Online Social Networks Research Articles

Unmasking deceptive profiles: A deep dive into fake account detection on instagram and twitter

The rise of online social networks, also known as OSNs, has captured the attention of younger generations and made them an integral part of social life. As a result, the use of various social media platforms has increased significantly, greatly impacting individuals' social connections. These platforms offer a wide range of features, such as news distribution, contributing to their widespread use. However, with the rapid growth of social media, the prevalence of fake accounts has become a major problem, posing a threat to both the security of users and the integrity of these platforms. In response, this article explores the effectiveness of machine learning algorithms (ML). Detect and identify fraudulent accounts on popular social media platforms, especially Instagram and Twitter. Our methodology involves analyzing user activity and account information to develop fine-tuned machine-learning models. Our approach takes into account important parameters such as number of followers, number of posts, and engagement.

Impact of Electronic Word of Mouth on Purchase Intention: Mediating Role of Brand Trust and Product Involvement

Word-of-mouth (WOM) has been recognized as one of the most influential tools of transmitting the information. With the technology-oriented advancements and the emergence of various online social networking sites (SNS) like Face book, Snapchat, Instagram, YouTube, LinkedIn, and Twitter, the concept of word of mouth has been revived to the electronic word-of-mouth. This spectacle has an enormous influence on the decisions of the consumers they make while purchasing a product or brand. Drawing on the theory of planned behavior, this study examined the influence of electronic word-of-mouth on purchase intention with the explanatory role of the brand trust and product involvement. In this quantitative study the empirical data has been collected through survey questionnaires. For this research the target population was the students of different universities of Lahore who use social networking platforms to read the online reviews and recommendations. Measurement items were adopted from previous studies in marketing literature. To test hypotheses, linear and hierarchal regression techniques have been applied. All the tests on the data to infer results were performed through SPSS version 22.0. The results indicate that product involvement and brand trust positively and partially mediate the relationship between electronic word-of-mouth and purchase intention. This paper endorsed the importance of electronic word-of-mouth, brand trust, product involvement, and this information will assist the marketers in making the efficient and low budgeted marketing strategies in future to boost up the purchase intention of the consumers that will ultimately leads to earn higher profits. The theoretical and practical implications are also discussed.

A bitwise approach on influence overload problem

Increasingly developing online social networks has enabled users to send or receive information very fast. However, due to the availability of an excessive amount of data in today’s society, managing the information has become very cumbersome, which may lead to the problem of information overload. This highly eminent problem, where the existence of too much relevant information available becomes a hindrance rather than a help, may cause losses, delays, and hardships in making decisions. Thus, in this paper, by defining information overload from a different aspect, we aim to maximize the information propagation while minimizing the information overload (duplication). To do so, we theoretically present the lower and upper bounds for the information overload using a bitwise-based approach as the leverage to mitigate the computation complexities and obtain an approximation ratio of 1−1e. We propose two main algorithms, B-square and C-square, and compare them with the existing algorithms. Experiments on two types of datasets, synthetic and real-world networks, verify the effectiveness and efficiency of the proposed approach in addressing the problem.

Consciousness of undergraduate students in Nigerian universities about security risks of online social networking

This study was designed to examine the consciousness of undergraduate students in Nigerian universities about the security risks of online social networking. Data was collected from 336 students using Qualtrics, an online survey tool. To a large extent, 53.4% of the respondents reported concerns about the specific data that other social media users could possess about them due to their participation in social networking. As high as 72.6% reported that they are aware of pretenders in the OSN; they are very vigilant in adding people to their friends’ list and seriously consider security (75.9%) before posting their photos to avoid exploitation. Indices of the security consciousness are not as high. Except for social media experience (p=0.112) and past privacy invasion (p=0.209), the rest of the variables explained the security risk consciousness of the students.

Extracting the diffusion dynamics of crisis information on online social networks: Model and application

Disaster mitigation greatly benefits from the diffusion of crisis information. To learn about the diffusion dynamics of crisis information on online social networks, this paper puts forward a crisis information diffusion model based on population dynamics and further analyses its parameters, such as ordinary users’ average number of followers, the number of potential audience members who might spread the crisis information, the forwarding probability for each audience member and the attenuation speed of the information based on real data from Sina Weibo. In addition, a method for the prediction of crisis information diffusion is proposed and verified on the basis of this model. The results show that this model could describe and predict well the diffusion process of crisis information on online social networks, enabling relevant departments to develop appropriate strategies for crisis information diffusion.

Review of the Association between Social Networks and Adolescent Development

Adolescents are consistently exposed to social networks. Considerable discourse has taken place within both the public sphere and the scientific realm regarding the correlation between social network usage during adolescence and the manifestation of both physical and mental health problems. The main object of this study was to do a thorough and systematic evaluation of the available literature that investigates the association between the well-being of teenagers and their social networks. The review incorporated a total of eleven articles. Adolescents who participate in offline social networks demonstrate an elevated degree of impact on their emotional state and feelings of isolation, although their perception of their physical appearance stays unaltered. Receiving affirmative comments and actively seeking social support can elicit a sense of contentment as well as a sense of isolation. The results from a cumulative total of seven studies continuously indicate a notable correlation between the use of online social networks and several dimensions of mental health, including mood, self-esteem, life satisfaction, body image, and overall subjective well-being. The implementation of intervention tactics and educational activities is of utmost importance in effectively addressing the possible risks to subjective well-being that are linked to online social networks among young persons, educators, and parents.

Demographic-Based Public Perception Analysis of Electric Vehicles on Online Social Networks

Electric vehicles have gained significant popularity in the market, with sales increasing yearly. The introduction of new policies and reforms aimed at promoting environmental sustainability, coupled with the release of more advanced electric vehicles with higher driving ranges and technical specifications, has encouraged more people to consider switching to electric vehicles. However, there is still a lack of understanding of public perception and the factors influencing the decision to switch to electric vehicles, especially among people from different demographic groups. In this study, we leverage machine learning techniques to analyze public opinion about electric vehicles across different demographic groups on two online social networks (OSNs), namely Reddit and Twitter. Our analyses provide valuable insights into how users on these platforms perceive electric vehicles and the factors that influence their perception. This information can be used to inform market strategies and future policies aimed at promoting the adoption of electric vehicles.

Social Normativity in the Transition of New Media: the Case of Facebook in Vietnam

In recent times, with the prominent emergence of social networks, online cultural and behavioural standards are gradually formed and re-formed in the online sphere, raising the demand to investigate the rules and the development of online social behaviours, as well as the approaches to foster its healthy practice towards cyber civilization. In Vietnam, Facebook is the most popular social media platform, posing as an important space for online discussions and social relations in Vietnam’s cyberspace. Therefore, this study aims to examine how normativity and netiquette are reflected on Facebook, how groups’ subculture on Facebook empowers the conformity of users’ behaviour, and the current situation of ‘Netiquette’ and related rules and regulations in Vietnam. The research seeks to understand how social norms are tested and reformed in online social networks, ultimately impacting beliefs and actions both online and offline. Based on the findings, this study proposes additional approaches to promote a behavioural culture and establish standardized user behaviour on social networks.
 Keywords: New Media, Facebook, Social normativity, Netiquette.

Cybersecurity discussions in Stack Overflow: a developer-centred analysis of engagement and self-disclosure behaviour

Stack Overflow (SO) is a popular platform among developers seeking advice on various software-related topics, including privacy and security. As for many knowledge-sharing websites, the value of SO depends largely on users’ engagement, namely their willingness to answer, comment or post technical questions. Still, many of these questions (including cybersecurity-related ones) remain unanswered, putting the site’s relevance and reputation into jeopardy. Hence, it is important to understand users’ participation in privacy and security discussions to promote engagement and foster the exchange of such expertise. Objective: Based on prior findings on online social networks, this work elaborates on the interplay between users’ engagement and their privacy practices in SO. Particularly, it analyses developers’ self-disclosure behaviour regarding profile visibility and their involvement in discussions related to privacy and security. Method: We followed a mixed-methods approach by (i) analysing SO data from 1239 cybersecurity-tagged questions along with 7048 user profiles, and (ii) conducting an anonymous online survey (N=64). Results: About 33% of the questions we retrieved had no answer, whereas more than 50% had no accepted answer. We observed that proactive users tend to disclose significantly less information in their profiles than reactive and unengaged ones. However, no correlations were found between these engagement categories and privacy-related constructs such as perceived control or general privacy concerns. Implications: These findings contribute to (i) a better understanding of developers’ engagement towards privacy and security topics, and (ii) to shape strategies promoting the exchange of cybersecurity expertise in SO.

Non-conservative diffusion and its application to social network analysis

Abstract Is the random walk appropriate for modelling and analysing social processes? We argue that many interesting social phenomena, including epidemics and information diffusion, cannot be modelled as a random walk, but instead must be modelled as broadcast-based or non-conservative diffusion. To produce meaningful results, social network analysis algorithms have to take into account differences between these diffusion processes. We formulate conservative (random walk-based) and non-conservative (broadcast-based) diffusion mathematically and show how these are related to well-known metrics: PageRank and Alpha-Centrality, respectively. This formulation allows us to unify two distinct areas of network analysis–centrality and epidemic models–and leads to insights into the relationship between diffusion and network structure, specifically, the existence of an epidemic threshold in non-conservative diffusion. We demonstrate, by ranking nodes in an online social network used for broadcasting news, that non-conservative Alpha-Centrality leads to a better agreement with empirical ranking schemes than conservative PageRank. In addition, we give a scalable approximate algorithm for computing the Alpha-Centrality in a massive graph. We hope that our investigation will inspire further exploration of the applications of non-conservative diffusion in social network analysis.

THE RELATIONSHIP BETWEEN INTERNET USE AND LIFE SATISFACTION IN LATER LIFE: A FOCUS ON THIRD-LEVEL DIGITAL DIVIDE

Abstract The third-level digital divide highlights how social factors can shape disparities in real-life experiences derived from digital use. This study aims to examine how different types of internet use are related to the life satisfaction of older adults with varying health and wealth statuses through the lens of the third-level digital divide. The data for this study came from the 2022 Korea Digital Divide Survey. A sample of 1,165 older adults aged between 65 and 96 years old was grouped into four categories based on their health and wealth statuses, considering that these two factors tend to shape the social positions of older adults in Korean society. Life satisfaction was measured using 5 items from the 4-point Satisfaction with Life Scale (SWLS), while different types of internet use were accessed with 13 dichotomous items. The findings showed that a significant positive effect of internet use was observed only among older adults with good health status, indicating a stronger effect for those with good wealth status. Particularly, online public services (d=1.41, p< 0.001), cloud services (d=1.35, p< 0.001), and social networking services (d=1.27, p< 0.001) exhibited significant effects. No significant effect was observed for older adults with poor health status, except for online entertainment content, which was found to be significantly related to the life satisfaction of those with low wealth status (d=0.87, p< 0.01). The study findings imply diversity in terms of internet use and its association with life satisfaction, while highlighting social disparities.

DIPA2

The world today is increasingly visual. Many of the most popular online social networking services are largely powered by images, making image privacy protection a critical research topic in the fields of ubiquitous computing, usable security, and human-computer interaction (HCI). One topical issue is understanding privacy-threatening content in images that are shared online. This dataset article introduces DIPA2, an open-sourced image dataset that offers object-level annotations with high-level reasoning properties to show perceptions of privacy among different cultures. DIPA2 provides 5,897 annotations describing perceived privacy risks of 3,347 objects in 1,304 images. The annotations contain the type of the object and four additional privacy metrics: 1) information type indicating what kind of information may leak if the image containing the object is shared, 2) a 7-point Likert item estimating the perceived severity of privacy leakages, and 3) intended recipient scopes when annotators assume they are either image owners or allowing others to repost the image. Our dataset contains unique data from two cultures: We recruited annotators from both Japan and the U.K. to demonstrate the impact of culture on object-level privacy perceptions. In this paper, we first illustrate how we designed and performed the construction of DIPA2, along with data analysis of the collected annotations. Second, we provide two machine-learning baselines to demonstrate how DIPA2 challenges the current image privacy recognition task. DIPA2 facilitates various types of research on image privacy, including machine learning methods inferring privacy threats in complex scenarios, quantitative analysis of cultural influences on privacy preferences, understanding of image sharing behaviors, and promotion of cyber hygiene for general user populations.

Research on the Construction of University Cultural Brand IP Driven by Network Cloud Platform Technology

Abstract Campus culture brand IP, as the soft power in the construction of university brand culture, has become an important material carrier for the inheritance and dissemination of university brand culture. In the cloud platform technology, we are using an information diffusion model to explore the diffusion degree and influence of college culture brand IP information on the network platform. The dissemination process of cultural brand IP information is examined from the point of view of sharing information resources in colleges and universities. Construct a competitive information dissemination system based on the user nodes of online social networks and the interrelationship between users. Promote the dissemination and diffusion of university cultural brand IP information on online social networks using a data-driven approach. The results show that: k value is 0.12, the promotion effect of college culture brand IP is 2.59621e+04, and the relative risk index is 0.354264. Within 0-6 hours, the maximum number of IP views reaches 34,000 times, which indicates that the authority of the college platform and the information diffusion model can improve the diffusion of college culture brand IP.

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Online monitoring of social networks offers exciting features for platforms, enabling both technical and behavioral analysis. Numerous studies have explored the adaptation of traditional quality control methods for detecting change points within social networks. However, the current research studies face limitations such as an overreliance on case-based attributes, high computational costs, poor scalability with large networks, and low sensitivity in fast change point detection. This paper proposes a novel algorithm for social network monitoring using One-Class Support Vector Machines (OC-SVMs) to address these limitations. Additionally, using both nodal and network-level attributes makes it versatile for diverse social network applications and effectively detecting network disturbances. The algorithm utilizes a well-defined training data dictionary with an updating procedure for evolutionary networks, enhancing memory and time efficiency by reducing the processing of input data. Extensive numerical experiments are conducted using an EpiCNet model to simulate interactions in an online social network, covering six change scenarios to evaluate the proposed methodology. The results show lower Average Run Length (ARL) and Expected Delay Detection (EDD), demonstrating the superior accuracy and effectiveness of the OC-SVM algorithm compared to alternative methods. Applying OC-SVM to the Enron Email network indicates its capability to identify change points, reflecting the tumultuous timeline that led to Enron's downfall. This further validates the substantial advancement of OC-SVM in social network monitoring and opens doors to broader real-world applications.

A survey of machine learning based techniques for hate speech detection on Twitter

The use of the Internet and social networks for communication has significantly increased in recent years. Twitter is the third most popular worldwide Online Social Network (OSN), only after Facebook and Instagram. Compared to other OSNs, Twitter presents a simpler data model and more straightforward data access API, making it a valuable tool for studying and analyzing online behavior, including abusive patterns. This survey attempts to create a machine learning-based guide for automatic hate speech classification, including a description of Twitter's technology and terminology, social graphs, sentiment analysis concepts, and hate speech identification. This study also adopted a systematic literature review on the most advanced computing techniques involved with the subject, focusing on state-of-the-art machine learning and research directions.

An algorithm for next-level connections of users in online social networks

An algorithm for next-level connections of users in online social networks

Generating Robust Adversarial Examples against Online Social Networks (OSNs)

Online Social Networks (OSNs) have blossomed into prevailing transmission channels for images in the modern era. Adversarial examples (AEs) deliberately designed to mislead deep neural networks (DNNs) are found to be fragile against the inevitable lossy operations conducted by OSNs. As a result, the AEs would lose their attack capabilities after being transmitted over OSNs. In this work, we aim to design a new framework for generating robust AEs that can survive the OSN transmission; namely, the AEs before and after the OSN transmission both possess strong attack capabilities. To this end, we first propose a differentiable network termed SImulated OSN (SIO) to simulate the various operations conducted by an OSN. Specifically, the SIO network consists of two modules: (1) a differentiable JPEG layer for approximating the ubiquitous JPEG compression and (2) an encoder-decoder subnetwork for mimicking the remaining operations. Based upon the SIO network, we then formulate an optimization framework to generate robust AEs by enforcing model outputs with and without passing through the SIO to be both misled. Extensive experiments conducted over Facebook, WeChat and QQ demonstrate that our attack methods produce more robust AEs than existing approaches, especially under small distortion constraints; the performance gain in terms of Attack Success Rate (ASR) could be more than 60%. Furthermore, we build a public dataset containing more than 10,000 pairs of AEs processed by Facebook, WeChat or QQ, facilitating future research in the robust AEs generation. The dataset and code are available at https://github.com/csjunjun/RobustOSNAttack.git .

Information security risks and sharing behavior on OSN: the impact of data collection awareness

PurposeThis study aims to ascertain the impact of data collecting awareness on perceived information security concerns and information-sharing behavior on social networking sites.Design/methodology/approachBased on communication privacy management theory, the study forecasted the relationship between information-sharing behavior and awareness of data collecting purposes, data collection tactics and perceived security risk using structural equation modeling analysis and one-way ANOVA. The sample size of 521 young social media users in Vietnam, ages 18 to 34, was made up of 26.7% men and 73.3% women. When constructing the questionnaire survey method with lone source respondents, the individual’s unique awareness and experiences with using online social networks (OSNs) were taken into account.FindingsThe results of the investigation demonstrate a significant relationship between information-sharing and awareness of data collecting, perceptions of information security threats and behavior. Social media users have used OSN privacy settings and paid attention to the sharing restriction because they are concerned about data harvesting.Research limitations/implicationsThis study was conducted among young Vietnamese social media users, reflecting specific characteristics prevalent in the Vietnamese environment, and hence may be invalid in other nations’ circumstances.Practical implicationsSocial media platform providers should improve user connectivity by implementing transparent privacy policies that allow users to choose how their data are used; have clear privacy statements and specific policies governing the use of social media users’ data that respect users’ consent to use their data; and thoroughly communicate how they collect and use user data while promptly detecting any potential vulnerabilities within their systems.Originality/valueThe authors ascertain that the material presented in this manuscript will not infringe upon any statutory copyright and that the manuscript will not be submitted elsewhere while under Journal of Information, Communication and Ethics in Society review.

An Interlayer Link Prediction Method Based on Edge-Weighted Embedding

Presently, users usually register accounts on online social networks (OSNs). Identifying the same user in different networks is also known as interlayer link prediction. Most existing interlayer link prediction studies use embedding methods, which represent nodes in a common representation space by learning mapping functions. However, these studies often directly model links within the pre-embedding layer as equal weights, fail to effectively distinguish the strength of edge relationships, and do not fully utilize network topology information. In this paper, we propose an interlayer link prediction model based on weighted embedding of connected edges within the network layer, which models the links within the network layer as weighted graphs to better represent the network and then uses appropriate embedding methods to represent the network in a low-dimensional space. After embedding, vector similarity and distance similarity are used as comprehensive evaluation scores. This paper has conducted a large number of simulation experiments on actual networks. The results show that our proposed model has higher prediction accuracy in all aspects than current advanced models and can achieve the highest accuracy when the training frequency is low, which proves the validity of the proposed model.

Towards Understanding and Characterizing the Arbitrage Bot Scam In the Wild

This paper presents the first comprehensive analysis of an emerging cryptocurrency scam named "arbitrage bot" disseminated on online social networks. The scam revolves around Decentralized Exchanges (DEX) arbitrage and aims to lure victims into executing a so-called "bot contract" to steal funds from them. To entice victims and convince them of this scheme, we found that scammers have flocked to publish YouTube videos to demonstrate plausible profits and provide detailed instructions and links to the bot contract. To collect the scam at a large scale, we developed a fully automated scam detection system namedCryptoScamHunter, which continuously collects YouTube videos and automatically detects scams. Meanwhile,CryptoScamHunter can download the source code of the bot contract from the provided links and extract the associated scam cryptocurrency address. Through deployingCryptoScamHunter from Jun. 2022 to Jun. 2023, we have detected 10,442 arbitrage bot scam videos published from thousands of YouTube accounts. Our analysis reveals that different strategies have been utilized in spreading the scam, including crafting popular accounts, registering spam accounts, and using obfuscation tricks to hide the real scam address in the bot contracts. Moreover, from the scam videos we have collected over 800 malicious bot contracts with source code and extracted 354 scam addresses. By further expanding the scam addresses with a similar contract matching technique, we have obtained a total of 1,697 scam addresses. Through tracing the transactions of all scam addresses on the Ethereum mainnet and Binance Smart Chain, we reveal that over 25,000 victims have fallen prey to this scam, resulting in a financial loss of up to 15 million USD. Overall, our work sheds light on the dissemination tactics and censorship evasion strategies adopted in the arbitrage bot scam, as well as on the scale and impact of such a scam on online social networks and blockchain platforms, emphasizing the urgent need for effective detection and prevention mechanisms against such fraudulent activity.