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Online social network (OSN) platforms have become a place where the users share their details through various activities. Nowadays, OSNs have major impact not only on the users' lives but also on business and society. Organizations use OSN platforms to reach the target audiences and influential nodes may perform a major role to reach the target audience effectively and efficiently. In this article, a model has been proposed to identify influential nodes for a user in egocentric online social networks by considering the activity behaviors of the user and its network members along with network structure of the user. Finally, the personalities of both the user and the influencers has been analyzed using sentiment analysis and hashtag terms analysis. This article has derived a novel and efficient influence measurement model to evaluate an influence factor of each influential interacted node in the user's network for any of the classified social fields with sentiment types. To achieve that, the influence measurement process has been divided into three different categories namely behavioral influence, structural influence, and collaborative influence which is derived from the first two. Finally, the model for personality analysis has been incorporated.

The coronavirus pandemic has led to a dramatic increase in depression cases worldwide. Several people are utilizing social media to share their depression or suicidal thoughts. Thus, the major goal of the proposed study is to examine Twitter posts by users and identify features that may indicate depressed symptoms among online users. A numerical metric for each user is proposed based on the sentiment value of their tweets, and it is demonstrated that this feature can detect depression with good accuracy by using several machine learning classifiers. The paper proposes a novel method for measuring the mental health index of an individual by combining the sentiment score with multimodal features extracted from his online activities. A real-time curve is generated using this index that can monitor a person's mental health in real time and offer real-time information about his state. The proposed model shows an accuracy of 89% using SVM, and proper feature selection is very essential for obtaining good performance.