Online Social Networks Research Articles

Graph Convolutional Network-Based Rumor Blocking on Social Networks

Misinformation and rumors can spread rapidly and widely through online social networks, seriously endangering social stability. Therefore, rumor blocking on social networks has become a hot research topic. In the existing research, when users receive two opposing opinions, they tend to believe the one arrives first. In this article, we argue that users will dialectically trust the information based on their own opinions rather than the rule of first-come-first-listen. We propose a confidence-based opinion adoption (CBOA) model, which considers the opinion and confidence according to the traditional linear threshold (LT) model. Based on this model, we propose the directed graph convolutional network (DGCN) method to select the <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> most influential positive cascade nodes to suppress the propagation of rumors. Finally, we verify our method on four real network datasets. The experimental results show that our method can sufficiently suppress the propagation of rumors and obtains smaller number of rumor nodes than the baseline algorithms.

Techniques for predicting dark web events focused on the delivery of illicit products and ordered crime

Malicious actors, specially trained professionals operating anonymously on the dark web (DW) platform to conduct cyber fraud, illegal drug supply, online kidnapping orders, CryptoLocker induction, contract hacking, terrorist recruitment portals on the online social network (OSN) platform, and financing are always a possibility in the hyperspace. The amount and variety of unlawful actions are increasing, which has prompted law enforcement (LE) agencies to develop efficient prevention tactics. In the current atmosphere of rapidly expanding cybercrime, conventional crime-solving methods are unable to produce results due to their slowness and inefficiency. The methods for accurately predicting crime before it happens "automated machine" to help police officers ease the burden on personnel while also assisting in preventing offense. To achieve and explain the results of a few cases in which such approaches were applied, we advise combining machine learning (ML) with computer vision (CV) strategies. This study's objective is to present dark web crime statistics and a forecasting model for generating alerts of illegal operations like drug supply, people smuggling, terrorist staffing and radicalization, and deceitful activities that are connected to gangs or organizations showing online presence using ML and CV to help law enforcement organizations identify, and accumulate proactive tactics for solving crimes.

Adversarial Heterogeneous Graph Neural Network for Robust Recommendation

Recommendation systems play a vital role in identifying the hidden interactions between users and items in online social networks. Recently, graph neural networks (GNNs) have exhibited significant performance gains by modeling the information propagation process in graph-structured data for a recommendation. However, existing GNN-based methods do not have broad applicability to heterogeneous graphs that integrate auxiliary data with diverse types. Moreover, graph structures are susceptible to noise and even unnoticed malicious perturbations, as perturbations from connected nodes can create cumulative effects on a target node in the graph. To enhance the robustness and generalization of GNN-based recommendations, we propose a new optimization model named Adversarial Heterogeneous Graph Neural Network for RECommendation (AHGNNRec). First, AHGNNRec learns user and item embeddings by exploring the distinct contributions of various types of interactions between users and items using a hierarchical heterogeneous graph neural network (HGNN). Second, to produce more robust embeddings for recommendations, we employ the adversarial training (AT) method to optimize the HGNN layers. AT is a min-max optimization training process where the generated adversarial fake nodes from normal nodes with intentional perturbations try to maximally deteriorate the recommendation performance. Following this, we learn about these adversarial user or item nodes by minimizing the impact of an additional regularization term for the recommendation. The experimental outcomes on two real-world benchmark datasets demonstrate the effectiveness of AHGNNRec.

A Dynamic Information Dissemination Model Based on User Awareness and Evolutionary Games

In online social networks, users’ awareness of a topic and their personal consciousness can significantly influence the dissemination of information on that topic. Traditional information dissemination research has mainly introduced user awareness as a single factor and has failed to fully consider the impact between users with different awareness. By considering the differences in user awareness and their impact, we proposed a multiple layer spatial information dissemination model based on user awareness. First, for the multidimensional nature of the communication space, we consider differences in users’ cognitive level into consideration, and by setting the corresponding threshold, the users who participate in topic dissemination are divided into two states of high awareness and low awareness. By constructing different communication networks, we depict the propagation dynamics of information at different levels of awareness and introduce an evolutionary game mechanism to analyze the influence between users at different levels of awareness networks. Second, during information dissemination, isolating transmission sources can provide effective control over the spread of information. Based on the susceptible-infectious-recovery (SIR) model in infectious disease dynamics, we consider that susceptible users have a certain chance of being isolated, so we abstract the isolated user group as an independent state Q (quarantine) and introduce it into the traditional SIR model to construct an SQIR information dissemination model. Finally, considering the influence between the information disseminated by high awareness users and low awareness users and the differences in users’ communication behavior at different periods, a dynamic information dissemination model based on user awareness and evolutionary game is obtained through a time-slicing process to dynamically explore the dynamics of information dissemination. The experiments showed that the proposed model could effectively reveal the influence of different levels of awareness on the dynamic information dissemination trend.

Enhance Rumor Controlling Algorithms Based on Boosting and Blocking Users in Social Networks

It is undeniable that rumors abound on online social networks and rumors can cause many disastrous consequences. Effective controlling of rumors is of great significance in social networks. However, the existing research only selects boosting users who are more likely to adopt the truth or select blocking users to terminate the spread of rumors. The former tends to correct the rumor after the spread is over but with high controlling cost, while the latter blocks the rumor without considering the truth transmission. In this article, we focus on how to select boosting&#x2013;blocking users to control rumors when the rumor and truth are spreading together. We propose a boosting-truth blocking-rumor cascade (BTBRC) model. Under this model, given the rumor seed set and truth seed set, the boosting rumor controlling (BRC) problem aims to find a boosting&#x2013;blocking seed set with <inline-formula> <tex-math notation="LaTeX">$k$</tex-math> </inline-formula> users such that the number of users influenced by the truth can be maximized. In order to solve it, we design a multihop neighbor boosting (MHNB) algorithm, which can get effective results with a data-parameter-dependent approximation ratio. Based on the above model, we also propose a positive boosting-truth blocking-rumor cascade (PBTBRC) model and design a connected multihop neighbor boosting (CMHNB) algorithm to solve the connected positive boosting rumor controlling (CPBRC) problem that requires a seed set to be connected under this model. Finally, extensive theoretical analysis and experimental results show the superiority of our algorithms over other comparison methods.

Algorithm-mediated social learning in online social networks.

Human social learning is increasingly occurring on online social platforms, such as Twitter, Facebook, and TikTok. On these platforms, algorithms exploit existing social-learning biases (i.e., towards prestigious, ingroup, moral, and emotional information, or 'PRIME' information) to sustain users' attention and maximize engagement. Here, we synthesize emerging insights into 'algorithm-mediated social learning' and propose a framework that examines its consequences in terms of functional misalignment. We suggest that, when social-learning biases are exploited by algorithms, PRIME information becomes amplified via human-algorithm interactions in the digital social environment in ways that cause social misperceptions and conflict, and spread misinformation. We discuss solutions for reducing functional misalignment, including algorithms promoting bounded diversification and increasing transparency of algorithmic amplification.

Visualizing Collective Idea Generation and Innovation Processes in Social Networks

Collective idea generation and innovation processes are complex and dynamic, involving a large amount of qualitative narrative information that is difficult to monitor, analyze, and visualize using traditional methods. In this study, we developed three new visualization methods for collective idea generation and innovation processes and applied them to data from online social network experiments. The first visualization is the Idea Cloud, which helps monitor collective idea posting activity and intuitively tracks idea clustering and transition. The second visualization is the Idea Geography, which helps understand how the idea space and its utility landscape are structured and how collaboration was performed in that space. The third visualization is the Idea Network, which connects idea dynamics with the social structure of the people who generated them, displaying how social influence among neighbors may have affected collaborative activities and where innovative ideas arose and spread in the social network.

Participation of librarians in online social networks to provide services during the covid-19 pandemic (case study: librarians of tehran public libraries)

Participation of librarians in online social networks to provide services during the covid-19 pandemic (case study: librarians of tehran public libraries)

AN INTEGRATED DEEP LEARNING APPROACH FOR SENTIMENT ANALYSIS ON TWITTER DATA

Analysis of Sentiment (SA) is a computational technique that seeks to extract subjective evaluations, attitudes, and emotional states from online platforms, specifically social media sites like Twitter. The subject has gained significant traction within the research community. The predominant emphasis of traditional sentiment analysis lies in the analysis of textual data. Twitter is widely recognized as a prominent online social networking platform that facilitates microblogging, wherein users share updates pertaining to various subjects through concise messages known as tweets. Twitter is a widely utilized platform that enables individuals to articulate their perspectives and emotions through the medium of tweets. Sentiment analysis refers to the computational methods of classification of given data in text format, categorizing it as either positive, negative, or neutral. The main goal of this study is to use deep learning methods for SA. The purpose is to guess sentiment and then evaluate the results based on accuracy, recall, and f-score. In this paper, a hybrid approach combining Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) techniques, specifically referred to as PSOGA, is proposed to optimize features for a modified neural network (MNN). The final step is to use the K-fold cross-validation method to assess the results. The dataset was obtained through the utilization of the Ruby Twitter API. The ultimate outcome is juxtaposed with the preceding Cuckoo Search (CS) algorithm that had been terminated.

Social Network Mental Disorders Detection

Abstract: With the rise in popularity of online social networks (OSNs), new terms such as Phubbing and Nomophobia have been introduced, and Social network mental illnesses (SNMDs) such as Information Overload and Net Compulsion have been noted Studies show that 1 in 8 Americans experience it suffer from problems internet use, as well as SNMD Overuse, depression, social withdrawal, and other negative behaviors can occur if SNMDs are socially relevant and frequent for them interactive users through online social media. Internet addiction (IAD) is a behavioral disorder, and research on depression in online social networks is increasing unlike most previous attention-grabbing research emphasizes individual behaviors and entries but does not fully explore social network structures and psychological possibilities we propose a learning approach for those that require a thorough examination of OSN topologies

Open Access Original Research Social Sciences (miscellaneous) Social Media Usage Affects Family Communication Among Chinese Adolescence

Purpose: The purpose of this study was to see how social media usage affected family communication among Chinese adolescents. The study's overarching goal was to determine the influence of social networks on family communication among Chinese adolescents. The study aimed to determine how frequently Chinese adolescents used social media and how this might affect their family interactions. Design / Methodology / Approach: The data were obtained from seven Chinese respondents who comprised the sample. In-depth interviews were used as a data collection tool. The data collected were thematically analysed. Main Findings: The findings reveal that Chinese adolescents spend more time interacting with friends and social networks online than speaking with their families. They frequently seek mentoring, advice, and emotional and social support from online friends. According to the study findings, adolescents' personalities significantly impede effective family interaction and communication. Research limitations/implications: The findings show that social media significantly impacts family communication. Future research could also look at the attitudes and behaviours of parents and adult children on social media. Originality/value: This research is unique in that it looks at the Chinese community's family communication patterns on social media. The findings of this study will benefit Chinese society because family communication serves as a crucible for society.

Predicting diversification scores of videos in recommendation network

Recommendation systems are crucial in various platforms from shopping to online social networks. A key challenge in recommendation systems is to leverage diversity, exposing or recommending diverse items to individuals. Despite much effort on studying diversity in the recommendation systems, little work has focused on estimating how much an item will potentially affect user’s diversity experiences by contributing to consecutive recommendations in a session. In this work, we propose a deep learning model that can predict diversification scores, which is a degree of potential contribution to users’ diversity experiences of an item. The proposed model adopts multiple graph neural network layers with a novel attention mechanism that can capture the features of a given item and its related items in terms of recommendation. To prove the effectiveness of our approach, we collect a large dataset of video recommendations from YouTube and conduct random-walk experiments to simulate user traces. The evaluation results on the dataset shows that the proposed model accurately predicts each item’s contribution on user diversity experiences.

Prediction Retweet Using User-Based and Content-Based with Artificial Neural Network-Harmony Search

Online social networking services allow users to post content in the form of text, images or videos. Twitter is a microblogging social networking service that enables its users to send and read text-based messages of up to 140 characters. Retweet is one of the features in Twitter that is important in disseminating information, popular tweets reflect the latest trends on Twitter, the main mechanism that encourages information dissemination is the possibility for users to re-share content posted by their social connections, then it can flow throughout the system. Retweets happen when someone republishes or forwards a post to their homepage and personal profile. Most retweets are credited to the original author of the original post. The retweet prediction system uses an Artificial neural network optimized for Harmony search with tweets about the Jakarta-Bandung Fast Train, which shows the best results when the oversampling method has been carried out with an f1 score of 96.8%.

Going digital: Developing social capital through online social networks in regional SMEs—An Australian study

AbstractIn an increasingly competitive and digitalized business environment, regional SMEs have multiple opportunities to extend their face‐to‐face networking activities to the digital realm. However, there is scant research on regional Australian SMEs use of online social networks (OSNs) to network and develop social capital. This paper follows an interpretive qualitative research approach to identify how 13 regional Australian SMEs from four sparsely populated regional areas use OSNs to enable knowledge sharing and the development of social capital. Our empirical findings classified regional Australian SMEs' use of OSNs into three categories: low, medium, and high, while also identifying OSN use opportunities. We propose four propositions related to OSN adoption and use to share knowledge and develop SC: (i) implementing an OSN adoption and use strategy as part of a larger digital strategy, (ii) investing in and adopting a variety of OSN channels for knowledge sharing, (iii) gradually replacing face‐to‐face knowledge sharing with OSNs, and (iv) controlling the sharing of competitive SME knowledge when using different OSN channels. These findings are significant to encourage regional Australian SMEs to adopt and use OSN to improve innovation opportunities through networking and collaboration in an increasingly digitalized realm.

DeepMUI: A novel method to identify malicious users on online social network platforms

SummaryThe use of online social network (OSN) platforms has become an essential component of contemporary society, facilitating global connectivity, and information sharing among individuals. The proliferation of malicious users has emerged as a noteworthy obstacle, exerting a detrimental effect on the authenticity of the data disseminated through these channels. A malicious profile is created with the intention of disseminating false information, manipulating perspectives, and executing harmful actions, including phishing schemes, identity theft, and the propagation of malware. Consequently, the identification of malicious users has emerged as an essential undertaking for both OSN platforms and researchers. The objective of this study is to investigate the issue of identifying malicious users on OSN platforms. The DeepMUI model has been introduced as a new approach to identifying malicious users on OSN platforms, utilizing user profile metadata‐derived characteristics. The DeepMUI architecture is composed of long short‐term memory and convolutional neural network models. Additionally, it integrates alterations to the pooling layer to improve its overall efficacy. The experiments have demonstrated that DeepMUI exhibits promising results in the task of identifying malicious users, with greater accuracy and minimal loss compared to existing methods.

Emergent local structures in an ecosystem of social bots and humans on Twitter

Bots in online social networks can be used for good or bad but their presence is unavoidable and will increase in the future. To investigate how the interaction networks of bots and humans evolve, we created six social bots on Twitter with AI language models and let them carry out standard user operations. Three different strategies were implemented for the bots: a trend-targeting strategy (TTS), a keywords-targeting strategy (KTS) and a user-targeting strategy (UTS). We examined the interaction patterns such as targeting users, spreading messages, propagating relationships, and engagement. We focused on the emergent local structures or motifs and found that the strategies of the social bots had a significant impact on them. Motifs resulting from interactions with bots following TTS or KTS are simple and show significant overlap, while those resulting from interactions with UTS-governed bots lead to more complex motifs. These findings provide insights into human-bot interaction patterns in online social networks, and can be used to develop more effective bots for beneficial tasks and to combat malicious actors.

The Attitudes of Umm Al-Qura University Students towards Rumors Spreading on Online Social Networks During Covid Pandemic

The current study aimed to explore the attitudes of Umm Al-Qura University students towards rumor spreading on online social networks during the Covid pandemic. The study utilized survey research methodology, which is one of the most important methods in media research and employed a questionnaire to collect the required data. The results revealed that the majority of the sample spent an average of more than three hours per day on social media platforms. The sampled students agreed that social media networks contribute to the propagation of rumors. Data analysis further revealed a common consensus among the study sample that WhatsApp was the most used medium for spreading rumors during the pandemic, and that the most wide-spread rumors were about vaccine efficacy and safety. Furthermore, the study recommended the implementation of social media awareness campaigns suitable for university students and citizens of all ages, focusing on the dangers of rumor spreading and promoting the effective use of social media networks to seek accurate information.

Depolarization of opinions on social networks through random nudges.

Polarization of opinions has been empirically noted in many online social network platforms. Traditional models of opinion dynamics, based on statistical physics principles, do not account for the emergence of polarization and echo chambers in online network platforms. A recently introduced opinion dynamics model that incorporates the homophily factor-the tendency of agents to connect with those holding similar opinions as their own-captures polarization and echo chamber effects. In this work, we provide a nonintrusive framework for mildly nudging agents in an online community to form random connections. This is shown to lead to significant depolarization of opinions and decrease the echo chamber effects. Though a mild nudge effectively avoids polarization, overdoing this leads to another undesirable effect, namely, radicalization. Further, we obtain the optimal nudge probability to avoid the extremes of polarization and radicalization outcomes.

Unveiling the dynamics of emotions in society through an analysis of online social network conversations

Social networks can provide insights into the emotions expressed by a society. However, the dynamic nature of emotions presents a significant challenge for policymakers, politicians, and communication professionals who seek to understand and respond to changes in emotions over time. To address this challenge, this paper investigates the frequency, duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets. The study shows that emotions with lower valence but higher dominance and/or arousal are more prevalent in online social networks. Emotions with higher valence and arousal tend to last longer, while dominant emotions tend to have shorter durations. Emotions occupying the conversations predominantly inhibit others with similar valence and dominance, and higher arousal. Over a month, emotions with similar valences tend to prevail in online social network conversations.

Knowledge Graph Construction for Social Customer Advocacy in Online Customer Engagement

The rise of online social networks has revolutionized the way businesses and consumers interact, creating new opportunities for customer word-of-mouth (WoM) and brand advocacy. Understanding and managing customer advocacy in the online realm has become crucial for businesses aiming to cultivate a positive brand image and engage with their target audience effectively. In this study, we propose a framework that leverages the pre-trained XLNet- (bi-directional long-short term memory) BiLSTM- conditional random field (CRF) architecture to construct a Knowledge Graph (KG) for social customer advocacy in online customer engagement (CE). The XLNet-BiLSTM-CRF model combines the strengths of XLNet, a powerful language representation model, with BiLSTM-CRF, a sequence labeling model commonly used in natural language processing tasks. This architecture effectively captures contextual information and sequential dependencies in CE data. The XLNet-BiLSTM-CRF model is evaluated against several baseline architectures, including variations of BERT integrated with other models, to compare their performance in identifying brand advocates and capturing CE dynamics. Additionally, an ablation study is conducted to analyze the contributions of different components in the model. The evaluation metrics, including accuracy, precision, recall, and F1 score, demonstrate that the XLNet-BiLSTM-CRF model outperforms the baseline architectures, indicating its superior ability to accurately identify brand advocates and label customer advocacy entities. The findings highlight the significance of leveraging pre-trained contextual embeddings, sequential modeling, and sequence labeling techniques in constructing effective models for constructing a KG for customer advocacy in online engagement. The proposed framework contributes to the understanding and management of customer advocacy by facilitating meaningful customer-brand interactions and fostering brand loyalty.