How to Calculate the Public Psychological Pressure in the Social Networks

With the worldwide application of social networks, new mathematical approaches have been developed that quantitatively address this online trend, including the concept of social computing. The analysis of data generated by social networks has become a new field of research; social conflicts on social networks occur frequently on the internet, and data regarding social behavior on social networks must be analyzed objectively. This type of social computing method can solve a series of complex social computing problems including the calculation of public psychological pressure. The quantitative calculation of public psychological pressure is so important to the public opinion analysis that it can be widely applied in a lot of public information analysis fields

Data-driven design of intelligent wireless networks: an overview and tutorial

Data science or "data-driven research" is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves

Bloggers Behavior and Emergent Communities in Blog Space

Interactions between users in cyberspace may lead to phenomena different from those observed in common social networks. Here we analyse large data sets about users and Blogs which they write and comment, mapped onto a bipartite graph. In such enlarged Blog space we trace user activity over time, which results in robust temporal patterns of user--Blog behavior and the emergence of communities. With the spectral methods applied to the projection on weighted user network we detect clusters of users related to their common interests and habits. Our results suggest that different mechanisms may play the role in the case of very popular Blogs. Our analysis makes a suitable basis for theoretical modeling of the evolution of cyber communities and for practical study of the data, in particular for an efficient search of interesting Blog clusters and further retrieval of their contents by text analysis

Psychosocial Determinants Of Using Online Social Networks: An Application Of The Theory Of Planned Behavior

The use of online social networking continues to increase among Americans, yet there is little research related to understanding of the behavior using online social networks. This study aimed to understand the underlying beliefs, evaluations, attitudes, norms, and perceptions behind the intention to log onto online social networks. The Theory of Planned Behavior was applied to the behavioral intention to log onto Facebook once a day for the next three months (n = 269). Regression analysis predicting intention from global constructs of the Theory of Planned Behavior yielded a multiple correlation of 0.62 with attitude (β = 0.32, p < 0.01), subjective norm (β = 0.41, p < 0.01), and perceived behavioral control (β = 0.08, ns). Salient consequences related to stronger intention to log onto Facebook once a day for the next three months included the behavioral beliefs of staying in touch, increasing social network, and sharing interests with others. Salient referents that were significantly correlated with intention to log onto Facebook once a day for the next three months included friends, other students, boyfriend/girlfriend/spouse, family, professors, and future employers. Implications for understanding the intention behind the use of online social networks will be discussed in regards to the salient referents and consequences of logging onto Facebook once a day for the next three months.Submitted to the faculty of the University Graduate School in partial fulfillment of the requirements for the degree Master of Public Health in the Department of Applied Health Science of the School of HPER, Indiana University December 200

Preserving Differential Privacy in Convolutional Deep Belief Networks

The remarkable development of deep learning in medicine and healthcare domain presents obvious privacy issues, when deep neural networks are built on users' personal and highly sensitive data, e.g., clinical records, user profiles, biomedical images, etc. However, only a few scientific studies on preserving privacy in deep learning have been conducted. In this paper, we focus on developing a private convolutional deep belief network (pCDBN), which essentially is a convolutional deep belief network (CDBN) under differential privacy. Our main idea of enforcing epsilon-differential privacy is to leverage the functional mechanism to perturb the energy-based objective functions of traditional CDBNs, rather than their results. One key contribution of this work is that we propose the use of Chebyshev expansion to derive the approximate polynomial representation of objective functions. Our theoretical analysis shows that we can further derive the sensitivity and error bounds of the approximate polynomial representation. As a result, preserving differential privacy in CDBNs is feasible. We applied our model in a health social network, i.e., YesiWell data, and in a handwriting digit dataset, i.e., MNIST data, for human behavior prediction, human behavior classification, and handwriting digit recognition tasks. Theoretical analysis and rigorous experimental evaluations show that the pCDBN is highly effective. It significantly outperforms existing solutions

Analysis of the Twitter Interactions during the Impeachment of Brazilian President

The impeachment process that took place in Brazil on April, 2016, generated a large amount of posts on Internet Social Networks. These posts came from ordinary people, journalists, traditional and independent media, politicians and supporters. Interactions among users, by sharing news or opinions, can show the dynamics of communication inter and intra groups. This paper proposes a method for social networks interactions analysis by using motifs, frequent interactions patterns in network. This method is then applied to analyze data extracted from Twitter during the voting for the impeachment of the Brazilian president. Results of this analysis highlight the behavior of some users by retweeting each other to increase the importance of their opinion or to reach visibility. In addition, interaction patterns reveal that messages from one group (against/in favor of impeachment) rarely propagate to the opposing group. As such, this brings evidence that Social Networks may not stimulate a debate, but reaffirm users’ beliefs

Clustering Memes in Social Media

The increasing pervasiveness of social media creates new opportunities to study human social behavior, while challenging our capability to analyze their massive data streams. One of the emerging tasks is to distinguish between different kinds of activities, for example engineered misinformation campaigns versus spontaneous communication. Such detection problems require a formal definition of meme, or unit of information that can spread from person to person through the social network. Once a meme is identified, supervised learning methods can be applied to classify different types of communication. The appropriate granularity of a meme, however, is hardly captured from existing entities such as tags and keywords. Here we present a framework for the novel task of detecting memes by clustering messages from large streams of social data. We evaluate various similarity measures that leverage content, metadata, network features, and their combinations. We also explore the idea of pre-clustering on the basis of existing entities. A systematic evaluation is carried out using a manually curated dataset as ground truth. Our analysis shows that pre-clustering and a combination of heterogeneous features yield the best trade-off between number of clusters and their quality, demonstrating that a simple combination based on pairwise maximization of similarity is as effective as a non-trivial optimization of parameters. Our approach is fully automatic, unsupervised, and scalable for real-time detection of memes in streaming data.Comment: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM'13), 201