SNMAM: Track on Social Network and Media Analysis and Mining

Call for Papers

The Social Network and Media Analysis and Mining (SNMAM) is a forum that brings researchers and practitioners to discuss research trends and techniques related to analyzing and mining social network and media data. The 8th SNMAM event will be organized as a track of SIMBig 2023 in Monterrey, México, 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 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 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 and 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 analyzing and mining social network and social media data and their application to real-world problems. Young scientists and researchers from scientific centers, students and graduates, and industrial partners are welcome to participate.

Scope and Topics

Authors are invited to submit original and innovative papers that break new ground and present insightful results based on your experience in Data Science. SIMBig 2023 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 media
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 approaches
Topological and spatiotemporal 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 networks and media in business, engineering, scientific, medical and public health domains, 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 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 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.

LATEX

MICROSOFT WORD

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. Follow this instruction to avoid an automatic rejection of the 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.

Submissions Website

Submissions for SNMAM must be performed strictly via EasyChair System here.

Program Committee

Alexandre Donizeti Alves, Federal University of ABC, Brazil;
Ankur Singh Bist, Graphic Era Hill University, India;
Aurea Soriano Vargas, State University of Campinas, Brazil;
Brett Drury, Liverpool Hope University, UK;
Cesar Beltran, Pontificia Universidad Catolica del Peru, Peru;
Cristian Gawron, South Westphalia University of Applied Sciences, Germany;
Edwin Villanueva, Pontificia Universidad Catolica del Peru, Peru;
Fabiola Souza Fernandes Pereira, Federal University of Uberlandia, Brazil;
Francesco Bailo, The Univesity of Sydeney, Ausatralia;
Luis Paulo Faina Garcia, University of Brasilia, Brazil;
Katarzyna Musial, University of Technology Sydney, Australia;
Marcos Aurélio Domingues, State University of Maringa, Brazil;
Maria Cristina Ferreira de Oliveira, University of São Paulo, Brazil;
Murilo Naldi, Federal University of São Carlos, Brazil;
Newton Spolaor, Western Parana State University, Brazil;
Rafael Delalibera Rodrigues, University of São Paulo, Brazil;
Rafael Santos, Brazilian National Institute for Space Research (INPE), Brazil;
Renan de Padua, iFood, Brazil;
Sabrine Mallek, ICN Business School, France;
Victor Stroele, Federal University of Juiz de Fora, Brazil.

Organizers

Jorge Valverde-Rebaza

PhD in Computer Science

Tecnológico de Monterrey

Mexico City, Mexico

Alan Demétrius Baria Valejo

Ph.D. in Computer Science

Federal University of São Carlos (UFSCar)

São Carlos, Brazil

Fabiana Rodrigues de Góes

PhD in Computer Science

Visibilia

São Paulo, Brazil

Thiago de Paulo Faleiros