Online Social Network Applications Research Articles

Social Media App for Connecting Similar Interests People Using flutter

Understanding interest similarity in Online Social Networks (OSNs) is crucial for various applications. This study addresses the challenge of determining interest similarity on platforms like Facebook, where users may not explicitly disclose their interests. Utilizing a substantial dataset of 479,048 users and 5,263,351 user-generated interests, the research focuses on movies, music, and TV shows. Findings reveal homophily in interest similarity, demonstrating that individuals tend to share more similar tastes when they have comparable demographic information or are connected as friends. A practical prediction model is proposed, facilitating the selection of users with high-interest similarities and enhancing decision-making for OSN applications. Additionally, the paper introduces a novel method using a tag network to connect users with similar interests, outperforming traditional methods by providing a more efficient means of connecting like-minded individuals in social networks. Key Word:Face image synthesis, Generative adversarial network, Face Recognition.

A REVIEW ARTICLE: USE OF SENTIMENT ANALYSIS IN SOCIAL MEDIA

As a result of the growth of online social networking platforms and applications, a sizeable amount of user-generated text content is created daily in the form of comments, reviews, and short text messages. Users can write messages, share them, and add images and videos to social networking sites like Twitter, Facebook, and others. Consequently, a significant volume of sentiment-rich data is generated. Sentiment analysis then comes into play in this scenario, which evaluates opinions as positive, neutral, or negative by extracting, recognizing, or representing them from various sources, including social media, news, articles, and blogs. This study aims to analyze the results from different sentiment analysis models and technologies that combine natural language processing. Case studies of various industries that can benefit or have been benefiting from sentiment analysis are also discussed to provide an approachable pathway for anyone who wishes to go more deeply into this field. For example, the business world has used it to learn what customers think of a certain company or brand. The impact of profanity on how readers interpret tweets and other social media messages are studied in sociology and psychology. Political scientists are trying to anticipate election results based on tweets to evaluate answers, among other things, and to look for trends, ideological bias, and opinions. Researchers have previously evaluated numerous models using well-known techniques like Naive Bayes, support vector machines, etc., and the findings have been compared with promising outcomes.

Effect of Online Social Networking on High School Students Academic Performance

Online Social Networking becoming one of the most popular trends among students around the globe. There is an increasing number of research in the recent years to study this phenomenon and this study is focusing on the implications of the use of Online Social Networking towards the high school students’ academic performance in Malaysia. Achieving excellence grades is vital to the high school students because it generally will give the students a better chance to obtain a good university as well as the opportunity for enrolling for better courses when pursuing their tertiary education. Is the growing use of Online Social Networking applications detrimental to the students’ academic success? Or is the Online Social Networking use behavior among the high school students in Malaysia help them to improve their academic performance? This study shade some light. The study result shows that students’ use of Online Social Networking is positively correlated to the students’ academic performance. This result supporting some of the previous study highlighting the benefits of Online Social Networking to the students.

Validity and Reliability of the Persian Version of COVID-19 Anxiety Syndrome Scale Among the Iranian General Population.

The crisis of the COVID-19 prevalence in Iran, as well as the world, caused mental disorders and anxiety syndrome. The COVID-19 anxiety syndrome scale (C-19ASS) assesses conceptually and psychometrically the nature of the COVID-19 threat experience instead of a response to the threat, fear, and COVID-19 anxiety. Therefore, the aim of this study is to evaluate the psychometric properties of the Persian version of the anxiety syndrome scale of COVID-19 in the population of Iran. The Persian version of C-19ASS was sent to Iranian adults via online social networking applications and finally, 932 adults responded to the questionnaire. The results of exploratory factor analysis revealed two-factor structures for C-19ASS, which explained 48.70% of the total variance. Given the confirmatory factor analysis findings, all goodness of fit indices confirmed the model fit. All coefficients of internal consistency were estimated as acceptable reliability. The results showed that the C-19ASS has good psychometric properties, and can be used by researchers, psychologists, and healthcare providers to assess the anxiety syndrome of the Iranian population during the COVID-19 pandemic.

DDoS attack resisting authentication protocol for mobile based online social network applications

The rapid development of smartphone technology and the Internet services in mobile devices facilitates easy access to online social networking (OSN) sites anytime, anywhere. At the same time, this allures the adversaries to exploit the OSNs as a soft target for easy execution of various attacks that can quickly spread to a large number of users. In distributed denial-of-service (DDoS) attacks, an adversary aims to overwhelm the normal traffic of a targeted server with a flood of fake login messages so that the associated Internet service or website turns inoperable. In this paper, we propose a secure and lightweight authentication scheme (PRDoS) that resists DDoS and other security attacks in mobile OSN environments. We provide a multi-faceted solution towards the remedy of DDoS attacks in the OSN environment. After a certain threshold, the scheme discards further user login attempts and blocks an adversary who intends to overload the network server. We use the pre-loaded shadow identity and emergency key pairs, and a key-refilling strategy that rebuilds the essential synchronization between a blocked naive user and the OSN server. This technique restores the intended un-linkability property of the protocol. Using NS3 simulation, we study the impact of DDoS attackers on network throughput and network delay. Moreover, we validate and compare the proposed scheme against state-of-the-art solutions using the real attacks and benign datasets. We use the Canadian Institute for Cybersecurity (CIC) DoS dataset 2017, which is generated by capturing the normal and DoS attack packets separately with subsequent pre-processed for testing. We also use the machine learning (ML) algorithms, such as K-Nearest Neighbor (KNN), Gaussian Naive Bayes, and Multilayer Perceptron (MLP) to demonstrate the performance of the proposed solution in a practical attack detection scenario. We observe that these algorithms provide 97.05%, 95.48%, and 96.6% DDoS attack detection accuracy, respectively.

Using Topic Modeling and Word Embedding for Topic Extraction in Twitter

Topic analysis (also called topic detection, topic modeling, or topic extraction) is a machine learning technique that organizes and understands large collections of text data, by assigning “tags” or categories according to each individual text's topic or theme. Topic analysis uses natural language processing (NLP) to break down human language into blocks (speech, words, sentences, context) so that you can find patterns and unlock semantic structures within texts to extract insights and help make data-driven decisions. The two most common approaches for topic analysis with machine learning are NLP topic modeling and NLP topic classification. Topic modeling faces several challenges: some are general to the NLP task (as extracting the context of document), and some are specific and are related to the nature (or properties) of the documents. One particular type of documents that raises several challenges are short-text documents that we find in Social Networks. First, we should note that with the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. However, despite their ubiquity, extracting topics from shorts texts remains a difficult task for several reasons. First, unlike traditional normal texts, short texts typically only include a few words. Therefore, directly applying traditional models on short texts will suffer from the severe data sparsity problem (i.e., the sparse word co-occurrence patterns in individual document). Second, the limited contexts make it more difficult to identify the senses of ambiguous words in short texts. Third, in general, the performance of the used machine learning models relies on labeled data. Unfortunately, due to their volume, labeling short-text from social networks remains a tedious and hard task. In this paper, we propose a model for extracting topics in short-texts, and more specifically in Twitter. The key feature of our proposal is the use of word embedding technique for topic modeling, and k-Means clustering for the semi-automatic annotation of tweets. In addition, and unlike most existing approaches, we assign in our model a set of topics to a tweet each with a confidence degree.

PSYCHOLOGICAL IMPACT OF COVID-19PANDEMIC ON PATIENTS WITH OBSESSIVE-COMPULSIVE DISORDER

Introduction: Obsessive-compulsive disorder is characterized by persistent irrational thoughts manifested by repeated actions. These disorders often have an impact on the social life of those who suffer from them, with a tendency towards isolation which can be exacerbated during this period of confinement. Among these disorders, the fear of contamination is very common. This fear is influenced by culture, society, and global health issues. Thus, in 1920, many cases of fear of syphilis emerged following major awareness campaigns pertaining to the disease. In the 1980s, obsessive disorders related to the risk of HIV infection appeared. Likewise, the Covid-19 coronavirus epidemic is likely to cause a significant deal of anxiety for some people with obsessive-compulsive disorder, thus justifying the aggravation of the clinical condition of this category of patients. The hand-washing recommendations that were promoted during the pandemic would prove to be a major trigger for obsessive-compulsive disorder. Quickly, this gesture becomes an obsession and gradually as the virus spreads, experts expect the cases of associated obsessive-compulsive disorders to increase [1]. Closeness between family members and disrupted habits during confinement can lead to conflict, which can increase the frequency of compulsions. Meditation can also increase the flow of uncontrolled thoughts which can promote the emergence of obsessions. During this time of the pandemic, doctors and people with obsessive-compulsive disorders find themselves in a dilemma. On the one hand, to treat obsessive-compulsive disorders, hand washing is prohibited, and on the other hand, to prevent the spread of the virus, hand washing is strongly recommended. Covid-19 can therefore be an unbearable nightmare, and harder to manage for people with obsessive-compulsive disorders. Objective: This study aims to assess the psychological repercussions of confinement on patients suffering from obsessive-compulsive disorders during the Covid-19 pandemic, and to underline the link between the aggravation of the symptoms of obsessive-compulsive disorder and the underlying perceived stress associated with it. Methodology: This is a descriptive and analytical study, which involves a sample of patients tracked for obsessive-compulsive disorders, and which is founded on collecting their individual information through an anonymous survey, which was submitted via social networks, either by using smartphones or during consultations. As an assessment instrument, in addition to the survey for collecting socio-demographic data, we opted for the Yale-Brown Scale to assess the severity of the symptoms of obsessive-compulsive disorder, as well as the Cohen Perceived Stress Scale (PSS-10) to assess the perceived stress on this group of patients during this pandemic. These scales were chosen because of their frequent use in several studies on the same topic. Results:We collected 102 responses, of which 55% were women 39% of our candidates worked remotely and only 38.8% of patients were able to continue their medical follow-up at the hospital during the confinement. Concerningour results,it was observed that our participants experienced significant changes in terms of the severity of the total symptoms of obsessive-compulsive disorder during the period of confinement with an accentuation of 90% of the symptoms of contamination and 53.8% of the hands washing compulsions during the Covid-19 pandemic. Respondents, who had symptoms of obsessive-compulsive disorder, since the onset of Covid-19, were the only ones who were significantly more likely to be vulnerable to moderately high stress. Likewise, respondents who compulsively washed their hands were significantly more likely to experience vulnerability to moderate to high stress. 45.3% of our applicants were extremely anxious, 33% were very anxious, and 21.7% were anguished to a tolerable level, with a rate of 75.5% of patients developing a social handicap or an inability to function. The sample showed that subjects who could not work or study from a remote position during the quarantine, those who lived with a relative in the same house during the quarantine, and those who showed symptoms of contamination, had significantly greater aggravation of the obsessive-compulsive disorder total symptoms during the confinement. Effects were not significant for other variables including gender, use of online social network sites and applications during the quarantine and remission status on obsessive-compulsive disorder symptoms before quarantine. Conclusion: Our results suggest that the stressful period of the pandemic is associated with a significant worsening of symptoms on patients with obsessive-compulsive disorder, particularly on those with contamination obsessions and washing compulsions. Having obsessive-compulsive disorder would increase the likelihood of experiencing significant stress during this pandemic.

RELATIONSHIP BETWEEN ONLINE SOCIAL NETWORKING SITES & APPLICATIONS AND UNIVERSITY STUDENTS’ ACHIEVEMENT

Purpose of the study: This study analysed the relationship between Online Social Networking websites and Applications (OSNSA) and university students' academic achievement.
 Methodology: In the present research, a quantitative research design was followed, and a survey research type of descriptive research was used in this study. Data were collected from 150 university students using a modified version of The Online Social Networking Sites scale (Paul, Baker, & Cochran, 2012). The correlation was calculated by using the SPSS 22nd version.
 Main Findings: The findings revealed a connection between students' time spent on OSNSA and their academic outcomes. The time spent on OSNSA was observed to be strongly influenced by the attention span of the students. In particular, we find that a lower academic output is provided by the greater time spent on OSNSA. Also, attention span was strongly associated with features that influence students' academic success, such as their perceptions of society's social networking outlook, their advantages, and disadvantages of OSNSA, use of OSNSA, etc.
 Applications of this study: Many students spend most of their hours on online networking pages, like Facebook, Tok-Tok, and WhatsApp, which are the most common, considering the harmful impact of social media. In certain eyes, social media has embedded a detrimental effect, but it is also used to acquire vital knowledge and social skills for pupils.
 Novelty/Originality of this study: This research is different from other studies because it explores the relationship between social networking sites & the application and academic performance of university students belonging from South Punjab by exploring various facets of OSNSA which ultimately promote university students’ academic performance.

Reducing Cumulative Errors of Incremental CP Decomposition in Dynamic Online Social Networks

CANDECOMP/PARAFAC (CP) decomposition is widely used in various online social network (OSN) applications. However, it is inefficient when dealing with massive and incremental data. Some incremental CP decomposition (ICP) methods have been proposed to improve the efficiency and process evolving data, by updating decomposition results according to the newly added data. The ICP methods are efficient, but inaccurate because of serious error accumulation caused by approximation in the incremental updating. To promote the wide use of ICP, we strive to reduce its cumulative errors while keeping high efficiency. We first differentiate all possible errors in ICP into two types: the cumulative reconstruction error and the prediction error. Next, we formulate two optimization problems for reducing the two errors. Then, we propose several restarting strategies to address the two problems. Finally, we test the effectiveness in three typical dynamic OSN applications. To the best of our knowledge, this is the first work on reducing the cumulative errors of the ICP methods in dynamic OSNs.

Peer-to-Peer-Based Social Networks: A Comprehensive Survey

The use of online social networks, such as Facebook and Twitter, has grown at a phenomenal rate. These platforms offer services that support interactions via messaging, chatting or audio/video conferencing, and also sharing of content. Most, if not all, of these platforms use centralized computing systems; therefore, the control and management of the systems lies entirely in the hands of one provider, who must be trusted to treat the data and communication traces securely. As a zero-trust alternative, peer-to-peer (P2P) technologies promise to support end-to-end communication, uncompromising access control, anonymity and resilience against censorship and massive data leaks through misused trust. The goals of this survey are threefold. First, the survey elaborates the properties of P2P-based online social networks and defines the requirements for such (zero-trust) platforms. Second, it gives an exposition of the building blocks for P2P frameworks that allow the creation of such sophisticated and demanding applications, such as user/identity management, reliable data storage, secure communication, access control and general-purpose extensibility, which are not properly addressed in other P2P surveys. As a third point, it gives a comprehensive analysis of proposed P2P-based online social network applications, frameworks and architectures by exploring the technical details, inter-dependencies and maturity of these solutions.

Systematic Evaluation of LibreSocial—A Peer-to-Peer Framework for Online Social Networks

Peer-to-peer (P2P) networks have been under investigation for several years now, with many novel mechanisms proposed as is shown by available articles. Much of the research focused on showing how the proposed mechanism improves system performance. In addition, several applications were proposed to harness the benefits of the P2P networks. Of these applications, online social networks (OSNs) raised much interest particularly because of the scalability and privacy concerns with centralized OSNs, hence several proposals are in existence. However, accompanying studies on the overall performance of the P2P network under the weight of the OSN applications outside simulations are very few, if any. In this paper, the aim is to undertake a systematic evaluation of the performance of a P2P framework for online social networks called LibreSocial. Benchmark tests are designed, taking into account the random behavior of users, effects of churn on system stability and effect of replication factor. We manage to run benchmark tests for up to 2000 nodes and show the performance against costs of the system in general. From the results it is evident that LibreSocial’s performance is capable of meeting the needs of users.

Analysis of the Public Visual Consumption Turn behind the Popularity of Short Video

From WeChat, QQ to Weibo, Tik Tok this series of online social networking applications, especially with the increasingly maturity of online shopping in recent years, short video and mobile intelligence have been developing rapidly, which have strongly influenced many people's living habits and shopping habits. Their development not only promotes the social and economic development, but also produces a new form of visual communication. This paper discusses the characteristics of short video, analyzes the shift of public visual consumption behind the popularity of short video, and hopes to further build a healthy development path of short video.

Using Topic Modeling Methods for Short-Text Data: A Comparative Analysis.

With the growth of online social network platforms and applications, large amounts of textual user-generated content are created daily in the form of comments, reviews, and short-text messages. As a result, users often find it challenging to discover useful information or more on the topic being discussed from such content. Machine learning and natural language processing algorithms are used to analyze the massive amount of textual social media data available online, including topic modeling techniques that have gained popularity in recent years. This paper investigates the topic modeling subject and its common application areas, methods, and tools. Also, we examine and compare five frequently used topic modeling methods, as applied to short textual social data, to show their benefits practically in detecting important topics. These methods are latent semantic analysis, latent Dirichlet allocation, non-negative matrix factorization, random projection, and principal component analysis. Two textual datasets were selected to evaluate the performance of included topic modeling methods based on the topic quality and some standard statistical evaluation metrics, like recall, precision, F-score, and topic coherence. As a result, latent Dirichlet allocation and non-negative matrix factorization methods delivered more meaningful extracted topics and obtained good results. The paper sheds light on some common topic modeling methods in a short-text context and provides direction for researchers who seek to apply these methods.

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

Online social network (OSN) applications provide different experiences; for example, posting a short text on Twitter and sharing photographs on Instagram. Multiple OSNs constitute a multiplex network. For privacy protection and usage purposes, accounts belonging to the same user in different OSNs may have different usernames, photographs, and introductions. Interlayer link prediction in multiplex network aims at identifying whether the accounts in different OSNs belong to the same person, which can aid in tasks including cybercriminal behavior modeling and customer interest analysis. Many real-world OSNs exhibit a scale-free degree distribution; thus, neighbors with different degrees may exert different influences on the node matching degrees across different OSNs. We developed an iterative degree penalty (IDP) algorithm for interlayer link prediction in the multiplex network. First, we proposed a degree penalty principle that assigns a greater weight to a common matched neighbor with fewer connections. Second, we applied node adjacency matrix multiplication for efficiently obtaining the matching degree of all unmatched node pairs. Thereafter, we used the approved maximum value method to obtain the interlayer link prediction results from the matching degree matrix. Finally, the prediction results were inserted into the priori interlayer node pair set and the above processes were performed iteratively until all unmatched nodes in one layer were matched or all matching degrees of the unmatched node pairs were equal to 0. Experiments demonstrated that our advanced IDP algorithm significantly outperforms current network structure-based methods when the multiplex network average degree and node overlapping rate are low.

Online cloud performance testing in social networks at peak demand scenarios

<p>Cloud computing assures to deliver reliable services through advanced data centers built on virtualized compute and storage technologies. Users demanding more cloud services will be able to access applications and data from a Cloud anywhere in the world in a pay-as-you-go model. In this paper, we focus on cloud-based performance testing for the applications. We use Load Storm testing tool to configure and test plans to measure the performance of web applications in online social networks. The experimental observations designed to assess the performance fluctuations of social networks on maximum consumer demand days have given specific data pointers which could be utilized for further studies of web service enhancements.</p>

A Prediction Method of Peak Time Popularity Based on Twitter Hashtags

Understanding the peak time of popularity evolution can provide insights on recommendation systems and online advertising campaigns. Although popularity evolution has been largely studied, the problem of how to predict its peak time remains unexplored. Taking Twitter hashtags as case study, the goal of this study is to predict when popularity reaches the peak for Twitter hashtags, from the perspective of an online social network application, in the context of the Twitter social network. On the whole, this paper includes three research aspects. Firstly, this paper investigates how early popularity reaches its peaks. Then, it is found that popularity tends to peak in the early stage of its evolution. Secondly, this paper discusses when a peak time prediction should be triggered. Thirdly, this paper designs a multi-modal based deep learning method, where the state-of-art deep learning techniques, such as multi-modal embedding and attention mechanisms, are adopted. We find that in the early stage of popularity evolution, no matter which factor is used as the input, the prediction effect is poor. By contrast, the hashtag string factor has the weakest contribution to popularity prediction in the middle and late stages of popularity evolution. The overall performance of our proposed method is evaluated in terms of the minimum, quartiles, and maximum values of absolute errors. From the experimental results, the prediction method we designed is superior.

Pairing-Free Identity-Based Encryption with Authorized Equality Test in Online Social Networks

Online social networking applications have become more and more popular in the advance of the technological age. Much of our personal information has been disclosed in social networking activities and privacy-preserving still remains a research challenge in social network. Public key encryption scheme with equality test(PKEET), which is an extension of public key encryption with keyword search (PEKS), seems to be a solution. PKEET enables the tester to check whether two given ciphertexts are derived from the same plaintext. Recently, Zhu et al. proposed a pairing-free public key encryption scheme with equality test based on the traditional public key cryptosystem. However, it suffers from certificates management issue. In this paper, we propose a pairing-free identity-based encryption scheme with authorized equality test(PF-IBEAET). The PF-IBEAET scheme also provides fine-grained authorizations. We prove that the scheme is one way secure against chosen identity and chosen ciphertext attack (OW-ID-CCA) and indistinguishable against chosen-identity and chosen-ciphertext attack (IND-ID-CCA) in the random oracle model (ROM). Performance analysis shows that the scheme achieves a better performance than similar schemes.

COMUNICAÇÃO NA INTERNET E A VIOLAÇÃO DO DIREITO À PRIVACIDADE: UMA ANÁLISE AVALIATIVA DAS POLÍTICAS E TERMOS DE USO NA INTERNET

The communication on the Internet brought speed and accessibility, surpassing geographical boundaries, but also brought the possibility of monitoring and collecting data from users. Guided by a bibliographic review, this work aims to address issues of the right to communication and privacy through an evaluative analysis of the privacy policies of the ten largest online social networking applications, especially regarding the collection of personal data and the how they are used, seeking to draw the common aspects and possible failures in communication with the users.

A Provably Secure Group Key Agreement Scheme With Privacy Preservation for Online Social Networks Using Extended Chaotic Maps

With the rapid growth in the popularity of mobile devices and development of network technologies, various online social networking applications have grown in popularity. While these social networking sites provide benefits in terms of enhanced connectivity with people all around the world, they can also pose security threats and raise privacy concerns due to their vulnerability to be exploited by malicious agents. Such social networking sites where members can transmit or share their social information via public channels increase the risk that these information may be exposed to unwanted users. Therefore, it is important to enhance the security of these online social network services using group session keys. These group session keys also need to be updated in case a new member joins the group or an old member leaves the social group. To enhance the trustworthiness of the online social network systems, in this paper, we propose a secure chaotic maps-based group key agreement scheme. In this proposed scheme, we also provide member anonymity to ensure the privacy of the communication between the social networking platform and the members. The proposed solution does not rely on a centralized online key center or a trusted group chairman, thus ensuring fairness. We integrate the mechanisms of message encryption and member verification into the scheme to allow the members to anonymously interact with the services of the online social network. We verify the formal security of the proposed solution using the widely accepted BAN logic analysis and simulation verification with Proverif to prove that our scheme is secure against both passive and active attacks. We also demonstrate that the proposed scheme is efficient in its implementation and achieves greater functionality criteria in comparison with similar existing proposals.

Exploring the Level of Utilizing Online Social Networks as Conventional Learning Settings in UAE from College Instructors’ Perspectives

This research study aims to measure the level of accepting the Online Social Networking applications as an appropriate environment for learning in both of the social and the academic skills of undergraduate students in the UAE colleges from the instructors’ point of view. Moreover, this study investigates the social skills of undergraduate students and their learning attitudes through their use of social applications and webpages. Ten instructors from the City University College of Ajman were the participants of this research study. The paper followed a qualitative method of collecting and analyzing data through assigning a set of semi-structure interview with instructors and a number of observation sessions for their students. The results came up with a positive feedback about using online learning through social networking sites and applications. Both of the social and academic skills of students were improved positively. Keywords: social networking, distance education, perspectives.