Temporal Novelty Quantification: a New Method to Quantify Temporal Novelty in Social Networks


Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.


DATA : 30/07/2020

HORA: 14:00

LOCAL: Remoto com ampla divulgação divulgação


Temporal Novelty Quantification: a New Method to Quantify Temporal Novelty in Social Networks


Temporal Graph, Concept Drift, Social Networks



Currently, there is an expressive number of social networks used for different purposes, such as connecting people with a common interest in research, job offers, musical preferences, and general content.  These networks have gained significant popularity in recent years.  To demonstrate this phenomenon,  research shows that 71% of young American adults use a social network at least once a day.  With this frequent access and the freedom given by the networks, users started to publish numerous information, from personal photos to texts with opinions on different topics such as politics, entertainment, and health. In this sense, a new volume of information started to be produced, since, before social networks, only specialized professionals with access to conventional media were able to publish their opinions.  From a scientific point of view, several techniques have been proposed in the literature aiming at analyzing the content produced in such social networks. Specifically related to the users’ behavior, it is common to observe their modeling through graphs or time series,  however,  these methods tend to ignore aspects of this behavior, for example, the temporal relationship or the dependence between terms used in publications.  Considering these limitations, this research project was developed based on the hypothesis that the adoption of temporal graphs, together with tools from the areas of text Mining and Time Series, allows the detection of changes in the behavior of users of social networks.  To validate this hypothesis, a new approach was developed to identify points of change in users’ behavior and to associate them with real events that influenced public opinion.   This procedure uses  Text  Mining techniques to find terms,  which will be used later in the creation of temporal graphs, maintaining their relationships in the original texts and their temporal dependencies.  A new measure has also been developed, to quantify how users’ opinions evolve with time.  Finally, a method for automatic detection of behavior change is presented, which aims to identify points when changes occur. This approach was evaluated considering a historic event in Brazil:  the 2018 presidential elections.  This period was chosen due to the volume of publications that effectively established social networks as the main mechanism for political activism.   The results obtained emphasize the importance of the proposed approach and open new possibilities, for example, for the identification of bots that propagate fake news.


Presidente - 2130353 - RICARDO ARAUJO RIOS

Interno - 1232218 - DANIELA BARREIRO CLARO

Externo à Instituição - ANGELO CONRADO LOULA - UEFS

Data da Defesa: 
30/07/2020 - 14:00
Tipo de Defesa: 
Defesa de Mestrado