| 1. |
- Boman, Magnus, et al.
(författare)
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Learning machines in Internet-delivered psychological treatment
- 2019
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Ingår i: Progress in Artificial Intelligence. - : Springer Verlag. - 2192-6352 .- 2192-6360. ; 8:4, s. 475-485
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Tidskriftsartikel (refereegranskat)abstract
- A learning machine, in the form of a gating network that governs a finite number of different machine learning methods, is described at the conceptual level with examples of concrete prediction subtasks. A historical data set with data from over 5000 patients in Internet-based psychological treatment will be used to equip healthcare staff with decision support for questions pertaining to ongoing and future cases in clinical care for depression, social anxiety, and panic disorder. The organizational knowledge graph is used to inform the weight adjustment of the gating network and for routing subtasks to the different methods employed locally for prediction. The result is an operational model for assisting therapists in their clinical work, about to be subjected to validation in a clinical trial.
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| 2. |
- Gong, Qingyuan, et al.
(författare)
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Exploring the power of social hub services
- 2019
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Ingår i: World wide web (Bussum). - : Springer Science and Business Media LLC. - 1386-145X .- 1573-1413. ; 22:6, s. 2825-2852
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Tidskriftsartikel (refereegranskat)abstract
- Given the diverse focuses of emerging online social networks (OSNs), it is common that a user has signed up on multiple OSNs. Social hub services, a.k.a., social directory services, help each user manage and exhibit her OSN accounts on one webpage. In this work, we conduct a data-driven study by crawling over one million user profiles from about.me, a representative online social hub service. Our study aims at gaining insights on cross-OSN social influence from the crawled data. We first analyze the composition of the social hub users. For each user, we collect her social accounts from her social hub webpage, and aggregate the content generated by these accounts on different OSNs to gain a comprehensive view of this user. According to our analysis, there is a high probability that a user would provide consistent information on different OSNs. We then explore the correlation between user activities on different OSNs, based on which we propose a cross-OSN social influence prediction model. With the model, we can accurately predict a user’s social influence on emerging OSNs, such as Instagram, Foursquare, and Flickr, based on her data published on well-established OSNs like Twitter.
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