RT Journal Article
T1 Real-time focused extraction of social media users
A1 Martínez Castaño, Rodrigo
A1 Losada Carril, David Enrique
A1 Pichel Campos, Juan Carlos
K1 Big data
K1 Distributed systems
K1 Focused user extraction
K1 Supervised learning
K1 Information retrieval
K1 Real-time processing
K1 Social media
AB In this paper, we explore a real-time automation challenge: the problem of focused extraction of Social Media users. This challenge can be seen as a special form of focused crawling where the main target is to detect users with certain patterns. Given a specific user profile, the task consists of rapidly ingesting Social Media data and early detecting target users. This is a real-time intelligent automation task that has numerous applications in domains such as safety, health or marketing. The volume and dynamics of Social Media contents demand efficient real-time solutions able to predict which users are worth to explore. To meet this aim, we propose and evaluate several methods that effectively allow us to harvest relevant users. Even with little contextual information (e.g., a single user submission), our methods quickly focus on the most promising users. We also developed a distributed microservice architecture that supports real-time parallel extraction of Social Media users. This modular architecture scales up in clusters of computers and it can beeasily adapted for user extraction in multiple domains and Social Media sources. Our experiments suggest that some of the proposed prioritisation methods, which work with minimal user context, are effective at rapidly focusing on the most relevant users. These methods perform satisfactorily with huge volumes of users and interactions and lead to harvest ratios 2 to 9 times higher than those achieved by random prioritisation
PB IEEE
SN 2169-3536
YR 2022
FD 2022
LK http://hdl.handle.net/10347/30186
UL http://hdl.handle.net/10347/30186
LA eng
NO R. Martínez-Castaño, D. E. Losada and J. C. Pichel, "Real-Time Focused Extraction of Social Media Users," in IEEE Access, vol. 10, pp. 42607-42622, 2022, doi: 10.1109/ACCESS.2022.3168977
NO This work was supported in part by the Ministerio de Ciencia e Innovación (MICINN) under Grant RTI2018-093336-B-C21 and Grant PLEC2021-007662; in part by Xunta de Galicia under Grant ED431G/08, Grant ED431G-2019/04, Grant ED431C 2018/19, and Grant ED431F 2020/08; and in part by the European Regional Development Fund (ERDF)
DS Minerva
RD 27 abr 2026