactions. We observed similar patterns for both kinds of events, mainly in terms of prevalent emotions (anger, fear, and sadness, respectively) and influence of genderon emotions (e.g. fear for women, and anger formen). The proximity to the events is influential only in mass shooting events. Tweeters expressing fear and sadness tend to share words of empathy and support, while those expressing anger tend to use intense words of hate, intolerance and call for justice.

Keywords: Deep Learning. Convolutional Neural Network. Sentiment Analysis. Twitter. Mass Violent Events.