Textual cues for online depression in community and personal settings

Depression is often associated with poor social skills. The Internet allows individuals who are depressed to connect with others via online communities, helping them to address the social skill deficit. While the difficulty of collecting data in traditional studies raises a bar for investigating the cues of depression, the user-generated media left by depression sufferers on social media enable us to learn more about depression signs. Previous studies examined the traces left in the posts of online depression communities in comparison with other online communities. This work further investigates if the content that members of the depression community contribute to the community blogs different from what they make in their own personal blogs? The answer to this question would help to improve the performance of online depression screening for different blogging settings. The content made in the two settings were compared in three textual features: affective information, topics, and language styles. Machine learning and statistical methods were used to discriminate the blog content. All three features were found to be significantly different between depression Community and Personal blogs. Noticeably, topic and language style features, either separately or jointly used, show strong indicative power in prediction of depression blogs in personal or community settings, illustrating the potential of using content-based multi-cues for early screening of online depression communities and individuals.