{"type":"ScholarlyArticle","title":"BlueMemo: Depression Analysis through Twitter Posts","authors":[{"name":"Pengwei Hu","slug":"pengwei-hu","given":"Pengwei","family":"Hu","affiliation":"IBM Research , China"},{"name":"Chenhao Lin","slug":"chenhao-lin","given":"Chenhao","family":"Lin","affiliation":"Xi'an Jiaotong University , China"},{"name":"Hui Su","slug":"hui-su","given":"Hui","family":"Su","affiliation":"Pattern Recognition Center , Wechat AI , Tencent Inc , China"},{"name":"Shaochun Li","slug":"shaochun-li","given":"Shaochun","family":"Li","affiliation":"IBM Watson Health , China"},{"name":"Xue Han","slug":"xue-han","given":"Xue","family":"Han","affiliation":"IBM Research , China"},{"name":"Yuan Zhang","slug":"yuan-zhang","given":"Yuan","family":"Zhang","affiliation":"IBM Research , China"},{"name":"Jing Mei","slug":"jing-mei","given":"Jing","family":"Mei","affiliation":"IBM Research , China"}],"abstract":"The use of social media runs through our lives, and users' emotions are also affected by it. Previous studies have reported social organizations and psychologists using social media to find depressed patients. However, due to the variety of content published by users, it isn't effortless for the system to consider the text, image, and even the hidden information behind the image. To address this problem, we proposed a new system for social media screening of depressed patients named BlueMemo. We collected real-time posts from Twitter. Based on the posts, learned text features, image features, and visual attributes were extracted as three modalities and were fed into a multi-modal fusion and classification model to implement our system. The proposed BlueMemo has the power to help physicians and clinicians quickly and accurately identify users at potential risk for depression.","doi":"10.24963/ijcai.2020/760","url":"https://ijcai.org/proceedings/2020/760","pdf":"https://ijcai.org/proceedings/2020/0760.pdf","markdown":"https://ijcai.org/proceedings/2020/760.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/760","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Demos","track":"Demos","number":760,"pages":{"first":5252,"last":5254},"keywords":["Natural Language Processing: general","Knowledge Representation and Reasoning: general","Machine Learning: general","Computer Vision: general"],"videos":[],"references":[{"position":1,"raw":"[Devlin et al., 2019] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. 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