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Psychological stress is threatening people’s health. It is non-trivial to detect stress timely for proactive care. With the popularity of social media, people are used to sharing their daily activities and interacting with friends on social media platforms, making it feasible to leverage online social network data for stress detection. In this paper, we find that users stress state is closely related to that of his/her friends in social media, and we employ a large-scale dataset from real-world social platforms to systematically study the correlation of users’ stress states and social interactions. We first define a set of stress-related textual, visual, and social attributes from various aspects, and then propose a novel hybrid model - a factor graph model combined with Convolutional Neural Network to leverage tweet content and social interaction information for stress detection. Experimental results show that the proposed model can improve the detection performance by 6-9 percent in F1-score. By further analyzing the social interaction data, we also discover several intriguing phenomena, i.e., the number of social structures of sparse connections (i.e., with no delta connections) of stressed users is around 14 percent higher than that of non-stressed users, indicating that the social structure of stressed users’ friends tend to be less connected and less complicated than that of non-stressed users. [ABSTRACT FROM PUBLISHER]

Exponential growth of information generated by online social networks demands effective and scalable recommender systems to give useful results. Traditional techniques become unqualified because they ignore social relation data; existing social recommendation approaches consider social network structure, but social contextual information has not been fully considered. It is significant and challenging to fuse social contextual factors which are derived from users’ motivation of social behaviors into social recommendation. In this paper, we investigate the social recommendation problem on the basis of psychology and sociology studies, which exhibit two important factors: individual preference and interpersonal influence. We first present the particular importance of these two factors in online behavior prediction. Then we propose a novel probabilistic matrix factorization method to fuse them in latent space. We further provide a scalable algorithm which can incrementally process the large scale data. We conduct experiments on both Facebook style bidirectional and Twitter style unidirectional social network data sets. The empirical results and analysis on these two large data sets demonstrate that our method significantly outperforms the existing approaches. [ABSTRACT FROM AUTHOR]

In the intelligence community, the existence of a malicious insider poses a severe threat to information, to the actual analytic process, and, ultimately, to any decision-making process relying on such information and analyses. An analyst with malicious intent can create irreversible short-term, as well as long-term, damage that is hard to detect. In this paper, we propose a novel methodology that detects malicious analysts who attempt to manipulate decision makers' perceptions through their intelligence reports. This detection method relies on each analyst's working style, which we assume to be consistent from task to task. In order to measure an analyst's degree of consistency, we employ a user-modeling technique that automatically builds a computational model of each analyst based on observation of their activities. We hypothesize that inconsistency is mainly caused by malicious actions. Therefore, the detection method evaluates how consistent an analyst is across different tasks and raises an alert if any significantly large inconsistency is detected. A normalization procedure is employed which allows us to compare across a group of analysts and is shown to reduce noise and amplify inconsistency that is due to malicious actions. We show that this improves detection performance. Our experiments demonstrate the effectiveness of our approach in detecting malicious insiders. In the experiments, the percentage of malicious insiders grouped with legitimate ones is varied, and results are collected with and without normalization in order to provide a comprehensive analysis of our approach. [ABSTRACT FROM PUBLISHER]