Dear Colleagues,

In the era of social media, users have changed the way they communicate, overcoming geographical distances. The shared content is a valuable, dynamic, and continually updated source of information that may be applied to different objectives for addressing marketing strategies, ad targeting, event detection and tracking, and so forth. Nevertheless, the nature of social media contents require Big and Fast Data enabled solutions, and the data value may be carried out only if we are able to filter out dirty and noisy data by also measuring the quality of information.

Over the years, much of the literature about social media analysis has focused on information extraction in order to guide recommender systems in different areas. However, social media contents often convey news, share information during critical events, support a political campaign, and so on. Further, it is sometimes difficult for users to recognize true information or evaluate the trustworthiness of its authors. Thus, it is also crucial to move attention towards these problems and evaluate the impact and new methods against the EU’s 2018 general data protection regulation (GDPR), recent events around information theft, and the role of social media in crime organization activities.

This Special Issue on "Advances in Social Media analysis" aims to collect recent advances in social media data stream analysis. In particular, we would like to collect works aiming to extract knowledge embedded in shared media (text, images, and video) for qualitative filtering, correlating, aggregating, summarizing, and turning data into usable understandable and actionable knowledge.

Potential topics include but are not limited to the following:

• Quality of information on social media;
• Information retrieval on social media;
• Information ranking on social media;
• Recommender systems;
• Misinformation and fake news;
• Social media marketing;
• Topic detection and tracking;
• Information summarization;
• Security and privacy for social network and media;
• Cloud-based data analysis for social network and media;
• Social media analysis challenges and applications.

Prof. Giuseppe Fenza
Prof. Xu An Wang
Guest Editors

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