199 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 11 DOI: 10.4018/978-1-5225-8567-1.ch011 ABSTRACT As people around the world are spending increasing amounts of time online, the question of how online experiences are linked to health and wellbeing is essential. Depression has become a public health concern around the world. Traditional methods for detecting depression rely on self-report techniques, which sufer from inefcient data collection and processing. Research shows that symptoms linked to mental illness are detectable on social media like Twitter, Facebook, and web forums, and automatic methods are more and more able to locate inactivity and other mental disease. The pattern of social media usage can be very helpful to predict the mental state of a user. This chapter also presents how activities on Facebook are associated with the depressive states of users. Based on online logs, we can predict the mental state of users. INTRODUCTION This chapter also presents how activities on Facebook are associated with the depressive states of users. Based on online logs, we can predict the mental state of users. For example depressed individuals re- ported smaller involvement on social networks regarding comments and likes, the two popular forms of interactions. In contrast to the decreased level of interactions, depressed individuals showed an increase in the wall post rates and were active online during odd times of the day, which can be interpreted as an endemic behavior linked to the perceived degree of loneliness among young adults who are avid users of social media. Social Media Analytics to Predict Depression Level in the Users Mohammad Shahid Husain https://orcid.org/0000-0003-4864-9485 Ibri College of Applied Sciences, Oman