ICCCI 2022 - ML-SDA 2022 : Special Session on Machine Learning for Social Data Analytics (ML-SDA) - 14th International Conference on Computational Collective Intelligence (ICCCI 2022)

ML-SDA 2022
Special Session on Machine Learning for Social Data Analytics
at the 14 th International Conference on Computational Collective Intelligence (ICCCI 2022) Hammamet, Tunisia, September 28-30, 2022
https://iccci.pwr.edu.pl/2022/download/ICCCI_2022_Special_Session_(ML-SDA_2022)_CFP_v.1.0.pdf


Description:

Nowadays, social networks play a crucial role in every aspect of our daily life. They are seen as huge data mines that attract researchers to tackle several challenges. Ranging from predicting users simple reactions to representing more complex personality features, mining social data clear the way for promising applications that significantly impact our real-life. Meanwhile, machine learning methods, characterized by recent breakthroughs of Deep Learning and Big Data, yield ultrapractical models dealing with data analytics. Following these advancements, text processing for example, has never been as intuitive as it is today. Particularly, embedding data, as dense vectors, ensures faithful representations of the text content making it possible to use the wide spectrum of machine learning methods for social data analytics. Analogically, mining other social data types (image, video, reactions, etc.) and layouts (graph, time series, sequential, etc.) has known remarkable success owing to the recent developments in machine learning methods.

Main Goals of this Special Session:

The main objective of the 2022 Special Session on Machine Learning for Social Data Analytics (ML-SDA 2022), hosted in the 14 th International Conference on Computational Collective Intelligence (ICCCI 2022) is to bring together scientists, researchers, engineers, and practitioners to present and to discuss recent and innovative research papers that address the breakthroughs of machine learning in social data analytics. It focuses on new methods, models and applications that use machine learning to process social data and namely to extract useful knowledge from it. The ML-SDA 2022 topics of interest deal with, but are not limited to, the use of different types of machine learning methods for:

- Sentiment and opinion mining
- Emotion detection
- Hate speech analysis
- Streaming data processing
- Multilingual content processing
- Hot Topic detection and tracking
- Abnormal and fake content identification
- Irony and sarcasm detection
- User behavior modeling and prediction
- Link prediction
- Community detection
- Opinion evolution modeling
- Social influence modeling
- Popularity prediction
- User Profiling from social data
- Recommendation systems using social data


Submission:

All contributions should be original and not published elsewhere or intended to be published during the review period. Authors are invited to submit their papers electronically in pdf format, through EasyChair. All the special sessions are centralized as tracks in the same conference management system as the regular papers. Therefore, to submit a paper please activate the following link and select the track: ML-SDA 2022: Special Session on Machine Learning for Social Data Analytics.
https://easychair.org/conferences/?conf=iccci20220

Authors are invited to submit original previously unpublished research papers written in English, of up to 13 pages, strictly following the LNCS/LNAI format guidelines. Authors can download the Latex (recommended) or Word templates available at Springer's web site. Submissions not following the format guidelines will be rejected without review. To ensure high quality, all papers will be thoroughly reviewed by the ML-SDA 2022 Program Committee. All accepted papers must be presented by one of the authors who must register for the conference and pay the fee. The conference proceedings will be published by Springer in the prestigious series LNCS/LNAI (indexed by ISI CPCI-S, included in ISI Web of Science, EI, ACM Digital Library, dblp, Google Scholar, Scopus, etc.).