Online Social Network Data Research Articles

Suicidal Thought Detection using Max Voting Ensemble Technique

Online social networking (SN) data presents a data stream that is rich in context and temporal information. It holds promise for predicting suicidal thoughts and behaviors. The fusion of SN data with machine learning algorithms offers a potential path forward. This research proposes a Max Voting Ensemble classifier model applied to a Reddit dataset for the identification of suicidal ideation. The preprocessing involves data cleansing, tokenization, and lemmatization. Additionally, TF-IDF and Word2Vec word embedding techniques are applied. Diverse machine learning algorithms, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), Multinomial Naive Bayes (MNB), AdaBoost, and XGBoost, are implemented. The results of selected Machine Learning Classifiers (MLCs) are amalgamated using a Max Voting Ensemble classifier. The research findings clearly indicate that the Max Voting Ensemble classifier yields improved precision of 91.39% coupled with a substantial accuracy of 87.5%. The application of Ensembling Techniques (ET) to SN data holds the potential to address the complexities and modeling challenges inherent in predicting acute Suicidal Ideation within these dynamic time scales.

Security and privacy concerns in social networks mathematically modified metaheuristic-based approach

Potential risks and vulnerabilities in Social Networks (SN) that may threaten the confidentiality, accuracy, and accessibility of data provided by users have been identified as safety risks. Unauthorized usage of the system, breaches of data, privacy violations, cyber-attacks, and other malicious behaviors that negatively impact the SN’s reliability and security may be addressed within these. Innovative techniques that use scientifically enhanced metaheuristic algorithms are the subject of the study, which attempts to address evolving issues of security and privacy in SNs. In order to improve the security of online social networking data encryption, researchers recommend implementing the MMCSOA approach, which is the Mathematically Modified Cuckoo Search Optimization Algorithm. An enhanced tuning of elliptic curve parameters can be accomplished by adapting the Cuckoo Search method, which, in consequence, provides an encrypted environment for multi-party computing. Users’ preferences for confidentiality are considered while applying a fuzzy logic (FL) decision matrix to select individuals. Data security is improved, and users receive additional authority over data shared with MMCSOA. Research findings prove that MMCSOA is higher compared to other methods in securing users’ privacy.

A Hybrid Probabilistic Privacy Preserving Based Community Detection Model on Online Social Networking Data

Privacy preserving plays a vital role on the online social networking sites due to high dimensionality and data size. Community detection is used to find the social relationships among the node edges and links. However, most of the conventional models are difficult to process the community structure detection due to high computational time and memory. Also, these models require contextual weighted nodes information for privacy preserving process. In order to overcome these issues, an advanced probabilistic weighted based community detection and privacy preserving framework is developed on the large social networking data. In this model, a filter based probabilistic model is developed to remove the sparse values and to find the weighted community detection nodes and its profiles for privacy preserving process. Experimental results show that the filter based probabilistic community detection framework has better efficiency in terms of normalized mutual information, Q, rand index and runtime (ms).

Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Suicide and self-harm prediction based on social media data using machine learning algorithms

Online social networking (SN) data is a context and time rich data stream that has showed potential for predicting suicidal ideation and behaviour. Despite the obvious benefits of this digital media, predictive modelling of acute suicidal ideation (SI) remains underdeveloped at now. In combined with robust machine learning algorithms, social networking data may provide a potential path ahead. Researchers applied a machine learning models to a previously published Instagram dataset of youths. Using predictors that reflect language use and activity inside this social networking, researchers compared the performance of the out-of-sample, cross-validated model to that of earlier efforts and used a model explanation to further investigate relative predictor relevance and subject-level phenomenology. The application of ensemble learning approaches to SN data for the prediction of acute SI may reduce the complications and modelling issues associated with acute SI at these time scales. Future research is required on bigger, more diversified populations to refine digital biomarkers and assess their external validity with more rigor

APSN: Adversarial Pseudo-Siamese Network for Fake News Stance Detection

Fake news is a longstanding issue that has existed on the social network, whose negative impact has been increasingly recognized since the US presidential election. During the election, numerous fake news about the candidates distributes vastly in the online social networks. Identifying inauthentic news quickly is an essential purpose for this research to enhance the trustworthiness of news in online social networks, which will be the task studied in this paper. The fake news stance detection can contribute to detect a startling amount of fake news, which aims at evaluating the relevance between the headline and text bodies. There exists a significant difference between news article headline and text body, since headlines with several key phrases are usually much shorter than the text bodies. Such an information imbalance challenge may cause serious problems for the stance detection task. Furthermore, news article data in online social networks is usually exposed to various types of noise and can be contaminated, which poses more challenges for the stance detection task. In this paper, we propose a novel fake news stance detection model, namely Adversarial Pseudo-Siamese Network model (APSN), to solve these challenges. With coupled input components with imbalanced parameters, APSN can learn and compute feature vectors and similarity score of news article headlines and text bodies simultaneously. In addition, by adopting adversarial setting, besides the regular training set, a set of noisy training instances will be generated and fed to APSN in the learning process, which can significantly enhance the robustness of the model. Extensive experiments have been conducted on a real-world fake news dataset, and the experimental results reveal that the presented model exceeds compared suspicious information detection models with significant advantages.

Enhanced Clustering Based OSN Privacy Preservation to Ensure k-Anonymity, t-Closeness, l-Diversity, and Balanced Privacy Utility

Online Social Networks (OSN) sites allow end-users to share a great deal of information, which may also contain sensitive information, that may be subject to commercial or non-commercial privacy attacks. As a result, guaranteeing various levels of privacy is critical while publishing data by OSNs. The clustering-based solutions proved an effective mechanism to achieve the privacy notions in OSNs. But fixed clustering limits the performance and scalability. Data utility degrades with increased privacy, so balancing the privacy utility trade-off is an open research issue. The research has proposed a novel privacy preservation model using the enhanced clustering mechanism to overcome this issue. The proposed model includes phases like pre-processing, enhanced clustering, and ensuring privacy preservation. The enhanced clustering algorithm is the second phase where authors modified the existing fixed k-means clustering using the threshold approach. The threshold value is determined based on the supplied OSN data of edges, nodes, and user attributes. Clusters are k-anonymized with multiple graph properties by a novel one-pass algorithm. After achieving the k-anonymity of clusters, optimization was performed to achieve all privacy models, such as k-anonymity, t-closeness, and l-diversity. The proposed privacy framework achieves privacy of all three network components, i.e., link, node, and user attributes, with improved utility. The authors compare the proposed technique to underlying methods using OSN Yelp and Facebook datasets. The proposed approach outperformed the underlying state of art methods for Degree of Anonymization, computational efficiency, and information loss.

An Approach for Fake News Detection in Social Media Using Hybrid Classifier

The users of social networks get ruined by fake news, which brings a vast effect on the offline community. A very significant aim is to improve the trust of data in online social networks by discovering fake news in an appropriately timed manner. A novel technique for identifying fake news on social media has been developed as a result. The feature extraction module receives the preprocessed output after the initial preprocessing stage on the news story. The extracted features are then analyzed using the generative adversarial network (GAN) and neural network (NN) to discover fake scores. Here, the rider shuffled shepherd optimization (RSSO), which was developed by integrating the rider optimization algorithm and the shuffled shepherd optimization algorithm, is used to train the NN classifier. Data labeling is considered an expensive task. GANs are unsupervised, so labeling of data is not required to train them. The score obtained from GAN and NN are utilized for computing the final score, which is evaluated using the weighted average model for detecting fake news. With the highest accuracy of 91.2%, the highest sensitivity of 92.1%, and the highest specificity of 88.8%, the proposed RSSO-based NN + GAN provided improved performance.

How Social Capital is Related to Migration Between Communities?

In addition to economic and infrastructural factors, social connections of people also influence migration patterns. This influence can be attributed to the resources that are made available by social contacts: social capital, which can also be utilized in the process of migration. Based on previous literature, we identify three different aspects of social capital and test their relationship with domestic migration simultaneously. First, we analyse if the intensity of connections within communities (local social capital) restrains from migration. Second, if the intensity of connections between two communities (bridging social capital) is associated with increased migration between them. Finally, we consider, if the extent to which local community networks exhibit open or closed structures (bonding social capital) contributes to higher or lower migration rates. We create indicators for these measures using archived online social network data, covering 40% of the adult population of Hungary, and combine them with official migration data of 175 subregions. Based on point-to-point gravity and negative binomial models, we find that bridging social capital between subregions is associated with increased migration flows, but we do not find that local social capital restrains from migration.

Public perception of electric vehicles on reddit over the past decade

Understanding public perception of electric vehicles (EVs) is imperative for increasing EV adoption, which can significantly reduce greenhouse gas (GHG) emissions, thereby mitigating climate change and global warming. While most existing research characterizes public perception of EV in the context of surveys, questionnaires, or interviews, our study leverages Reddit online social network (OSN) data to capture EV public perception at a much larger scale. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) between January 2011 and December 2020 relevant to EVs and analyzed them along several axes to understand how EVs are perceived by the public on Reddit through the following research questions: (1) What EV-related topics have been discussed by Reddit users? Whether/how Reddit users’ interest in different topics has changed during 2011–2020? (2) What sentiment do Reddit users hold towards EVs? Whether public sentiment on Reddit has shifted over the past 10 years? (3) Whether/how do various Reddit communities (i.e., subreddits) have different perceptions of EVs? Our analysis evinces the potential of utilizing a large-scale OSN dataset for demonstrating a much wider spectrum of topics that the public is interested in than previous studies show, reveals fringe communities including r/conspiracy have many (controversial) discussions on EVs, especially on the environmental impacts of EVs, and one political community (r/The_Donald) has similar patterns with fringe communities in both sentiment and topic aspects. By answering these research questions, we aim to develop a more comprehensive understanding of the public perception of EVs in the past decade.

Days with and without self-injurious thoughts and behaviors: Impact of childhood maltreatment on adolescent online social networking.

Among adolescents, an increasing rate of interpersonal interactions occurs online. Previous research has shown that interpersonal context exerts a robust impact on suicidal thoughts or behaviors, yet little attention has focused on examining the content of online interactions surrounding self-injurious thoughts or behaviors. As such, the present study sought to compare online social networking behaviors among adolescents on days with and without experiencing self-injurious thoughts or behaviors, as influenced by childhood maltreatment history. Adolescents aged 13-18 hospitalized for self-injurious thoughts or behaviors were recruited as part of an ongoing longitudinal study. A subsample (N = 22) of adolescents provided data from their online social networking platforms (i.e., text messages, Facebook, Instagram, and Twitter). Using a mixed-methods approach, online social networking data on days of experiencing self-injurious thoughts or behaviors and days of not experiencing self-injurious thoughts or behaviors were compared. Results indicate the frequency and content of online social networking messaging do not change by day of self-injurious thoughts or behaviors or history of childhood maltreatment. However, childhood maltreatment predicts received conflictual messages as well as sent symptomatic messages on days of experiencing self-injurious thoughts or behaviors. Childhood maltreatment may play a role in the content of adolescent online behaviors, particularly on days when they experience self-injurious thoughts or behaviors. Implications for intervention are discussed.

Data anonymization to balance privacy and utility of online social media network data

There are tremendous users of social media networks, and they keep growing day by day. In recent years, social media networks have sparked widespread interest among the general public because they offer a simple and appealing form of communication. Users’ data is at risk due to their expanding contact with social networks, necessitating securing their privacy. Users communicate with each other on social media and share information that is vital and private. This user’s information is on the tip of attraction, as many third-party ethical users use it for good causes like increasing the new customers by analyzing their needs. Still, unethical users misuse it for destructive purposes like stealing data, burglary, or personification. And hence the data owner’s task is to preserve the privacy of information. Before publishing such information in the public domain, it needs to be anonymized so that a third party cannot misuse it. Again balancing privacy and utility also need to be focused. The proposed improved Mondrian algorithm anonymizes data by partitioning and enhances privacy and utility balance on the Adult dataset same as with measures PIRL, NCP and DP.

An analysis of twitter as a relevant human mobility proxy

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatio-temporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

Artificial Intelligence-Enabled Cyberbullying-Free Online Social Networks in Smart Cities

In recent years, smart city services have moved the existence of people from the physical to the virtual world (cyberspace), e.g., online banking, e-commerce, telemedicine, etc. Along with the benefits of smart cities, the problems of the physical world are also moved to the cyber world, like cyberbullying in online social networks (OSN). Automated cyberbullying detection techniques need to be designed to remove the potential tragedies in OSNs. The recent advent of artificial intelligence (AI) models like machine learning and deep learning (DL) models can be employed for the detection of cyberbullying in the OSN. With this motivation, this paper develops an AI-enabled cyberbullying-free OSN (AICBF-ONS) technique in smart cities. The proposed AICBF-ONS technique involves chaotic salp swarm optimization (CSSO)-based feature selection technique to derive a useful set of features from the OSN data. In addition, stacked autoencoder model is used as a classification model to allocate appropriate class labels of the OSN data. To improve the detection performance of the SAE model, a parameter tuning process take place using the mayfly optimization (MFO) algorithm. An extensive experimental analysis ensured the supremacy of the proposed AICBF-ONS technique.

Информационно-аналитическая система поддержки управления региональным развитием на основе открытых больших данных социальных медиа: концепция разработки и практика реализации

The paper considers the possibilities of using big open data of online social networks for information and analytical support of regional development. The concepts of social objects and processes are introduced, which are associated with the socio-economic sphere of the region and reflected in the data presented in social networks. An overview of modern tools and technologies for analyzing data of social networks for solving state, municipal and business problems is given.The potential for expanding the functionality of the considered systems based on the use of formative artificial intelligence is noted. The experience of forming the necessary system infrastructure and developing components of a software framework for working with big data of social networks used for information and analytical support of regional development, is presented on the example of the Murmansk region.

Diversity Preference-Aware Link Recommendation for Online Social Networks

Link recommendation, which recommends links to connect unlinked online social network users, is a fundamental social network analytics problem with ample business implications. Existing link recommendation methods tend to recommend similar friends to a user but overlook the user's diversity preference, although social psychology theories suggest the criticality of diversity preference to link recommendation performance. In recommender systems, a field related to link recommendation, a number of diversification methods have been proposed to improve the diversity of recommended items. Nevertheless, diversity preference is distinct from diversity studied by diversification methods. To address these research gaps, we define and operationalize the concept of diversity preference for link recommendation and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then analyze key properties of the new link recommendation problem and develop a novel link recommendation method to solve the problem. Using two large-scale online social network data sets, we conduct extensive empirical evaluations to demonstrate the superior performance of our method over representative diversification methods adapted for link recommendation as well as state-of-the-art link recommendation methods.

A privacy preservation method for multiple-source unstructured data in online social networks

Significant amounts of data are generated in different formats (e.g., text, audio, and video) in online social networks and from other online sources, which can be mined to facilitate decision-making in different applications. However, it is nontrivial to preserve user and/or data privacy when we are analyzing data from multiple sources, with different characteristics. In this paper, we propose a method to preserve privacy when dealing with data obtained from multiple/disparate sources. Taking images and corresponding text descriptions as examples, we set up different methods and protection scenarios to protect the private information in the data. Our proposed method is also designed to maintain the availability of existing information and links between the two forms to some extent. We then quantitatively analyze the performance of the proposed approach.

Privacy Preservation in Online Social Networks Using Multiple-Graph-Properties-Based Clustering to Ensure k-Anonymity, l-Diversity, and t-Closeness

As per recent progress, online social network (OSN) users have grown tremendously worldwide, especially in the wake of the COVID-19 pandemic. Today, OSNs have become a core part of many people’s daily lifestyles. Therefore, increasing dependency on OSNs encourages privacy requirements to protect users from malicious sources. OSNs contain sensitive information about each end user that intruders may try to leak for commercial or non-commercial purposes. Therefore, ensuring different levels of privacy is a vital requirement for OSNs. Various privacy preservation methods have been introduced recently at the user and network levels, but ensuring k-anonymity and higher privacy model requirements such as l-diversity and t-closeness in OSNs is still a research challenge. This study proposes a novel method that effectively anonymizes OSNs using multiple-graph-properties-based clustering. The clustering method introduces the goal of achieving privacy of edge, node, and user attributes in the OSN graph. This clustering approach proposes to ensure k-anonymity, l-diversity, and t-closeness in each cluster of the proposed model. We first design the data normalization algorithm to preprocess and enhance the quality of raw OSN data. Then, we divide the OSN data into different clusters using multiple graph properties to satisfy the k-anonymization. Furthermore, the clusters ensure improved k-anonymization by a novel one-pass anonymization algorithm to address l-diversity and t-closeness privacy requirements. We evaluate the performance of the proposed method with state-of-the-art methods using a “Yelp real-world dataset”. The proposed method ensures high-level privacy preservation compared to state-of-the-art methods using privacy metrics such as anonymization degree, information loss, and execution time.

Differential Private Social Network Publication and Persistent Homology Preservation

Online social networks (OSNs) service providers share and analyze users’ data for better performance. Hence, they are responsible for the protection of sensitive information. Previous researchers proposed several anonymization techniques for OSN data. Some anonymization techniques based on differential privacy claim to preserve privacy and graph utility under certain graph metrics. However, these graph utility metrics can partially describe the whole graph. In this paper, we employ persistent homology to have a comprehensive description of the OSN graph utility. The proposed scheme novelly preserves the persistent structures and differential privacy. In the proposed scheme, we employ the adjacency matrix model as the graph abstraction model. To strengthen privacy protection, we add exponential noise to the adjacency matrix. We then find the number of adding/deleting edges under the guidance of differential privacy. To preserve persistent homology, we collect edges along with the persistent structures. The proposed scheme carefully perturbs the edges while preserving those structures. Two anonymization sub-schemes, PHDP and rPHDP, have been proposed to balance the computation complexity and utility preservation. The evaluation results show that the proposed scheme outperforms other traditional anonymization schemes under graph utility metrics and graph application metrics.

Construction of online social network data mining model based on blockchain

Construction of online social network data mining model based on blockchain