[AISWorld] CFP - IJCAI 2023 Workshop on Deep Learning Methods for Social Media Mining (MAISoN 2023)

[Fattane Zarrinkalam]

Call For Papers=============
The 9th International Workshop on Mining Actionable Insights from Social
Networks (MAISoN 2023)Special Edition on Deep Learning Methods for Social
Media Mining at 32nd International Joint Conference on Artificial Intelligence
(IJCAI 2023) August 19, 2023Workshop website:
https://2023.maisonworkshop.org/

Important Dates:

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- Submission deadline:  April 26, 2023

- Acceptance notification: May 30, 2023


Details:

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The wide adoption of social networks resulted in an ocean of data which
presents an interesting opportunity for performing data mining and
knowledge discovery in a real-world context. The enormity and high variance
of the information that propagates through large user communities
influences the public discourse in society and sets trends and agendas in
topics that range from marketing, education, business and medicine to
politics, technology and the entertainment industry. Mining the contents of
social networks provides an opportunity to discover social structure
characteristics, analyze action patterns qualitatively and quantitatively,
and gives the ability to predict future events. The recent highly
impressive advances in deep learning and natural language processing
present exciting opportunities for developing automatic methods for the
collection, extraction, representation, analysis, and validation of social
media data for real-world applications.

In this workshop, we aim to invite and gather researchers and practitioners
from across the world and, in particular, from different disciplines, such
as information retrieval, data mining and machine learning. But also social
network analysis, network science and complex networks. The goal is to
share ideas and research achievements in order to deliver technology and
solutions for mining actionable insight from social network data.


Topics of interest include, but are not limited to:

=======================================

The topics of interest include but are not limited to applying deep learning
techniques in the following areas:

- Modeling and analysis of real-world (social) networks.

- Analysis of social media, viral marketing and the spreading of fake news.

- Predictive modeling based on social networks such as box office
prediction, election prediction, and flu prediction.

- Product adaptation models with social networks such as sale price
prediction, new product popularity prediction, brand popularity, and
business downfall prediction.

- Information diffusion modeling with social networks such as sentiment
diffusion in social networks and competitive intelligence.

- User modeling and social networks including predicting daily user
activities, recurring events, user churn prediction.

- Social networks and information/knowledge dissemination such as topic and
trend prediction, prediction of information diffusion patterns, and
identification of causality and correlation between
event/topics/communities.

- Social influence analysis on online social networks.

- Trust and reputation in social networks.

- New datasets and evaluation methodologies for predictive modeling in
social networks.


Submission Instructions:

====================

We invite the submission of regular research papers (no longer than 9 pages
in total: 7 pages for the body of the paper (including all figures/tables),
plus up to 2 additional pages with references) as well as position papers
(2-4 pages). Submissions must adhere to IJCAI 2023 guidelines available at
https://www.ijcai.org/authors_kit.


All submissions must be submitted in PDF format according to the guidelines
through the Easychair installation:
https://easychair.org/conferences/?conf=maison2023.


Organizers (Alphabetical):

=====================


Ebrahim Bagheri, Toronto Metropolitan University, bagheri at ryerson.ca

Ashique KhudaBukhsh, Rochester Institute of Technology (RIT), axkvse at rit.edu


Fattane Zarrinkalam, University of Guelph, fzarrink at uoguelph.ca

Amirhossein Zohrehvand, Leiden University, a.h.zohrehvand at sbb.leidenuniv.nl