Abstract-We study emotion influence in large image social networks. We focus on users' emotions reflected by images that they have uploaded and social influence that plays a role in changing users' emotions. We first verify the existence of emotion influence in the image networks, and then propose a probabilistic factor graph based emotion influence model to answer the questions of "who influences whom". Employing a real network from Flickr as the basis in our empirical study, we evaluate the effectiveness of different factors in the proposed model with in-depth data analysis. The learned influence is fundamental for social network analysis and can be applied to many applications. We consider using the influence to help predict users' emotions and our experiments can significantly improve the prediction accuracy (3.0%-26.2%) over several alternative methods such as Naive Bayesian, SVM (Support Vector Machine) or traditional Graph Model. We further examine the behavior of the emotion influence model, and find that more social interactions correlate with higher emotion influence between two users, and the influence of negative emotions is stronger than positive ones.
Extracting emotions from images has attracted much interest, in particular with the rapid development of social networks. The emotional impact is very important for understanding the intrinsic meanings of images. Despite many studies having been done, most existing methods focus on image content, but ignore the emotion of the user who published the image. One interesting question is: How does social effect correlate with the emotion expressed in an image? Specifically, can we leverage friends interactions (e.g., discussions) related to an image to help extract the emotions? In this paper, we formally formalize the problem and propose a novel emotion learning method by jointly modeling images posted by social users and comments added by their friends. One advantage of the model is that it can distinguish those comments that are closely related to the emotion expression for an image from the other irrelevant ones. Experiments on an open Flickr dataset show that the proposed model can significantly improve (+37.4% by F1) the accuracy for inferring user emotions. More interestingly, we found that half of the improvements are due to interactions between 1.0% of the closest friends.
Psychological theories suggest that emotion represents the state of mind and instinctive responses of one’s cognitive system (Cannon 1927). Emotions are a complex state of feeling that results in physical and psychological changes that influence our behavior. In this paper, we study an interesting problem of emotion contagion in social networks. In particular, by employing an image social network (Flickr) as the basis of our study, we try to unveil how users’ emotional statuses influence each other and how users’ positions in the social network affect their influential strength on emotion. We develop a probabilistic framework to formalize the problem into a role-aware contagion model. The model is able to predict users’ emotional statuses based on their historical emotional statuses and social structures. Experiments on a large Flickr dataset show that the proposed model significantly outperforms (+31% in terms of F1-score) several alternative methods in predicting users’ emotional status. We also discover several intriguing phenomena. For example, the probability that a user feels happy is roughly linear to the number of friends who are also happy; but taking a closer look, the happiness probability is superlinear to the number of happy friends who act as opinion leaders (Page et al. 1999) in the network and sublinear in the number of happy friends who span structural holes (Burt 2001). This offers a new opportunity to understand the underlying mechanism of emotional contagion in online social networks.
As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods. Electronic supplementary materialThe online version of this article
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