Socio-Temporal Characterization of Content in Social Networks based on Stream Processing

Wednesday, November 24, 2021
Speaker(s) : Diogo Menezes Ferrazani Mattos (Universidade Federal Fluminense, Brésil)

Abstract: The propagation dynamics and speed of subjects published on Twitter characterizes the social network platform as an uninterrupted data source. Nevertheless, the characters number limitation and the large volume of data are latent challenges for knowledge extraction. This seminar discusses a distributed approach based on complex network metrics for the socio-temporal characterization of textual data from Twitter. The proposal integrates Apache Kafka for the data ingestion and Apache Spark for the data stream processing to ensure the continuous and efficient capture of content from different sources. The proposal identifies, correlates and monitors the use of hashtags in real-time through a dynamic graph structure, generating an ontology about the topic of interest. Unlike previous works, which use historical data, the proposal is applied to a real use case with great repercussions and engagement of Twitter users. By evaluating metric fluctuations such as centrality, diameter and density for multiple components of the generated graph, the results reveal writing trends and relationship patterns that reinforce the feeling of echo chambers and media opportunism in the logic of using hashtags.
Bio: Diogo Menezes Ferrazani Mattos is a professor at Universidade Federal Fluminense (Niterói, Brazil). He received his degrees of D.Sc. and M.Sc. in Electrical Engineering from Universidade Federal do Rio de Janeiro, Rio de Janeiro, Brazil, in 2017 and 2012. He received a Computer and Information Engineer degree from Universidade Federal do Rio de Janeiro, in 2010, awarded with Magna Cum Laude. Between 2015 and 2016, he had a sandwich scholarship to work on his Ph.D. thesis on the LIP6 (Laboratoire d’Informatique de Paris 6) at Sorbonne Université (Campus Pierre et Marie Curie), Paris, France. In 2017, he was a postdoctoral fellow in the Electrical Engineering Program at Universidade Federal do Rio de Janeiro. His publications and research interests cover network security, next-generation networks, virtualization, software-defined networking, and the Internet of the future. He coordinates research projects focused on providing secure primitives based on machine learning for next-generation networks and retrieving knowledge from social networks.

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