Application of Data Mining in Social Media

Dear Colleagues,

The growing interest of people around the globe in social networking sites, streaming platforms, and other digital platforms has made the Internet a necessary tool for everyday tasks such as commerce, schooling, leisure activities, and interaction. Nowadays, individuals using the internet have almost limitless accessibility to distribute content. This creates an excellent chance to utilize this beneficial data by turning it into knowledge using the right methods. In such a scenario, data mining techniques become powerful instruments for assisting consumers in finding the most appropriate online content, goods, or services by investigating a variety of social media factors, including user behaviour, communities, network topologies, informational dispersion, and a lot more. However, the vast volumes of social networking information and the extremely complicated and constantly changing social behaviour of consumers have resulted in the development of massive quantities of high-dimension, unreliable, ambiguous, and noisy data from such platforms. Consequently, demonstrating and analysing this enormous ambiguity of electronic content and offering excellent services to customers seems to be very difficult.

Soft computing approaches (i.e., fuzzy logic, machine learning, deep learning, etc.) can play a considerably vital part in tackling the above-mentioned issues because of their ability to cope with data unpredictability and ambiguity. These approaches are not only used in traditional social media analysis but also show effectiveness in distinct areas such as the detection of hate speech, misinformation, sentiment analysis, and abusive behaviour. The topics of interest include, but are not limited to, the following:

1. Social media analysis using data mining;
2. Machine learning models for data mining;
3. Deep learning models for data mining;
4. The intersection of computer vision and artificial intelligence with data mining;
5. Soft computing and modelling in data mining;
6. Applications of data mining in hate speech detection;
7. Misinformation and abusive behaviour detection using data mining;
8. Sentiment analysis techniques in social media using data mining;
9. Data mining approaches for social media in healthcare;
10. Role of data mining in analysing user behaviour on social media platforms.

This Special Issue offers an opportunity for scientists and professionals from computer science, data mining, ubiquitous computing, and social sites to exchange concepts, novel solutions, and strategies for advancing the smart analysis of data and online handling of data. We invite the submission of unpublished, original work that applies any advanced techniques and methodologies to all areas around the subject matter of this Special Issue.

Dr. Junaid Rashid
Prof. Dr. Patrick Siarry
Guest Editors

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