Special Track

Track on Social Network and Media Analysis and Mining (SNMAM)

Call for Papers

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.

Scope and Topics

SNMAM includes all the topics related to social network and media analysis and mining. The topics suitable for SNMAM include, but not limited to:

  • Crowdsourcing of social network and media data generation and collection
  • Data preparation and data modeling for social networks and social medias
  • Exploratory and visual data analysis of social network and media data
  • Identification and discovery of dynamics and evolution patterns of social network and media data based on data mining and machine learning approachess
  • Topological and spatio-temporal aspects in social networks and social media
  • Large-scale graph, search and time series algorithms on social networks
  • Social network analysis and mining tasks:
    • Anomaly and outlier detection
    • Community discovery
    • Link and node classification
    • Link and node prediction
    • Entity resolution
    • (Social) Graph construction
  • Data mining applications on social network and media data:
    • Recommender systems
    • Opinion and suggestion mining
    • Sentiment analysis
    • Misbehavior detection
    • Activity prediction
    • Event detection
    • (Epidemic) Spreading
  • Applications of social network and media in business, engineering, scientific, medical and public health domains, terrorism and/or criminology, fraud detection, politics, cyberbullying, and case studies
    • Special case study: social behavior analysis due to COVID-19
  • Analysis and mining of location-based social networks, urban (social) networks, multilayer social networks and others
  • Social media monitoring and analysis
  • Ethics, privacy and security in online social networks and social media platforms
  • Tools and infrastructures for social networking platforms and web communities
  • New applications and services arising from big data, social network analysis and social media.
Long Papers
Long Papers are limited to a total of 13-16 pages, including all content and references, and must be in PDF formatted with the Springer publication format.
Short Papers
Short Papers are limited to a total of 7-12 pages, including all content and references, and must be in PDF formatted with the Springer publication format.

Paper Submission Guidelines

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.

Easychair Submissions Website

Submissions for SNMAM 2021 here.

Program Committee

  • Alexandre Luis Magalhães Levada, Federal University of São Carlos (UFSCar), Brazil
  • Ankur Singh Bist, Signy Advanced Technology, India
  • Aurea Soriano Vargas, University of Campinas, Brazil
  • Brett Drury, LIAAD-INESC-TEC, Portugal
  • Celia Ghedini Ralha, University of Brasília (UnB), Brazil
  • Cesar Henrique Comin, Federal University of São Carlos (UFSCar), Brazil
  • Daniela Godoy, UNICEN University, Argentina
  • Dibio Leandro Borges, University of Brasília (UnB), Brazil
  • Diego Furtado Silva, Federal University of São Carlos (UFSCar), Brazil
  • Geraldo Pereira Rocha Filho, University of Brasília (UnB), Brazil
  • Guilherme Novaes Ramos, University of Brasília (UnB), Brazil
  • Heloísa de Arruda Camargo, Federal University of São Carlos (UFSCar), Brazil
  • José Reinaldo da Cunha Santos Aroso Vieira da Silva Neto, University of Brasília (UnB), Brazil
  • Jorge Poco, Fundação Getulio Vargas, Brazil
  • Luca Rossi, Queen Mary University of London, UK
  • Maria Da Conceição Rocha, INESC/CPES, Portugal
  • Mathieu Roche, Cirad-TETIS, France
  • Newton Spolaôr, State University of Western Paraná, Brazil
  • Pascal Poncelet, University of Montpellier, France
  • Paulo Eduardo Althoff, University of Brasília (UnB), Brazil
  • Pedro Nelson Shiguihara Juárez, Universidad San Ignacio de Loyola, Peru
  • Rafael Delalibera Rodrigues, University of São Paulo (USP), Brazil
  • Rafael Giusti, Federal University of Amazonas, Brazil
  • Rafael Santos, National Institute for Space Research (INPE), Brazil
  • Ronaldo Prati, Federal University of ABC, Brazil
  • Tiago Colliri, University of São Paulo (USP), Brazil
  • Victor Stroele, Federal University of Juiz de Fora, Brazil
  • Vinicius Ruela Pereira Borges, University of Brasília (UnB), Brazil
  • Willy Ugarte, University of Applied Sciences (UPC), Peru

Organizers

Jorge Valverde-Rebaza

Jorge Valverde-Rebaza

PhD in Computer Science

Department of Scientific Research, Visibilia

São Carlos, Brazil

Alan Valejo

Alan Demétrius Baria Valejo

Ph.D. in Computer Science

Federal University of São Carlos (UFSCar)

São Carlos, Brazil

Thiago de Paulo Faleiros

Thiago de Paulo Faleiros

Ph.D. in Computer Science

University of Brasília (Unb)

Brasília - DF, Brazil