Special Issue: Big Data and Social Media Intelligence Website: https://www.mdpi.com/journal/applsci/special_issues/Data_Social_Media Deadline for manuscript submissions: 30 December 2020. Applied Sciences (IF: 2.474; ISSN 2076-3417) is an international, peer-reviewed open access journal. The journal is covered the Science Citation Index Expanded (Web of Science), Scopus, INSPEC and Chemical Abstracts. Manuscripts are peer-reviewed and a first decision provided to authors approximately 15.9 days after submission; acceptance to publication is undertaken in 3.6 days (median values for papers published in this journal in the second half of 2019). An article processing charge (APC) of 1800 CHF (Swiss Francs) applies to papers accepted after peer review. For further details on the submission process, please see the instructions for authors at http://www.mdpi.com/journal/applsci/instructions Special Issue Information The spread of low-quality information by means of social media can influence public perception regarding important topics such as politics, health, or climate change. Sometimes these disinformation activities are carried out by groups of coordinated accounts that pollute social debate with a massive number of targeted messages. All these issues contribute to transforming social media into a fertile ground for manipulation attempts. The huge amount of data produced by interactions of people requires the use of sophisticated processing and analysis techniques in order to produce information for intelligence purposes. Recent studies have highlighted the importance of exploring, studying, and modeling large amounts of data in order to discover patterns or relationships and translate them into valuable information. In this Special Issue, we invite authors to submit original research articles related to recent advances at all levels of the applications and technologies of big data and cyber intelligence. We are particularly interested in presenting emerging technologies related to machine learning and deep learning that may have a significant impact on this research field. Topics of interest include (but are not limited to) the following: -Machine learning-based detection techniques to contrast malicious activities; -Network analysis techniques for the characterization and detection of anomalous behaviors; -The detection of coordinated inauthentic behaviors; -Techniques for identifying information polarization on social media; -Investigating the dynamics of the diffusion of disinformation; -Text mining and graph mining for open-source intelligence; -Social network analysis of large networks; -Detection of DeepFake texts, images, and videos; -Fake news and hoax detection -The detection and characterization of information operations.