Sebastião Pais

Universidade da Beira Interior | UBI · Department of Computer Science

Mobs, riots, and crowds greatly impact societies in numerous ways. On a mid-term basis, the collective behavior of crowds can be an agent of social change and an affirmation of existing social mores and structures. On a short-term basis, they may have dramatic consequences such as killings, slaughters, or material damages, which badly reflect how fractured our societies can be. Daily reports of demonstrations in various parts of the world emerge. More recently we have seen anti-government protests in the US, Venezuela, and some European countries. Between 2012 and 2015, hundreds of thousands of people protested in the streets of Portugal (goo.gl/WeH7qR) against troika's public policies, sometimes resulting in violent clashes. In France, one recent anti-police brutality protest turned violent, with masked youths and police engaging in running street battles, after the death of a young environmental activist. Seven police officers were injured after around 2,000 people gathered to protest, with some throwing Molotov Cocktails (goo.gl/FQQCdi). In India, one of the recent news that attracted attention is the Gujarat violence, which happened due to the caste-based protest led by Patel Community (goo.gl/tymXhc). This caused the death of 9 people, and 18 were injured. Army personnel had to be deployed. Schools were closed and trains bound to the city were canceled. Within this project, we propose to develop a multilingual surveillance system capable of detecting emerging crowds by identifying rising events that foster high focus, high energy and high emotion on social media. Our fundamental hypothesis is that virtual crowds evidence similar characteristics to real crowds, which may allow their modelization in terms of complex computer systems by relying on advanced natural language processing and machine learning techniques. The current project lays at the intersection of important scientific research topics, namely urban informatics, natural language processing for social media, predictive analytics over big social data and image semtiment analysis.

The HULTIGCorpus-V.1 (http://hultigcorpus.di.ubi.pt/) dataset is being created by the Centre For Human Language Technology And Bioinformatics at University of the Beira Interior to support research on information retrieval and related human language technologies. Currently, the dataset has already indexed text from 10 million web pages, in various web domains (.com, .uk, .es, .fr, .mo, .pt, .ru, .bl, etc),the indexation began in January 2017.