S ocial networks have been developed as a great point for its users to communicate with their interested friends and share their opinions, photos, and videos reflecting their moods, feelings and sentiments. This creates an opportunity to analyse social network data for user’s feelings and sentiments to investigate their moods and attitudes when they are communicating via these online tools. People feelings could be positive, negative or neutral. In order to determine depression levels, person’s negative response is important because it tells us about the negativism.
Although diagnosis of depression using social networks data has picked an established position globally, there are several dimensions that are yet to be detected. In this study, we aim to perform depression analysis on Facebook data collected from an online public source. To investigate the effect of depression detection, we propose machine learning technique as an efficient and scalable method.
The widespread use of social media may provide opportunities to help reduce undiagnosed mental illness. A growing number of studies examine mental health within social media contexts, linking social media use and behavioural patterns with stress, anxiety, depression, suicidality, and other mental illnesses. The greatest number of studies of this kind focus on depression. Depression continues to be under-diagnosed, with roughly half the cases detected by primary care physicians and only 13–49% receiving minimally adequate treatment.
Automated analysis of social media potentially provides methods for early detection. If an automated process could detect elevated depression scores in a user, that individual could be targeted for a more thorough assessment, and provided with further resources, support, and treatment. Studies to date have either examined how the use of social media sites correlates with mental illness in users or attempted to detect mental illness through analysis of the content created by users. This review focuses on the latter: studies aimed at predicting mental illness using social media. We first consider methods used to predict depression, and then consider four approaches that have been used in the literature. We compare the different approaches, provide direction for future studies, and consider ethical issues.
In this study, we aim to analyse Facebook data to detect any factors that may reflect the depression of relevant Facebook’s users. Various machine learning techniques are employed for such purpose.
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