Special Track
Social network is a structure formed by a set (or groups of sets) of entities (e.g. people, organizations, etc.) with some patterns of social interaction. Social Network Analysis (SNA) is focused on uncovering these patterns through the use of network science and graph theory. In the last decades, online social networks (OSNs) have become the most widely used web platforms due to the variety of services they offer to their users. This explosion has led to the diversification of both content (text, images, audio and other) and the sources (e.g. newscasts, newspapers and other companies) in OSNs. The need for processing and analyzing this content as well as understanding its impact on users' lives, has allowed for the rapid development of various techniques for mining and analysis of social media data.
In this context, the Social Network and Media Analysis and Mining (SNMAM) provides a forum that brings both researchers and practitioners to discuss research trends and techniques related to the analysis and mining of social network and media data. The 5th SNMAM event will be organized as a track of SIMBig 2021 in Lima, Peru, as an interdisciplinary venue for computer scientists, computer engineers, software engineers and application developers who work on networks and web-based methods.
Therefore, SNMAM welcomes experimental and theoretical works on analysis and mining of social network and social media data along with their application to real-world problems. Young scientists and researchers from scientific centers, students and graduates, as well as industrial partners are welcome to participate.
SNMAM includes all the topics related to social network and media analysis and mining. The topics suitable for SNMAM include, but not limited to:
All research submissions must be in English. Please, note that SNAMAM submission is double-blind. This means that both the reviewer and author identities are concealed from the reviewers, and vice versa, throughout the review process. To facilitate this, authors need to ensure that their manuscripts are prepared in a way that does not reveal their identity, i.e. without any author names and affiliations in the text or on the title page as well as self-identifying citations and references in the article text should either be avoided or left blank.
Submissions must be in PDF, formatted with the Springer Publications format. For details on the Springer style, see here.
Submissions for SNMAM 2021 here.
