Special Track

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

Call for Papers

The Social Network and Media Analysis and Mining (SNMAM) is 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 7th SNMAM event will be organized as a track of SIMBig 2022 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.

Social network is a structure formed by a set of entities 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.

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

Authors are invited to submit original and innovative papers that break new ground, present insightful results based on your experience in Data Science. SIMBig 2022 has a broad scope, and specific topics of interest include (but are 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
    • Hate speech, misbehavior and toxic language detection
    • Activity prediction
    • Echo chambers, homophily, filter bubbles, and polarisation analysis
    • Event detection
    • Fake news detection
    • (Epidemic) Spreading and rumors detection
  • Applications of social network and media in business, engineering, scientific, medical and public health domains (e.g., social behavior analysis due to COVID-19), terrorism and/or criminology, fraud detection, politics, cyberbullying, and case studies
  • 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.
  • Submissions must be in PDF, formatted with the Springer Publications format. For details on the Springer style, see here.
  • Please, note that SNMAM submission is double-blind. Therefore, authors need to ensure that their manuscripts are prepared in a way that does not reveal their identity. Failure to follow this instruction will result in an automatic rejection of submission.
  • Papers accepted for publication: the camera-ready version of your paper must be submitted only using Latex format. This is to ensure proper insertion of the manuscript in proceedings. Failure to follow this instruction will result in disregard of submission.

Easychair Submissions Website

Submissions for SNMAM 2022 here.

Program Committee

  • Alexandre Donizeti Alves, Federal University of ABC, Brazil
  • Alexandre Levada, Federal University of São Carlos (UFSCar), Brazil
  • Ankur Singh Bist, Govind Ballabh Pant University of Agriculture and Technology, India
  • Aurea Rossy Soriano Vargas, University of Campinas, Brazil
  • Brett Drury, Liverpool Hope University, England
  • Conceição Rocha, INESC/CPES, Portugal
  • Daniela Godoy, UNICEN University, ISISTAN Research Institute, Argentina
  • Diego Furtado Silva, University of São Paulo (USP), Brazil
  • Geraldo Pereira Rocha Filho, University of Brasília (UnB), Brazil
  • Katarzyna Musial-Gabrys, University of Technology Sydney, Australia
  • Marco Aurélio Domingues, State University of Maringá, Brazil
  • Maria Da Conceição Rocha, INESC-TEC/CPES, Portugal
  • Mathieu Roche, Cirad-TETIS, France
  • Murilo Naldi, Federal University of São Carlos (UFSCar), Brazil
  • Muhammad Nihal Hussain, University of Arkansas at Little Rock, USA
  • Newton Spolaôr, Western Paraná State University, Brazil
  • Pascal Poncelet, University of Montpellier, France
  • Pedro Henrique Luz de Araujo, University of Brasília (UnB), Brazil
  • Pedro Nelson Shiguihara Juáre, Universidad San Ignacio de Loyola
  • Rafael Giusti, Federal University of Amazonas, Brazil
  • Rafael Rodrigues, University of São Paulo, Brazil
  • Rafael Santos, National Institute for Space Research (INPE), Brazil
  • Renan Padua, iFood, Brazil
  • Ricardo Campos, Ci2 - Polytechnic Institute of Tomar; INESC TEC, Portugal
  • Ricardo Marcacini, University of São Paulo, Brazil
  • Ronaldo Prati, Federal University of ABC, Brazil
  • Sabrine Mallek, Institut Supérieur de Gestion, Tunisie
  • Solange Rezende, University of São Paulo (USP), Brazil
  • Sylvia Iasulaitis, Federal University of São Carlos (UFSCar), Brazil
  • Vinicius 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 Paulo, Brazil

Alan Demétrius Baria Valejo

Alan Demétrius Baria Valejo

Ph.D. in Computer Science

Federal University of São Carlos (UFSCar)

São Carlos, Brazil

Fabiana Rodrigues de Góes

Fabiana Rodrigues de Góes

PhD(c) in Computer Science

University of São Paulo (USP)

São Carlos, Brazil