Massive User Research Articles

GEOPRIVACY KNOWLEDGE, ATTITUDES, AND BEHAVIORS in CONTEMPORARY CHINA

ABSTRACT China has an Internet penetration rate of over 70 percent and a massive user base of social media. However, the topic of privacy attitudes among Chinese individuals remains understudied. We analyzed geoprivacy concerns in China through an online survey and regression analysis. Our findings suggest a positive relation among privacy knowledge, attitude, and behavior, consistent with related literature. Declarative knowledge (such as privacy rights), on the other hand, was found to have a negative relation with privacy concerns, which has not been reported previously. In terms of demographic moderators, females had less privacy knowledge but more privacy protection behaviors, while the impact of age on privacy concerns was inconclusive. A notable discovery was the regional difference in privacy concerns within China, suggesting the potential geopolitical influence on individuals’ values and beliefs. Combined with the uncovering of behavioral change in response to involuntary location disclosure, the results of this article challenge the conventional notion that Chinese individuals are indifferent to their online privacy, thus reintroducing an underexplored perspective from the Global South into geoprivacy studies.

Short Video Platform Advertising Marketing Ideas and Logic--Taking KANS as an Example

With the rapid rise of mobile Internet, short video platforms have captured massive user attention. In 2023, short video users exceeded 750 million, shifting advertising revenue from traditional to new media. Short video marketing, with its rich content and immersive experiences, has become crucial for brand promotion. Young users' creativity is pivotal, and short video ads blend seamlessly with content to enhance brand recognition. However, challenges still exist. Content homogenization, low user participation may impact brand loyalty. Ad effectiveness should be evaluated properly. This paper examines KANS's short video marketing, highlighting strategies like "short drama + advertising," and addressing issues through creative planning and interactive engagement. KANS's success, aided by precise audience targeting and innovative tactics, demonstrates the potential of short video advertising. However, continuous improvement in content uniqueness, user engagement, and data-driven evaluation is necessary for sustained effectiveness. The study provides suggestions for brands to optimize their short video marketing strategies, ensuring alignment with user needs and market dynamics.

Mobile User Traffic Generation Via Multi-Scale Hierarchical GAN

Mobile user traffic facilitates diverse applications, including network planning and optimization, whereas large-scale mobile user traffic is hardly available due to privacy concerns. One alternative solution is to generate mobile user traffic data for downstream applications. However, existing generation models cannot simulate the multi-scale temporal dynamics in mobile user traffic on individual and aggregate levels. In this work, we propose a multi-scale hierarchical generative adversarial network (MSH-GAN) containing multiple generators and a multi-class discriminator. Specifically, the mobile traffic usage behavior exhibits a mixture of multiple behavior patterns, which are called micro-scale behavior patterns and are modeled by different pattern generators in our model. Moreover, the traffic usage behavior of different users exhibits strong clustering characteristics, with the co-existence of users with similar and different traffic usage behaviors. Thus, we model each cluster of users as a class in the discriminator’s output, referred to as macro-scale user clusters. Then, the gap between micro-scale behavior patterns and macro-scale user clusters is bridged by introducing the switch mode generators, which describe the traffic usage behavior in switching between different patterns. All users share the pattern generators. In contrast, the switch mode generators are only shared by a specific cluster of users, which models the multi-scale hierarchical structure of the traffic usage behavior of massive users. Finally, we urge MSH-GAN to learn the multi-scale temporal dynamics via a combined loss function, including adversarial loss, clustering loss, aggregated loss, and regularity terms. Extensive experiment results demonstrate that MSH-GAN outperforms state-of-art baselines by at least 118.17% in critical data fidelity and usability metrics. Moreover, observations show that MSH-GAN can simulate traffic patterns and pattern switch behaviors.

Negative rivalry in use: toward a knowledge-sharing perspective in the digital economy era

PurposeThis study aims to introduce and prove the existence of negative rivalry in use, which should be an integral part of goods taxonomy, from the perspective of knowledge sharing and further present the critical role of knowledge sharing in the digital economy era by reviewing the literature, theoretical analysis and real-world cases. It also aims to open a new door for re-recognizing knowledge sharing through an interdisciplinary framework.Design/methodology/approachThis study proves the existence of negative rivalry through both theoretical analysis (4-E model) and real-world cases, especially the cases of Tesla and ChatGPT, and puts up new reasons for knowledge sharing in the era of digital economy through interdisciplinary methods.FindingsThe authors find out that there are many new phenomena beyond the spectrum of current goods taxonomy, especially beyond the priori understanding of rivalry in use. Digital platforms such as ChatGPT should have been “used up” in no time, for they have attracted so massive users according to (positive) rivalry in use, or should have been unchanged at most according to non-rivalry in use. But what we see is their rapid upgrading with the help of billions of users. The reason is that negative rivalry in use has completely been neglected. The authors find out that the process of knowledge sharing unveils the unrecognized attribute of rivalry in use, i.e. negative rivalry in use, which serves as the fundamental driving force of the breathtaking growth of all kinds of digital platforms.Originality/valueThis study originally put up a 4-E model of goods’ rivalry in use, the brand new term, i.e. negative rivalry in use, and proves its existence and working mechanism from the perspective of knowledge sharing. With the introduction of negative rivalry in use, the traditional four-type goods classification model is extended into a six-type model, which may be a sound marginal contribution, to the best of the authors’ knowledge. The study may reshape people’s mindsets on goods usage, especially knowledge management, into a more open-sharing model because it shows that there is very likely a positive-sum game instead of a zero- or negative-sum game for shared knowledge per se and its platform in the process of knowledge sharing in the era of digital economy.

Application of network data security based on wireless sensor technology in the development of digital sharing economy

Nowadays, when the existing algorithms are fused in the feature function network feature, they do not consider the different effects of different features on the saliency detection task. In order to solve this problem, this paper proposes a saliency feature fusion network based on multi-focus mechanism to complete the saliency detection task. While recognizing the important role of the network, it must also be realized that because most network users do not understand the details of network composition and network system analysis, the security of computer network data is relatively low. Unscrupulous hackers use their network technology and software to invade unauthorized networks and hosts without authorization. Therefore, network data security is very important. The competition and monopoly of the digital sharing economy platform has always been a hot topic of increasing attention from domestic and foreign economists. The platform economy is based on an efficient data collection and transmission system, a digital platform supported by continuously developing computing power and powerful data processing algorithms, which greatly promotes the development of social productivity. The core trend of the development of China's digital economy is the ecologicalization of the digital sharing economy. Small industrial platforms rely on massive user resources collected by a large platform to quickly connect the upper and lower channels of the industry to transform and form a new industry ecosystem.

Multi-agent deep reinforcement learning for trajectory planning in UAVs-assisted mobile edge computing with heterogeneous requirements

In heterogeneous wireless networks, massive user equipments (UEs) generate computing tasks with time-varying heterogeneous requirements. To improve the service quality, this paper formulates a unmanned aerial vehicles (UAVs)-assisted mobile edge computing (MEC) framework for time-varying heterogeneous task requirements. In the framework, the task delay and the number of successfully executed tasks are optimized by jointly controlling the trajectories of multiple UAVs. To address the considered trajectory planning optimization problem, a collaborative multi-agent deep reinforcement learning (MADRL) algorithm is proposed, where each UAV is regarded as a learning agent. First, a counterfactual inference based personalized policy update mechanism is proposed to evaluate the independent policy of agents by comparing the policy with a designed counterfactual policy. Based on this idea, each agent updates a personalized policy from both group and individual interests to improve its cooperation ability in dynamic and complex environments. Then, a diversified experience sampling mechanism is proposed to enhance the efficiency of policy evaluation and update with rich experiences provided by the environment interaction and the modified whale optimization algorithm. Finally, evaluation results demonstrate the superiority and effectiveness of the proposed MADRL algorithm.

Automatically Expanding User-Management System for Massive Users in the Cloud Platform

Cloud computing has become one of the key technologies used for big data processing and analytics. User management on cloud platforms is a growing challenge as the number of users and the complexity of systems increase. In light of the user-management system provided by major cloud service providers, which cannot manage multiple types of user systems, this article proposed scale-out automated expansion user management for authorization synchronization to improve the efficiency and scalability of user management on cloud platforms. Three modules for user-automated expansion were designed and implemented to synchronize the authentication information from the cloud platform resource user to the data-processing user. Additionally, an optimized dynamically weighted load-balancing algorithm in Nginx is presented in this article that adjusts the weight according to load information such as CPU and memory usage, and a better load balance can be achieved. The effectiveness of the proposed user-management system is substantiated by comparing it with two existing infrastructures, including multiple data centers and the Huawei cloud platform. The experimental results validate the finding that scale-out automated expansion user management across the Huawei cloud platform can effectively synchronize data accessing authority with cloud resource utilizing authority. Furthermore, the optimized weighted load-balancing algorithm is also valuable for massive concurrent user registration based on limited cloud resources. In the future, this scale-out user-management system could be applied to other cloud platforms and extended by database synchronization to satisfy the needs of data sharing among multiple types of users belonging to different cloud platforms.

When the future comes knocking: psychological perspectives on Artificial Intelligence / Cuando el futuro toca a la puerta: perspectivas psicológicas sobre la Inteligencia Artificial

The recent appearance and massive user adoption of ChatGPT and related technologies is rapidly turning AI from what to most people was once merely a distant concern based in theoretical speculation into one of the most pressing and significant developments we are facing as individuals and as a society. In order to better navigate the abrupt changes we are experiencing, we must consider the history and broader context of AI, as well as spur a dialogue that involves as wide an array of voices as possible. This special issue seeks to contribute by offering a platform to scholars in psychology and the social sciences from which to share their unique perspectives and reflections, so that they may enrich our understanding of this global, urgent and multi-faceted phenomenon.

Analysis of Tencent’s current business model and Why will it continue to Succeed in the Future

Tencent, a global technology giant headquartered in China, has emerged as a key player in the digital landscape, with a diverse portfolio ranging from social media and gaming to financial services. This paper provides a comprehensive analysis of Tencent’s current dominance and the market’s reasons through its business model, examining the key components that lead to its success in the future.The study begins by delving into Tencent’s core business and exploring how its massive user base has contributed to its success. Furthermore, the analysis underscores Tencent’s investment, including the importance of high technology and big data.Looking ahead, this paper proposes strategic pathways for Tencent’s future development. Recommendations include enhancing global partnerships--Tencent’s global expansion strategy and impact on the company’s growth, diversifying cooperation areas, and strengthening content innovation. Additionally, the study emphasizes the significance of investing in emerging technologies such as artificial intelligence and virtual reality in Tencent’s corporate strategy.By synthesizing current research and market trends, this paper aims to provide valuable insights for business leaders, analysts, and stakeholders interested in understanding Tencent’s business model and the potential future directions the company may take.

FROM ACADEMIA TO ANECDOTES: ASSESSING THE QUALITY OF ULCERATIVE COLITIS CONTENT ON YOUTUBE

Abstract INTRODUCTION This study examines the varying impact of YouTube videos on Ulcerative Colitis (UC) from academic and private institutions. While academic channels offer evidence-based, scientifically vetted information, private sources may lack rigorous scrutiny and sometimes prioritize commercial or anecdotal content. With YouTube's massive user base, the absence of an editorial process can result in misinformation. This content quality and credibility discrepancy can significantly affect patient education and treatment outcomes for UC. METHODS We conducted a search on YouTube using the keywords "Ulcerative Colitis" and "UC IBD" to identify relevant videos. Exclusions were made for videos that were not in English, irrelevant, or lacked audio. We documented various video attributes such as views, likes, dislikes, comments, and the nature of the source (academic or private). The videos were then classified as reliable or unreliable based on the scientific accuracy of their content. We employed DISCERN, Global Quality Score (GQS), and the Patient Education Materials Assessment Tool (PEMAT) to evaluate video quality. The consistency among seven investigators in applying DISCERN, GQS, and PEMAT was measured using intraclass correlation. RESULTS We reviewed 21 YouTube videos that appeared in the search results, of which 11 (52.3%) were academic and 10 (47.6%) were private. Academic videos received higher DISCERN scores than private ones (317.5 vs. 23.64 5.07, p=0.018). The Global Quality Score was also higher for academic videos (3.9 vs. 2.2, p< 0.01), as was the PEMAT score (4.1 vs. 2.7, p= 0.022). Furthermore, academic videos were found to have a positive correlation with the number of likes (OR: 0.75, P< 0.001), subscribers (OR:0.68, P< 0.0001), and views (OR:1.52, P< 0.001). CONCLUSION Our study reveals significant quality discrepancies in YouTube videos on Ulcerative Colitis. Academic sources consistently outperformed private ones in accuracy and reliability, as indicated by higher DISCERN, Global Quality Score, and PEMAT ratings. The data also showed a positive correlation between academic sources and user engagement metrics like likes, subscribers, and views. These findings highlight the necessity of directing patients to academically-backed information to minimize the risk of misinformation.

Design and Implementation of a Mobile Experimental Application for Networked Control Systems

With the development of mobile technologies, mobile applications are increasingly designed and developed. In the field of science, technology, engineering, and mathematics (STEM) education, the idea of using mobile devices for teaching and learning has gradually gained popularity and gained in-depth research. Owing to the advantages of convenience, portability and excellent interactive abilities, mobile devices are a good choice for accessing remote laboratories. In propose of promoting the massive online experimentation of control education, a mobile application with framework based on React-Native is implemented in this paper. The overall remote laboratory architecture adopts front-end and back-end separation scheme, which benefits the mobile application focusing on the construction of front-end interface and user experience. Various functional modules and technologies are applied in the mobile application, allowing users to access various experiment test rigs in the remote laboratory with intuitive experimental immersion. Moreover, a complete laboratory management system to manage the massive users from different universities has also been integrated into the mobile application. At the end, a remote networked control experiment has been conducted to prove the practicability of the mobile application.

Exploration of Data Asset Valuation for Internet Platform Companies Based on the DEA-BCC Model

The valuation and pricing of data assets is not only a key factor in the national strategic development but also of extreme importance for corporate operations. This article proposes to explore the valuation and pricing of data assets from the perspective of intangible assets based on the theory that “data assets are a component of intangible assets.” Secondly, it focuses on the internet platform companies that rely on massive user data. Considering that a company’s value is influenced by various asset values (including the value of fixed assets, intangible assets, current assets, etc.), this article constructs an input-output indicator system based on corporate assets, and describes the value of data assets from three aspects: data quality, data application, and data risk. Ultimately, the total value of a company’s data assets is obtained through the proportion of various data asset indicators in the company’s total market value.

Fake News Sharing Among Weibo Users in China

In an era where social media's impact on public discourse is increasingly pronounced, this study probes into the spread of fake news among Weibo users in China, a significant issue given the platform's massive user base and China's unique media environment. Adopting a quantitative research approach, the study primarily investigates how situational motivation and information-seeking behaviours influence the sharing of fake news. Utilising regression analysis, a method pivotal for understanding the relationship between various independent variables and the sharing of misinformation, the research sheds light on the nuanced interplay of factors that drive users to disseminate false information. Key findings reveal that situational factors, such as the need for social recognition or the urge to disseminate urgent news, considerably elevate the likelihood of sharing fake news. Additionally, the study uncovers the complex nature of information-seeking behaviour: while it generally leads to better-informed decisions, it can sometimes result in the sharing of unverified news under certain circumstances. These outcomes are crucial for understanding the behavioural patterns behind fake news sharing on social media. They offer valuable insights for policymakers, social media platforms, and educators in formulating effective countermeasures against misinformation, aiming to foster a more discerning and informed online community. This study, therefore, contributes significantly to the existing body of knowledge on digital media, misinformation, and public opinion, highlighting the critical need for strategies to combat the spread of fake news in the digital age. Keywords: Fake news sharing, information seeking, situational motivation, agenda-setting theory, China.

Towards high spectral efficiency UDWDM-PON based on the simplified coherent reception of pulsed shaped PAM-4 signals.

Enhancing spectral efficiency (SE) of ultra-dense wavelength division multiplexing passive optical network (UDWDM-PON) is vital to providing broadband access for massive users. Here, we experimentally demonstrate a high SE UDWDM-PON in the C-band, based on the simplified coherent reception of 10 Gb/s 4-level pulse-amplitude modulation (PAM-4) signals. We investigate the WDM signal reception by mathematical derivation and propose to enhance the SE by adopting both intradyne detection and pulse shaping techniques. Then, both approaches are numerically evaluated, with an identification that there occurs a trade-off between SE and power budget improvements. Finally, we experimentally achieve a SE of 0.83 (bit/s)/Hz and a power budget of 25 dB for a proof-of-concept 3 × 10 Gb/s PAM-4 downstream transmission over 20 km standard single mode fiber (SSMF).

Research on the detection model of mental illness of online forum users based on convolutional network

Recently, there will be more than 4.62 billion social media users worldwide. A large number of users tend to publish personal emotional dynamics or express opinions on social media. These massive user data provide data support for the development of mental illness detection research and have achieved good results. However, it is difficult for current mental illness detection models to accurately identify key emotional features from a large number of posts issued by users to detect problem users. In view of the fact that the existing models cannot more accurately extract the words with high emotional contribution in the content of user posts, this paper proposes two hierarchical user post feature representation models, named Single-Gated LeakReLU-CNN (SGL-CNN) and Multi-Gated LeakyReLU-CNN (MGL-CNN). We leverage these 2 models to identify users with mental illness in online forums. For all posts published by each user within a certain time span, the model proposed in this paper can identify key emotional features in them and filter out other unimportant information as much as possible. In addition, the addition of gating units in this paper can significantly improve the performance of emotion detection tasks. The experimental results based on the task of RSDD dataset prove that the performance of the model proposed in this paper is superior to that of the existing methods.

A Scalable Deep-Learning-Based Active User Detection Approach for SEU-Assisted Cell-Free Massive MIMO Systems

Massive ultra-reliable and low-latency communications (mURLLC) is an emerging and dominate traffic service in 6G. To reduce the signaling overhead and access delay, grant-free random access (GFRA) is widely used in mURLLC. As the first step in GFRA, active user detection (AUD) is aimed to identify the set of active users accurately and timely. Conventional AUD schemes relying on iterative computations over massive users bring redundant computing overload and processing delay, which seriously affect the system scalability in the mURLLC scenario. Considering the near-real-time requirement of mURLLC, we propose a scalable deep learning-based AUD approach utilizing similar channel sparsity in cell-free (CF) massive multiple input multiple-output (mMIMO) systems. By exploiting the distributed computing unit, i.e., space expansion unit (SEU), we design a SEU-assisted CF mMIMO to improve the scalability of the traditional centralized CF computing architecture. In the proposed system, all access points (APs) are divided into several clusters, and the SEU in each cluster provides a reliable distributed AUD scheme through a one-dimensional convolutional network (1D CNN). In addition, a transfer learning-based ensemble model is established at the CPU to achieve a better global detection decision. Simulation results demonstrate the superiority of our scalable deep learning-based approach, and reveal that through the transfer learning-based model fusion at the CPU, our proposed scalable SEU-assisted approach can obtain success probability close to that of the centralized CF computing scheme with less access delay. In addition, our scheme requires fewer pilots than other CS-based schemes.

Emerging Technologies for 6G Communication Networks: Machine Learning Approaches.

The fifth generation achieved tremendous success, which brings high hopes for the next generation, as evidenced by the sixth generation (6G) key performance indicators, which include ultra-reliable low latency communication (URLLC), extremely high data rate, high energy and spectral efficiency, ultra-dense connectivity, integrated sensing and communication, and secure communication. Emerging technologies such as intelligent reflecting surface (IRS), unmanned aerial vehicles (UAVs), non-orthogonal multiple access (NOMA), and others have the ability to provide communications for massive users, high overhead, and computational complexity. This will address concerns over the outrageous 6G requirements. However, optimizing system functionality with these new technologies was found to be hard for conventional mathematical solutions. Therefore, using the ML algorithm and its derivatives could be the right solution. The present study aims to offer a thorough and organized overview of the various machine learning (ML), deep learning (DL), and reinforcement learning (RL) algorithms concerning the emerging 6G technologies. This study is motivated by the fact that there is a lack of research on the significance of these algorithms in this specific context. This study examines the potential of ML algorithms and their derivatives in optimizing emerging technologies to align with the visions and requirements of the 6G network. It is crucial in ushering in a new era of communication marked by substantial advancements and requires grand improvement. This study highlights potential challenges for wireless communications in 6G networks and suggests insights into possible ML algorithms and their derivatives as possible solutions. Finally, the survey concludes that integrating Ml algorithms and emerging technologies will play a vital role in developing 6G networks.

Distributed ItemCF Recommendation Algorithm Based on the Combination of MapReduce and Hive

The ItemCF algorithm is currently the most widely used recommendation algorithm in commercial applications. In the early days of recommender systems, most recommendation algorithms were run on a single machine rather than in parallel. This approach, coupled with the rapid growth of massive user behavior data in the current big data era, has led to a bottleneck in improving the execution efficiency of recommender systems. With the vigorous development of distributed technology, distributed ItemCF algorithms have become a research hotspot. Hadoop is a very popular distributed system infrastructure. MapReduce, which provides massive data computing, and Hive, a data warehousing tool, are the two core components of Hadoop, each with its own advantages and applicable scenarios. Scholars have already utilized MapReduce and Hive for the parallelization of the ItemCF algorithm. However, these pieces of literature make use of either MapReduce or Hive alone without fully leveraging the strengths of both. As a result, it has been difficult for parallel ItemCF recommendation algorithms to feature both simple and efficient implementation and high running efficiency. To address this issue, we proposed a distributed ItemCF recommendation algorithm based on the combination of MapReduce and Hive and named it HiMRItemCF. This algorithm divided ItemCF into six steps: deduplication, obtaining the preference matrixes of all users, obtaining the co-occurrence matrixes of all items, multiplying the two matrices to generate a three-dimensional matrix, aggregating the data of the three-dimensional matrix to obtain the recommendation scores of all users for all items, and sorting the scores in descending order, with Hive being used to carry out steps 1 and 6, and MapReduce for the other four steps involving more complex calculations and operations. The Hive jobs and MapReduce jobs are linked through Hive’s external tables. After implementing the proposed algorithm using Java and running the program on three publicly available user shopping behavior datasets, we found that compared to algorithms that only use MapReduce jobs, the program implementing the proposed algorithm has fewer lines of source code, lower cyclomatic complexity and Halstead complexity, and can achieve a higher speedup ratio and parallel computing efficiency when processing all datasets. These experimental results indicate that the parallel and distributed ItemCF algorithm proposed in this paper, which combines MapReduce and Hive, has both the advantages of concise and easy-to-understand code as well as high time efficiency.

UNISON framework for user requirement elicitation and classification of smart product-service system

Smart product-service system (smart PSS) has become the manufacturers’ new revenue and sustainable competitiveness sources with the rise of digital servitization. To configure a customer-tailored smart PSS effectively, requirement analysis plays a significant role. Till now, the heterogeneity feature of smart PSS requirements, the usage of user-generated data, and data-driven paradigm have not been considered sufficiently. Hence, this study develops a UNISON framework based on the integration of deep learning approaches for eliciting and evaluating the user requirements for smart PSS with a systematic process, data-driven manner, and quantitative results. Adopting the user-centric perspective, this study employed massive user online reviews as the niche for requirement analysis. The bidirectional long short-term memory (Bi-LSTM) neural network and bi-term topic model (BTM) were employed to extract and elicit the smart PSS requirements in both product and service aspects. Further, a multi-dimensional and automatic requirement evaluation method integrating the sentiment analysis, IPA-Kano model, and opportunity algorithm was proposed for assessing, classifying, and prioritizing the identified requirement items. A smart PSS requirement landscape map was also developed to present the analysis results visually and guide decision-making. An empirical study has been conducted for the smart cleaning robot service system to prove the feasibility of the suggested approach. Enabled by the hybrid application of big data analytics and deep learning techniques, this study can empower the smart PSS provider to understand the user requirements comprehensively and upgrade the smart PSS design effectively.

Social Media: An Exploratory Study of Information, Misinformation, Disinformation, and Malinformation

Abstract The widespread use of social media all around the globe has affected the way of life in all aspects, not only for individuals but for businesses as well. Businesses share their upcoming events, reveal their products, and advertise to their potential customers, where individuals use social media to stay connected with their social circles, get updates and news from social media pages of news agencies, and update their information regarding the latest activities, businesses, economy, events, politics, trends, and about their area of interest. According to Statista, there were 4.59 billion users of social media worldwide in 2022 and expected to grow up to 5.85 billion in the year 2027. With its massive user base, social media does not only generate useful information for businesses and individuals, but at the same time, it also creates an abundance of misinformation and disinformation as well as malinformation to acquire social-political or business agendas. Individuals tend to share social media posts without checking the authenticity of the information they are sharing, which results in posts having misinformation, disinformation, or malinformation becoming viral around the world in a matter of minutes. Identifying misinformation, disinformation, and malinformation has become a prominent problem associated with social media.