TY - JOUR
T1 - Towards a large-scale twitter observatory for political events
AU - Fernando, Senaka
AU - Amador Díaz López, Julio
AU - Şerban, Ovidiu
AU - Gómez-Romero, Juan
AU - Molina-Solana, Miguel
AU - GUO, Yi-Ke
N1 - Funding Information:
The authors acknowledges Chris Snowden's MSc project for his initial ideas on visualising the Brexit dataset. M. Molina-Solana was funded by the European Union's H2020 R&I programme under the Marie Sklodowska?Curie grant agreement No. 743623.
Funding Information:
The authors acknowledges Chris Snowden’s MSc project for his initial ideas on visualising the Brexit dataset. M. Molina-Solana was funded by the European Union’s H2020 R&I programme under the Marie Sklodowska—Curie grant agreement No. 743623 . Senaka Fernando is a Ph.D. candidate and a Research Assistant at the Data Science Institute of the Imperial College London. Senaka is a Computer Science and Engineering graduate with over 10 years working experience in the industry in the distributed enterprise middleware domain. At the Data Science Institute Senaka is also the Systems Architect of the Data Observatory and the main contributor its Open Visualisation Environment (OVE) project. Senaka’s research interests involve data visualisation in large high resolution display environments. Julio Amador is a Research Fellow at Imperial College London and holds a Ph.D. in Economics from the University of Essex. His area of expertise is applied machine learning (ML). Julio has held different research positions, both in the UK and abroad. His research includes big-data studies of online political participation and applying ML to categorise public opinion and automatically identifying fake news. Julio is currently dedicated to the study of misinformation. Ovidiu Şerban ( erban) is a Research Associate at the Data Science Institute (DSI), Imperial College London. His current work includes real-time Natural Language Processing and Large Scale Visualisation Systems. Ovidiu’s research topics are Natural Language Processing, Machine Learning, Affective Computing and Interactive System Design. He holds a joint Ph.D. from Normandy University (France) and “Babeş-Bolyai” University (Romania), while working at LITIS Laboratory in France. Juan Gómez-Romero is a Research Fellow at the Computer Science and Artificial Intelligence department of Universidad de Granada since 2013. He received his degree in Computer Science (2004) and his Ph.D. (2008) in Intelligent Systems from the same university. He worked as a visiting professor in the Applied Artificial Intelligence Group of Universidad Carlos III de Madrid from 2008 to 2013. His research interests focus on the use of semantic representation models and machine learning techniques to perform automatic reasoning towards higher-level information fusion. He has participated in more than 20 projects in the areas of security, ambient intelligence and energy efficiency. Miguel Molina-Solana is a Marie Curie Research Fellow at the Data Science Institute (DSI) at Imperial College London, working on the DATASOUND project. Before, he was a Research Associate at the DSI, and a postdoc researcher at the Department of Computer Science and Artificial Intelligence of University of Granada. He holds a Ph.D. and a M.Sc. in Computer Science from University of Granada, and a M.Sc. in Soft Computing and Intelligent Systems. His research experience comprises work in the areas of Data Mining, Machine Learning and Knowledge representation applied in different areas such as Music, Energy management and Healthcare. Yike Guo is a Professor of Computing Science in the Department of Computing at Imperial College London. He is the founding Director of the Data Science Institute at Imperial College, as well as leading the Discovery Science Group in the department. Professor Guo also holds the position of CTO of the tranSMART Foundation, a global open source community using and developing data sharing and analytics technology for translational medicine.
PY - 2020/9
Y1 - 2020/9
N2 - Explosion in usage of social media has made its analysis a relevant topic of interest, and particularly so in the political science area. Within Data Science, no other techniques are more widely accepted and appealing than visualisation. However, with datasets growing in size, visualisation tools also require a paradigm shift to remain useful in big data contexts. This work presents our proposal for a Large-Scale Twitter Observatory that enables researchers to efficiently retrieve, analyse and visualise data from this social network to gain actionable insights and knowledge related with political events. In addition to describing the supporting technologies, we put forward a working pipeline and validate the setup with different examples.
AB - Explosion in usage of social media has made its analysis a relevant topic of interest, and particularly so in the political science area. Within Data Science, no other techniques are more widely accepted and appealing than visualisation. However, with datasets growing in size, visualisation tools also require a paradigm shift to remain useful in big data contexts. This work presents our proposal for a Large-Scale Twitter Observatory that enables researchers to efficiently retrieve, analyse and visualise data from this social network to gain actionable insights and knowledge related with political events. In addition to describing the supporting technologies, we put forward a working pipeline and validate the setup with different examples.
KW - Big data
KW - Large-scale visualisation
KW - Scalable resolution display environments
KW - Social media
KW - Twitter analytics
UR - http://www.scopus.com/inward/record.url?scp=85075328392&partnerID=8YFLogxK
U2 - 10.1016/j.future.2019.10.013
DO - 10.1016/j.future.2019.10.013
M3 - Journal article
AN - SCOPUS:85075328392
SN - 0167-739X
VL - 110
SP - 976
EP - 983
JO - Future Generation Computer Systems
JF - Future Generation Computer Systems
ER -