This paper presents a critical assessment analysis on mental health detection in Online Social Networks (OSNs) based on the data sources, machine learning techniques, and feature extraction method. The appropriateness of the mental health detection was also investigated by identifying its data analysis method, comparison, challenges, and limitations. This study reviewed articles published in major databases between 2007 and 2018 through keyword searches. The articles were screened base on their titles and abstracts before the full texts were reviewed. The articles were coded in accordance with data set (e.g., data sources, keywords, and geographical locations), method of data analysis, machine learning or deep learning technique, classifier performance, and feature extraction method. 22 articles were selected for review from the total of 2770. As OSNs exhibit high potential as a data source in early detection of mental health problems, most researchers used text analysis on a new data set extracted from different OSNs sources. The extracted data were examined using a statistical analysis or machine learning techniques. Several studies also applied multimethod techniques, which included distributing questionnaires while requesting for the respondents' consent to later access and extract information from his/her OSNs account. Big data in OSNs contribute on mental health problem detection. The presented method is an alternative approach to the early detection of mental health problems rather than using traditional strategies, such as collecting data through questionnaires or devices and sensors, which are time-consuming and costly. However, mental health problem detection through OSNs necessitates a comprehensive adoption, innovative algorithms, and computational linguistics to describe its limitations and challenges. Moreover, referrals from mental health specialists as subject matter experts are also required to help obtain accurate and effective information.

Mental health detection in Online Social Network (OSN) is widely studied in the recent years. OSN has encouraged new ways to communicate and share information, and it is used regularly by millions of people. It generates a mass amount of information that can be utilised to develop mental health detection. The rich content provided by OSN should not be overlooked as it could give more value to the data explored by the researcher. The main purpose of this study is to extract and scrutinise related works from related literature on detection of mental health using OSN. With the focus on the method used, machine learning algorithm, sources of OSN, and types of language used for the mental health detection were chosen for the study. The basic design of this study is in the form of a survey from the literature related to current research in mental health. Major findings revealed that the most frequently used method in mental health detection is machine learning techniques, with Support Vector Machine (SVM) as the most chosen algorithm. Meanwhile, Twitter is the major data source from OSN with English language used for mental health detection. The researcher found a few challenges from the previous studies and analyses, and these include limitations in language barrier, account privacy in OSN, single type of OSN, text analysis, and limited features selection. Based on the limitations, the researcher outlined a future direction of mental health detection using language based on user's geo-location and mother tongue. The use of pictorial, audio and video formats in OSN could become one of the potential areas to be explored in future research. Extracting data from multiple sources of OSNs with new features selection will probably improve mental health detection in the future. In conclusion, this research has a big potential to be explored further in the future.

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Massive open online course (MOOC) is an online learning tool, especially for distance learning. It has attracted a great deal of attention by higher education institution around the globe. It also gave rise to academic discussion on MOOC impact, design and research. However, researches on MOOC's impact on language learning are still lacking. Therefore, this study aims to assess the research trend in MOOC for language learning around the globe by using the Systematic Literature Review approach from three databases within periods 2013 until 2018. Ten full assessed articles have been selected from ScienceDirect, ERIC and Research gate. The major findings show that the English language has dominated in language learning using MOOC. It is also revealed that MOOC has the potential to enhance language learning among students in other languages.

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