Social media has more than three billion users sharing events, comments, and feelings throughout the world. It serves as a critical information source with large volumes, high velocity, and a wide variety of data. The previous studies on information spreading, relationship analyzing, and individual modeling, etc., have been heavily conducted to explore the tremendous social and commercial values of social media data. This survey studies the previous literature and the existing applications from a practical perspective. We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis, and network analysis. After that, we present the impacts of such applications in three different areas, including disaster management, healthcare, and business. Finally, we list existing challenges and suggest promising future research directions in terms of data privacy, 5G wireless network, and multilingual support.

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Survey on Data Analysis in Social Media: A Practical Application Aspect

Communities in real life are usually dynamic and community structures evolve over time. Detecting community evolution provides insight into the underlying behavior of the network. A growing body of study is devoted in studying the dynamics of communities in evolving social networks. Most of them provide an event-based framework to characterize and track the community evolution. A part of these studies take a step further and provide a predictive model of the events by exploiting community features. However, the proposed models require the community extraction and computing the community features relevant to the time point to be predicted. In this paper, we proposed a new approach for predicting events by estimating feature values related to the communities in a given network. An event-based framework is used to characterize community behavior patterns. Then, a time series ARIMA model is used to predict how particular community features will change in the following time period. Distinct time windows are examined in constituting and analyzing time series. Our proposed approach efficiently tracks similar communities and identifies events over time. Furthermore, community feature values are forecasted with an acceptable error rate. Event prediction using forecasted feature values substantially match up with actual events.

Predicting Community Evolution based on Time Series Modeling

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks. This article proposes a new framework for inference attacks in social networks, which smartly integrates and modifies the existing state-of-the-art convolutional neural network (CNN) models. As a result, the framework can fit wider applicable scenarios for inference attacks no matter whether a user has a legit profile image or not. Moreover, the framework is able to boost the existing high-accuracy CNN for sensitive information prediction. In addition to the framework, the article also shows the detailed configuration of fully connected neural networks (FCNNs) for inference attacks. This part is usually missing in the existing studies. Furthermore, traditional machine learning algorithms are implemented to compare the results from the constructed FCNN. Last but not least, this article also discusses that applying differential privacy (DP) can effectively undermine the accuracy of inference attacks in social networks.

https://par.nsf.gov/servlets/purl/10075125

Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks

Structural network analysis, including node ranking, community detection, and link prediction, has received a lot of attention lately. In the literature, most works focused on the structural analysis of a single network. In this paper, we are particularly interested in how the network structure evolves over time. For this, we propose a general framework to track, model, and predict the dynamic network structures. Unlike some recent works that directly tracks the adjacency matrices of the networks, our framework utilizes the spectral graph theory to track the latent feature vectors obtained by a low-rank eigendecomposition of the Laplacian matrices of the networks. We then use the Finite Impulse Response (FIR) filter to model the evolution of the latent feature vector of each node. By solving a ridge regression problem, the parameters of the FIR filter can be learned and used for predicting the future network structures, including node ranking, community detection, and link prediction. To test the effectiveness of our framework, we perform various experiments based on our synthetic datasets and three real-world datasets. Our experimental results show that our framework is very effective in tracking latent feature vectors and predicting future network structures.

Tracking Network Evolution and Their Applications in Structural Network Analysis

Online social networks (OSN) are one of the most popular forms of modern communication and among the best known is Facebook. Information about the connection between users on the OSN is often very scarce. It is only known if users are connected, while the intensity of the connection is unknown. The aim of the research described was to determine and quantify friendship intensity between OSN users based on analysis of their interaction. We built a mathematical model, which uses: supervised machine learning algorithm Random Forest, experimentally determined importance of communication parameters and coefficients for every interaction parameter based on answers of research conducted through a survey. Taking user opinion into consideration while designing a model for calculation of friendship intensity is a novel approach in opposition to previous researches from literature. Accuracy of the proposed model was verified on the example of determining a better friend in the offered pair.

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Determination of Friendship Intensity between Online Social Network Users Based on Their Interaction

The link prediction problem in social networks defined as a task to predict whether a link between two particular nodes will appear in the future is still a broadly researched topic in the field of social network analysis. However, another relevant problem is solved in the paper instead of individual link forecasting: prediction of key network measures values, what is a more time saving approach. Two machine learning techniques were examined: time series forecasting and classification. Both of them were tested on two real-life social network datasets.

https://www.ii.pwr.edu.pl/~michalski/publications/pub_2012_csna.pdf

D. Krol

Papers

Predicting Social Network Measures Using Machine Learning Approach