Popular Users Research Articles

Attraction behind “Beauty”: Revealing Gay Men’s Self-Presentation on a Dating App with Computer Vision

Profile photos, which are fundamental constructs of online self-presentation, are crucial to building successful online relationships. The visual cues embodied in profile photos are factors that help initiate socialization on visual-based gay dating platforms. This study applies app crawler techniques to collect publicly disclosed profile photos from Finka, a Chinese gay men’s dating app, and uses computer vision artificial intelligence applications to analyze the visual cues from the photos. The results show that appealing faces are relevant factors of user popularity, seemingly confirming the existence of the gay male beauty myth in the dating community. Photos that expose bodies are also popular, although most profile photos have low body visibility, which may be rooted in gay men’s culture in China. Extending existing research, we incorporate the background scene as another theoretical dimension of visual cues. Interestingly, we find an ambiance effect in profile photos—showing background scenes can serve as a self-presentation tactic that potentially influences users’ popularity. In particular, private scenes (e.g., locker room and bedroom scenes) can arouse more desire for further interaction with the presenter.

The Case for Validating ADDIE Model as a Digital Forensic Model for Peer-to-Peer Network Investigation

AbstractRapid technological advancement can substantially impact the processes of digital forensic investigation and present a myriad of challenges to the investigator. With these challenges, it is necessary to have a standard digital forensic framework as the foundation of any digital investigation. State-of-the-art digital forensic models assume that it is safe to move from one investigation stage to the next. It guides the investigators with the required steps and procedures. This brings a great stride to validate a non-specific framework to be used in most digital investigation procedures. This paper considers a new technique for detecting active peers that participate in a peer-to-peer (P2P) network. As part of our study, we crawled the µTorrent P2P client over ten days in different instances while logging all participating peers. We then employed digital forensic techniques to analyse the popular users and generate evidence within them with high accuracy. We evaluated our approach against the standard Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model for the digital investigation to achieve the credible digital evidence presented in this paper. Finally, we presented a validation case for the ADDIE model using the United States Daubert Test and the United Kingdom’s Forensic Science Regulator Guidance – 218 (FSR-G-218) and Forensic Science Regulator Guidance – 201 (FSR-G-201) to formulate it as a standard digital forensic model.

Discourse Formation of Political Dissents via Twitter: Political Sociology of the Subversion Discourse in the Islamic Republic of Iran

Four decades after the Islamic Revolution in Iran and the rise of the Islamic Republic (IR) system, several anti-IR groups have emerged. In the past, the disorganized structure of these groups, their flaws, and dissonance over a common criterion to support or oppose IR and its activities posed some obstacles to their unity. However, over the last decade, they have reached consensus over the subversion of the IR. As a decentralized and disorganized social movement emerging in the wake of 2017 and 2019 protests, this discourse has gained momentum among the political activists, and more people are joining this trend every day, and it is very popular on Persian language social media, especially on Twitter. Loss of faith in the reforms of the IR is one of the issues upon which this discourse movement has reached a general consensus. By exploring and describing the main signifiers of the disorganized subversion discourse in Iran, the article seeks to identify its major characteristics through the qualitative-descriptive analysis of Farsi Twitter content. The research method was the content analysis of Farsi Twitter hashtags of the most popular user accounts belonging to the subversive movement in Iran by extracting the main signifiers and their discourse analysis to examine the main characteristics of the subversion discourse in Iran after 2017.

Assessing the impact of promotion algorithms in social networks on user popularity

In the modern world, social networks are one of the main tools for interaction between people and are becoming more and more popular. Therefore, it is important to understand how the algorithms of social networks and the structural and dynamic aspects of user behavior affect their popularity. The study is based on graph theory and the Frobenius – Perron theory of non-negative matrix decomposition using the stationary distribution of Markov chains. As a result, the behavior of the model over a long period of time is shown and appropriate conclusions are made about the distribution and popularity of users in social networks.

Identifying content unaware features influencing popularity of videos on YouTube: A study based on seven regions

Predicting the popularity of User Generated Content (UGC) is a subject of interest to the Internet service providers, content makers, social media researchers, and online advertisers. However, it is also a challenging task due to multiple factors that influence social networks' content popularity. This work utilizes the Artificial Intelligence (AI) techniques to identify the features that contribute towards a video to enter into the trending category on YouTube. It examines the data generated by a video and its potential to get trending. For this, the present work utilizes AI for feature prediction. An AI-based methodology is presented that assesses the impact of various content-agnostic factors regarding video popularity in seven different regions, including Canada, France, Germany, India, Pakistan, United Arab Emirates, and the United States of America. A dataset is extracted from YouTube for these regions, and feature selection techniques are executed on the datasets to extract important attributes. A class label is assigned to each video, and the dataset is profiled having one of the two class labels, i.e., trending or non-trending. The top three features for each video (region wise) are obtained. It is observed that the trending behavior is dissimilar in different regions. Finally, three classifiers, namely, artificial neural networks, k-Nearest Neighbor, and support vector machine, are trained to predict if a video can get into the trending category on YouTube. The proposed solution is compared with two closely related state-of-the-art methods. This work is useful for content creators and YouTubers to make their video trending and more appealing.

Social effects of topic propagation on Weibo

Weibo, China’s largest microblogging platform, has become one of the key information-sharing platforms in modern society. This study examines topic propagation in relation to microblogging from the perspective of the “peer effect.” Using data of hot topics from Weibo, we analyze how the social effect and propagation pathway influence the topic propagation process. We propose a spatial and temporal heterogeneity diffusion model that includes endogenous and exogenous social effects and is based on but different from the Bass diffusion model. We find that most propagation pathways end after a single level of propagation. The endogenous social effect in microblogs primarily influences the inflow of topics. Such endogenous social effect, combined with the multiplier effect, motivates most users to share a microblog topic in a short period of time. The exogenous social effect primarily influences the outflow of topics, and therefore, the microblog topics of a small number of popular users’ account for most of the share volume. Our results are robust to potential serial correlation, reflection problem, and potential self-selection due to user status.The findings reveal that group characteristics affect individuals’ behaviors and choices in relation to the topic propagation process on microblogging platforms. The use of a spatial and temporal heterogeneity diffusion model and the robustness of the analysis process provide new information for scholars in this field.

Privacy protection scheme for mobile social network

In the recent years, popularity and number of users of mobile social networks have grown significantly. Due to different types of communication on these networks, users share a lot of information with each other. One of the most critical challenges of social networks is disclosure and unauthorized access to information, data, and communication between users that is a kind of violation of their privacy, which can be done by social network providers, especially unauthorized users. One way to protect one’s privacy is using encryption. Therefore, in the present paper, an improved secure design was presented for mobile social network using ciphertext-policy attribute-based encryption (CP-ABE) and advanced encryption standard (AES) that will encrypt users data in an end-to-end manner. In the proposed scheme by CP-ABE encryption, in addition to data privacy, the users’ desired access control is applied to the access of others to their shared data. As a result, unauthorized users will not be able to access and violate data privacy. Furthermore, it will prevent the unauthorized access of social network provider with the help of AES. This scheme provides new management to exchange encryption keys and manage network components for their information. Therefore, the keys are exchanged securely and some of them are generated outside reach of the social network server and by the user. The users' main keys are encrypted using the Rivest-Shamir-Adleman (RSA) method.Consequently, the proposed scheme will protect users privacy and prevent the unauthorized access to users’ data and information by the social network provider and unauthorized users by comparing results of the proposed scheme with other similar investigated schemes. In addition, it will be resistant to common attacks (man-in-the-middle attack and replay attack). Also, according to our findings, it is a safe and comprehensive scheme with high security and practicality while needing less time for both encrypting and decrypting the users messages.

PGN-LM Model and Forcing-Seq2Seq Model: Multiple Automatic Models of Title Generation for Natural Text using Deep Learning

In the current era, the amount of information from the Internet in general and the electronic press in particular has increased rapidly and has extremely useful information value in all aspects of life, many popular users have posted several high-quality writings as casual blogs, notes or reviews. Some of them are even selected by editors to be published in professional venues. However, the original posts often come without titles, which are needed to be manually added by the editing teams. This task would be done automatically, with the recent advancement of AI techniques, especially deep learning. Even though auto-title can be considered as a specific case of text summarization, this job poses some major different requirements. Basically, a title is generally short but it needs to capture major content while still maintaining the writing style of the original document. To fulfill those constraints, we introduce PGN-LM Model, an architecture evolved from the Pointer Generator Network, with the ability to solve Out-of-Vocabulary problems that traditional Seq2Seq models cannot handle, and at the same time combined with language modeling techniques. In addition, we also introduce a model called Forcing-Seq2Seq Model, an enhanced Seq2Seq architecture, in which the classical TF-IDF scores are incorporated with Named Entity Recognition method to identify the major keywords of the original texts. To enforce the appearance of those keywords in the generated titles, the specific Teacher Forcing mechanism combined with the language model technique are employed. We have tested our approaches with real datasets and obtained promising initial results, on both metrics of machine and human perspectives.

Object-aware Policy Network in Deep Recommender Systems

Deep learning has been successfully applied in the recommender system. Low-dimensional dense embedding is typically used to represent the feature of users and items. To optimize the model, some models propose to dynamically search the embedding size based on the popularity of different users and items. However, these models ignore the interaction between the user and the item which will hinder the optimization of the features in embedding. In this paper, we propose Object-aware Policy Network (OPN) and introduces an object-aware method that is used for optimizing the features in embedding. We evaluate our model on the two real-world benchmark datasets. With less than 10% increased time consumption in all experiments, the results show that our proposed model is able to improve the performance of binary classification task by a margin of 0.30 and multiclass classification task by a margin of 0.35 compared to the best accuracies achieced by baselines on different datasests.

Analysis of Scholarly Communications of Students on Twitter

As part of an applied activity of a university course in Japan, a total of 65 students posted messages on Twitter regarding global issues to expand the scope of scholarly communication with social users on Twitter. The students’ motivations to learn about world issues were evaluated using a standard questionnaire developed under the attention, relevance, confidence, and satisfaction (ARCS) model. Thereafter, students’ connections were categorized into three types: crossing, private, and isolation. A one-way analysis of variance test and multiple comparison test showed a significant result—students with higher levels of motivation could communicate with users in other communities of the crossing type. In this study, we tried for the first time to utilize social network analysis and exponential random graph models to investigate the motivation and structures of scholarly connections on Twitter. Although students selected social users as targets for tweeting, most students who had reciprocal ties with these users did not contribute to the expansion of the community. Since the requirement to be a popular user was explicated by focusing on a remarkable student in the community, the inner-community enhancement of increasing ties among students was concluded for developing social capital in the community.

Visual Sentiment Analysis from Disaster Images in Social Media

The increasing popularity of social networks and users’ tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content have opened new opportunities and challenges in sentiment analysis. While sentiment analysis of text streams has been widely explored in the literature, sentiment analysis from images and videos is relatively new. This article focuses on visual sentiment analysis in a societally important domain, namely disaster analysis in social media. To this aim, we propose a deep visual sentiment analyzer for disaster-related images, covering different aspects of visual sentiment analysis starting from data collection, annotation, model selection, implementation, and evaluations. For data annotation and analyzing people’s sentiments towards natural disasters and associated images in social media, a crowd-sourcing study has been conducted with a large number of participants worldwide. The crowd-sourcing study resulted in a large-scale benchmark dataset with four different sets of annotations, each aiming at a separate task. The presented analysis and the associated dataset, which is made public, will provide a baseline/benchmark for future research in the domain. We believe the proposed system can contribute toward more livable communities by helping different stakeholders, such as news broadcasters, humanitarian organizations, as well as the general public.

SAFE VAULT: A PASSWORD MANAGER

Text passwords have long been the most popular user authentication method, with a huge number of Internet services using them. Users are faced with the almost insurmountable difficulty of generating and managing a huge number of site-unique and strong (i.e., non-guessable) passwords if they follow suggested practice. A password generator, i.e., a client-side technique that generates (and regenerates) site-specific strong passwords on demand with minimal user input, is one solution to handle this problem. Safe Vault, a password generating scheme previously defined as part of a wider review of such schemes, is detailed specified and analyzed in this work. Safe Vault was created to address flaws that had previously been recognized in password generators, and it uses unique approaches to do so. Safe Vault allows you to create passwords that meet critical real-world needs, such as required password updates, pre-specified passwords, and passwords that meet site-specific requirements.

Federated learning empowered mobility-aware proactive content offloading framework for fog radio access networks

Proactive content caching has emerged as a promising solution to cope with exponentially increasing mobile data traffic. The popular user contents can be cached near the network edge for faster retrieval and processing. Current state-of-the-art approaches adopt a centralized model training mechanism that requires high communication and data exchange overheads to predict content popularity. Moreover, these approaches fail to deal with the dynamicity of the environment since they do not take into account the users’ mobility information and are unable to incorporate content offload timings. In this paper, we address these limitations by proposing a novel federated learning-based Mobility and Demand-aware Proactive Content Offloading (MDPCO) framework. MDPCO exploits distributed learning strategies and capitalizes on users’ mobility and demand information for proactive content offloading. Extensive simulations are carried out to validate the efficacy of MDCPO against local and cloud-based models. Our proposed model yields an average performance improvement of 6.7% in comparison to the cloud-based model. Furthermore, with the increase in the number of fog servers, the MDPCO achieves a 9.8% higher data offloading ratio and 1.18% increase in the downlink rates while being more energy-efficient than cloud-based approaches.

The Influence of Work Zone Management on User Carbon Dioxide Emissions in Life Cycle Assessment on Highway Pavement Maintenance

The higher contribution of traffic delay to environmental impacts is urging the highway agencies to take work zone management into the maintenance schemes decision-making. Aiming to understand the role of work zone management in user CO2 emissions reduction, this paper firstly developed a practical methodological framework of traffic delay-related CO2 emissions caused by highway maintenance based on a popular life cycle user cost analysis approach in regard of the microscopic vehicle operation analysis. The method was applied in an actual freeway flexible pavement with 15-year design life in Shaanxi Province, China, covering three types of preventive maintenance, correction maintenance, and rehabilitation. In addition, the impacts of key inputs of proposed method on work zone user CO2 emissions results were checked. The results show that traffic delay attributes to 29.4% of total CO2 emissions of the life cycle of highway pavement maintenance, and 51.8% of work zone user CO2 emissions result from preventive maintenance, especially from micro vehicle operations including speed change and queue near work zone (62% of total work zone user CO2 emissions). The work zone management alternative strategies related to less traffic volume or higher highway capacity including vehicle type limitation and the limited work zone speed have an advantage in reducing the work zone CO2 emissions over changing work zone length or work zone timing. The findings in this paper may present a useful tool and reference for robustly supporting the decision-making on highway maintenance carbon mitigation in work zone traffic.

Analyzing Political Polarization on Social Media by Deleting Bot Spamming

Social media platforms are part of everyday life, allowing the interconnection of people around the world in large discussion groups relating to every topic, including important social or political issues. Therefore, social media have become a valuable source of information-rich data, commonly referred to as Social Big Data, effectively exploitable to study the behavior of people, their opinions, moods, interests and activities. However, these powerful communication platforms can be also used to manipulate conversation, polluting online content and altering the popularity of users, through spamming activities and misinformation spreading. Recent studies have shown the use on social media of automatic entities, defined as social bots, that appear as legitimate users by imitating human behavior aimed at influencing discussions of any kind, including political issues. In this paper we present a new methodology, namely TIMBRE (Time-aware opInion Mining via Bot REmoval), aimed at discovering the polarity of social media users during election campaigns characterized by the rivalry of political factions. This methodology is temporally aware and relies on a keyword-based classification of posts and users. Moreover, it recognizes and filters out data produced by social media bots, which aim to alter public opinion about political candidates, thus avoiding heavily biased information. The proposed methodology has been applied to a case study that analyzes the polarization of a large number of Twitter users during the 2016 US presidential election. The achieved results show the benefits brought by both removing bots and taking into account temporal aspects in the forecasting process, revealing the high accuracy and effectiveness of the proposed approach. Finally, we investigated how the presence of social bots may affect political discussion by studying the 2016 US presidential election. Specifically, we analyzed the main differences between human and artificial political support, estimating also the influence of social bots on legitimate users.

Free Papers Compiled

Free Papers Compiled

Migraine and epilepsy drugs promoted to under-18s as weight-loss aids on TikTok

An investigation into one of the UK’s fastest growing social media platforms has found that several prescription drugs are being promoted to teenagers searching for diet pills. The analysis of the top 100 most popular user posts under the #dietpills hashtag — carried out in August 2022 registered as a female aged 16 years — […]

Pengaruh Kesadaran Keamanan Informasi dan Privasi Jaringan Sosial Terhadap Perilaku Perlindungan Privasi pada Para Pengguna Jaringan Sosial

Social networks are communication media that are used for sharing information and communicating. However, the popularity of social networks's user can not cover the anxiety about the privacy of user information data. This study aims to examine the factors that influence user anxiety about data security and security behavior. This study consists of five variables, namely response efficacy, self-efficacy, security awareness, and perceived security threat as an independent variable and internet user information privacy concern, and security behavior as the dependent variable. This study is a quantitative study that collects data and distributes questionnaires. The data in the study were analyzed through structural equation modeling (SEM) techniques with the help of SPSS. From the results of the study, this study proves that user perceptions of security threats (perceived security threats) have a significant effect on user anxiety about data privacy security when accessing social networks (Internet Users' Information Privacy Concern). This research provides insight to social network users to be more selective in managing data information security in providing personal data and taking protective actions against security behavior.

Preference for Number of Friends in Online Social Networks

Preferences or dislikes for specific numbers are ubiquitous in human society. In traditional Chinese culture, people show special preference for some numbers, such as 6, 8, 10, 100, 200, etc. By analyzing the data of 6.8 million users of Sina Weibo, one of the largest online social media platforms in China, we discover that users exhibit a distinct preference for the number 200, i.e., a significant fraction of users prefer to follow 200 friends. This number, which is very close to the Dunbar number that predicts the cognitive limit on the number of stable social relationships, motivates us to investigate how the preference for numbers in traditional Chinese culture is reflected on social media. We systematically portray users who prefer 200 friends and analyze their several important social features, including activity, popularity, attention tendency, regional distribution, economic level, and education level. We find that the activity and popularity of users with the preference for the number 200 are relatively lower than others. They are more inclined to follow popular users, and their social portraits change relatively slowly. Besides, users who have a stronger preference for the number 200 are more likely to be located in regions with underdeveloped economies and education. That indicates users with the preference for the number 200 are likely to be vulnerable groups in society and are easily affected by opinion leaders.

The Road to Popularity: The Dilution of Growing Audience on Twitter

On social media platforms, like Twitter, users are often interested in gaining more influence and popularity by growing their set of followers, aka their audience. Several studies have described the properties of users on Twitter based on static snapshots of their follower network. Other studies have analyzed the general process of link formation. Here, rather than investigating the dynamics of this process itself, we study how the characteristics of the audience and follower links change as the audience of a user grows in size on the road to user's popularity. To begin with, we find that the early followers tend to be more elite users than the late followers, i.e., they are more likely to have verified and expert accounts. Moreover, the early followers are significantly more similar to the person that they follow than the late followers. Namely, they are more likely to share time zone, language, and topics of interests with the followed user. To some extent, these phenomena are related with the growth of Twitter itself, wherein the early followers tend to be the early adopters of Twitter, while the late followers are late adopters. We isolate, however, the effect of the growth of audiences consisting of followers from the growth of Twitter's user base itself. Finally, we measure the engagement of such audiences with the content of the followed user, by measuring the probability that an early or late follower becomes a retweeter.