Popular Users Research Articles

Transfer learning-accelerated network slice management for next generation services

The current trend in user services places an ever-growing demand for higher data rates, near-real-time latencies, and near-perfect quality of service. To meet such demands, fundamental changes were made to the traditional radio access network (RAN), introducing Open RAN (O-RAN). This new paradigm is based on a virtualized and intelligent RAN architecture. However, with the increased complexity of 5G applications, traditional application-specific placement techniques have reached a bottleneck. Our paper presents a Transfer Learning (TL) augmented Reinforcement Learning (RL) based networking slicing (NS) solution targeting more effective placement and improving downtime for prolonged slice deployments. To achieve this, we propose an approach based on creating a robust and dynamic repository of specialized RL agents and network slices geared towards popular user service types such as eMBB, URLLC, and mMTC. The proposed solution consists of a heuristic-controlled two-module-based ML Engine and a repository. The objective function is formulated to minimize the downtime incurred by the VNFs hosted on the commercial-off-the-shelf (COTS) servers. The performance of the proposed system is evaluated compared to traditional approaches using industry-standard 5G traffic datasets. The evaluation results show that the proposed solution consistently achieves lower downtime than the traditional algorithms.

New custom rating for improving recommendation system performance

Recommendation system is currently attracting the interest of many explorers. Various new businesses have surfaced with the rise of online marketing (E-Commerce) in response to Covid-19 pandemic. This phenomenon allows recommendation items through a system called Collaborative Filtering (CF), aiming to improve shopping experience of users. Typically, the effectiveness of CF relies on the precise identification of similar profile users by similarity algorithms. Traditional similarity measures are based on the user-item rating matrix. Approximately, four custom ratings (CR) were used along with a new rating formula, termed New Custom Rating (NCR), derived from the popularity of users and items in addition to the original rating. Specifically, NCR optimized recommendation system performance by using the popularity of users and items to determine new ratings value, rather than solely relying on the original rating. Additionally, the formulas improved the representativeness of the new rating values and the accuracy of similarity algorithm calculations. Consequently, the increased accuracy of recommendation system was achieved. The implementation of NCR across four CR algorithms and recommendation system using five public datasets was examined. Consequently, the experimental results showed that NCR significantly increased recommendation system accuracy, as evidenced by reductions in RMSE, MSE, and MAE as well as increasing FCP and Hit Rate. Moreover, by combining the popularity of users and items into rating calculations, NCR improved the accuracy of various recommendation system algorithms reducing RMSE, MSE, and MAE up to 62.10%, 53.62%, 65.97%, respectively, while also increasing FCP and Hit Rate up to 11.89% and 31.42%, respectively.

Arabic Spam Tweets Classification: A Comprehensive Machine Learning Approach

Nowadays, one of the most common problems faced by Twitter (also known as X) users, including individuals as well as organizations, is dealing with spam tweets. The problem continues to proliferate due to the increasing popularity and number of users of social media platforms. Due to this overwhelming interest, spammers can post texts, images, and videos containing suspicious links that can be used to spread viruses, rumors, negative marketing, and sarcasm, and potentially hack the user’s information. Spam detection is among the hottest research areas in natural language processing (NLP) and cybersecurity. Several studies have been conducted in this regard, but they mainly focus on the English language. However, Arabic tweet spam detection still has a long way to go, especially emphasizing the diverse dialects other than modern standard Arabic (MSA), since, in the tweets, the standard dialect is seldom used. The situation demands an automated, robust, and efficient Arabic spam tweet detection approach. To address the issue, in this research, various machine learning and deep learning models have been investigated to detect spam tweets in Arabic, including Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and Long-Short Term Memory (LSTM). In this regard, we have focused on the words as well as the meaning of the tweet text. Upon several experiments, the proposed models have produced promising results in contrast to the previous approaches for the same and diverse datasets. The results showed that the RF classifier achieved 96.78% and the LSTM classifier achieved 94.56%, followed by the SVM classifier that achieved 82% accuracy. Further, in terms of F1-score, there is an improvement of 21.38%, 19.16% and 5.2% using RF, LSTM and SVM classifiers compared to the schemes with same dataset.

Explainable assessment of financial experts’ credibility by classifying social media forecasts and checking the predictions with actual market data

Social media include diverse interaction metrics related to user popularity, the most evident example being the number of user followers. The latter has raised concerns about the credibility of the posts by the most popular creators. However, most existing approaches to assess credibility in social media strictly consider this problem a binary classification, often based on a priori information, without checking if actual real-world facts back the users’ comments. In addition, they do not provide automatic explanations of their predictions to foster their trustworthiness. In this work, we propose a credibility assessment solution for financial creators in social media that combines Natural Language Processing and Machine Learning. The reputation of the contributors is assessed by automatically classifying their forecasts on asset values by type and verifying these predictions with actual market data to approximate their probability of success. The outcome of this verification is a continuous credibility score instead of a binary result, an entirely novel contribution by this work. Moreover, social media metrics (i.e., user context) are exploited by calculating their correlation with the credibility rankings, providing insights on the interest of the end-users in financial posts and their forecasts (i.e., drop or rise). Finally, the system provides natural language explanations of its decisions based on a model-agnostic analysis of relevant features.

What makes UGC more popular on social media platforms? Insights from information adoption theory

ABSTRACT It is becoming an important marketing strategy for enterprises to utilise user-generated content (UGC) to build brands and promote products on social media platforms, but little is known about the influence mechanism of the popularity of UGC related to brands and products. Drawing upon information adoption theory, this study developed a conceptual model to explain what makes UGC more popular in social media context. Using a dataset of 16,974 records related to the UGC about products from Xiaohongshu app, the most popular user experience sharing platform in China, the empirical study indicates that factors extracted from information quality and source credibility have significant effects on UGC popularity. Interestingly, text length and strength of text sentiment have different effects moderated by product types. In addition, media richness may lead to information redundancy and weaken the effect of text length. The findings offer an insightful understanding into the mechanism of utilising UGC in product marketing on social media platforms.

EchoTap: Non-Verbal Sound Interaction with Knock and Tap Gestures

The growing demand for highly accessible interaction technologies to effectively interact with smart devices has led to the increasing popularity of voice user interfaces (VUIs). However, VUIs face interpretation challenges stemming from the variability of natural language input, such as speech clarity issues, linguistic variability, and speech impediments. As an alternative, non-verbal sound-based interaction techniques emerge as highly advantageous for smart device control, mitigating the inherent challenges of VUIs. In this article, we introduce EchoTap, a novel audio interface that harnesses the distinctive sound responses generated by knock and tap gestures on target objects. Employing deep neural networks, EchoTap recognizes both the type and location of these gestures based on their unique sound signatures. Through offline evaluation, EchoTap demonstrated competitive classification accuracy (88% on average) and localization precision (93% on average). Moreover, a user study involving 12 participants validated EchoTap’s practical effectiveness and user-friendliness in real-world scenarios. This study highlights EchoTap’s potential for various daily interaction contexts and discusses further design implications for leveraging auditory interfaces based on simple gestures.

A hybrid similarity model for mitigating the cold-start problem of collaborative filtering in sparse data

Similarity is a vital component for neighborhood-based collaborative filtering (CF). To improve the quality of recommendation, many similarity methods have been proposed and analyzed in recent decades. However, nearly all traditional similarity methods and many advanced similarity methods only utilize corated items among users to compute their similarity, which provides limited information in cold-start/sparse scenarios and yields misleading results. In addition, although a few advanced hybrid similarity models consider items beyond corated items, which can partly mitigate the above limitation, they still have drawbacks, such as disregarding penalizing noncorated items that have many disadvantages. In this paper, we explore a new robust hybrid similarity model, namely Wasserstein distance-based CF (WCF) model, for mitigating the cold-start problem of CF in sparse data. Specifically, we measure item similarity via the Wasserstein distance, which can help circumvent the drawbacks in the Bhattacharyya coefficient and KL divergence that are used in the literature, and is thus more robust in a cold-start/sparse scenario. Besides, we further design a new multiplicative user similarity formula which identifies all noncorated items as a whole to prioritize the importance of corated items and impair the negative effects of noncorated items, which will also play an important role in a cold-start/sparse scenario. In addition, we also propose two novel heuristic similarity factors to impair the negative effects of popular users and items as supplements. We conduct extensive experiments on five real-world benchmark recommendation datasets to test WCF. The experimental results show the superiority of WCF over other existing similarity methods in cold-start/sparse scenarios.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

Privacy-Preserving Serverless Edge Learning With Decentralized Small-Scale Mobile Data

In next-generation (i.e., 6G) networking systems, the data-driven approach will play an essential role, being an efficient tool for networking system management and bringing popular user applications. With those unprecedented and novel usages, existing frameworks fail to consider the complex nature of the next-generation networking system and consequently fail to be applied to future communication systems directly. Moreover, existing frameworks also fail to support popular privacy-preserving learning strategies efficiently by presenting special designs to respond to the resource-demanding nature of the aforementioned strategies. To fill this gap, this paper extends conventional serverless platforms with serverless edge learning architectures, providing a mature and efficient distributed training framework by fully exploiting limited wireless communication and edge computation resources in the considered networking system with the following three features. Firstly, this framework dynamically orchestrates resources among heterogeneous physical units to efficiently fulfill privacy-preserving learning objectives. The design jointly considers learning task requests and underlying infrastructure heterogeneity, including last-mile transmissions, computation abilities of edge and cloud computing centers, and loading status of infrastructure. Secondly, the proposed framework can easily work with data-driven approaches to improve network management efficiency, realizing AI for network promise of next-generation networking systems to provide efficient network automation. Lastly, to significantly reduce distributed training overheads, small-scale data training is proposed by integrating with a general, simple data classifier. This low-load enhancement can seamlessly work with various distributed deep models in the proposed framework to improve communications and computation efficiencies during the training phase. Based on the above innovations, open challenges, and future research directions encourage the research community to develop efficient privacy-preserving learning techniques.

Political context of the European vaccine debate on Twitter

At the beginning of the COVID-19 pandemic, fears grew that making vaccination a political (instead of public health) issue may impact the efficacy of this life-saving intervention, spurring the spread of vaccine-hesitant content. In this study, we examine whether there is a relationship between the political interest of social media users and their exposure to vaccine-hesitant content on Twitter. We focus on 17 European countries using a multilingual, longitudinal dataset of tweets spanning the period before COVID, up to the vaccine roll-out. We find that, in most countries, users’ endorsement of vaccine-hesitant content is the highest in the early months of the pandemic, around the time of greatest scientific uncertainty. Further, users who follow politicians from right-wing parties, and those associated with authoritarian or anti-EU stances are more likely to endorse vaccine-hesitant content, whereas those following left-wing politicians, more pro-EU or liberal parties, are less likely. Somewhat surprisingly, politicians did not play an outsized role in the vaccine debates of their countries, receiving a similar number of retweets as other similarly popular users. This systematic, multi-country, longitudinal investigation of the connection of politics with vaccine hesitancy has important implications for public health policy and communication.

ИССЛЕДОВАНИЕ СТРАТЕГИЙ БЕЗОПАСНОСТИ ИНФОРМАЦИОННЫХ ТЕХНОЛОГИЙ КОМПЬЮТЕРНЫХ СЕТЕЙ

The improvement of computer network technologies has brought convenience to people’s lives. With the growing popularity of Internet users, more attention has been paid to the security of computer networks. The security problems of computer networks are mainly related to the strong development of computer networks, the presence of insecurity and the possibility of disclosure of user information, the occurrence of leaks, intrusion risks and other phenomena. Therefore, we must pay attention to information management of computer networks, strengthen protection and ensure the safety of users during use.

The Investigation of Soccer Fans’ Social Media Using Motivations

Social media has become an indispensable part of the daily lives of most people who have come into contact with technology, and is one of the basic sources of communication and free expression. The popularity and number of social media users, which shape people’s behavior and enable online socialization, is increasing day by day. This increase can be largely attributed to the increasing number of computers, tablets, and especially smartphones, as well as the decreasing age of users. From this point of view, the aim of this study is to determine the motives of fans of the teams of the Turkish Football Super League for using social media. In this framework, a total of 460 people who were selected from the population according to the principle of random sampling constitute the sample of the study. In the study, the “Personal Information Form” prepared by the researcher was used to determine the demographic characteristics of the participants, and the “Motivations of Fans to Follow Their Teams on Social Media Scale” was used to investigate the fans’ motives for using social media. The descriptive statistics of the variables were conducted in the form of percentages (%), frequency, mean, and standard deviation using SPSS 25 package program for the analysis of the research. On the other hand, the t-test for independent samples and analysis of variance, which are parametric tests, were used to test the relationships between the variables. The result is that in the study of the motivations of fans to follow the accounts of the teams they support in social media, the gathering of information, sharing of information and the perception of being a fan are the most important. Therefore, it is recommended that clubs get to know their fan base and enrich the content of strongly preferred social media accounts according to the identified motivations to ensure the active participation of fans in this process.

A Systematic Literature Review of Predictors of Social Media Popularity

A growing area of research has examined the individual behaviors and social antecedents that enable and constrain the popularity of social media users. This systematic review gathers and summarizes 68 naturalistic studies that measure popularity based on users’ reach (e.g., followers, fans and subscribers) or engagement (e.g., likes, comments and shares) on multiple platforms. It draws on Barnlund’s (2008) transactional model of communication to organize the literature and provides a roadmap for future research by identifying areas of the research that are characterized by consensus and disagreement. It also reveals a gap in the literature. Previous research focuses on communication strategies that maximize reach and engagement and provides less evidence of social structural influences on popularity. More research is needed to understand how the social, economic, and cultural characteristics of users affect their success.

Subjective data models in bioinformatics and how wet lab and computational biologists conceptualise data

Biological science produces “big data” in varied formats, which necessitates using computational tools to process, integrate, and analyse data. Researchers using computational biology tools range from those using computers for communication, to those writing analysis code. We examine differences in how researchers conceptualise the same data, which we call “subjective data models”. We interviewed 22 people with biological experience and varied levels of computational experience, and found that many had fluid subjective data models that changed depending on circumstance. Surprisingly, results did not cluster around participants’ computational experience levels. People did not consistently map entities from abstract data models to the real-world entities in files, and certain data identifier formats were easier to infer meaning from than others. Real-world implications: 1) software engineers should design interfaces for task performance, emulating popular user interfaces, rather than targeting professional backgrounds; 2) when insufficient context is provided, people may guess what data means, whether or not they are correct, emphasising the importance of contextual metadata to remove the need for erroneous guesswork.

Popularity Bias in Online Dating Platforms: Theory and Empirical Evidence

Problem definition: Generating recommendations of compatible dating partners is a challenging task for online dating platforms because uncovering users’ idiosyncratic preferences is difficult. Thus, platforms tend to recommend popular users to others more frequently than unpopular users. This paper studies such popularity bias in an online dating platform’s recommendations and its consequences for users’ likelihood of finding dating partners. Methodology/results: Motivated by the empirical evidence that a user’s chance of being recommended by the platform’s algorithm increases significantly with the user’s popularity, we study an online dating platform’s incentive that generates popularity bias by modeling the platform’s recommendations and users’ subsequent interactions in a three-stage matching game. Our analysis shows that the recommendations that maximize the platform’s revenue and those that maximize the number of successful matches between users are not necessarily at odds, even though the former leads to a higher bias against unpopular users. Unbiased recommendations result in significantly lower revenue for the platform and fewer matches when users’ implicit cost of evaluating incoming messages is low. Popular users help the platform generate more revenue and a higher number of successful matches as long as these popular users do not become “out of reach.” We validate our theoretical results by running simulations of the platform based on a machine learning–based predictive model that estimates users’ behavior. Managerial implications: Our result indicates that an online dating platform can increase revenue and users’ chances of finding dating partners simultaneously with a certain degree of bias against unpopular users. Online dating platforms can use our theoretical results to understand user behavior and our predictive model to improve their recommendation systems (e.g., by selecting a set of users leading to the highest probabilities of matching or other revenue-generating interactions). Supplemental Material: The online appendices are available at https://doi.org/10.1287/msom.2022.0132 .

COVID-19 and social media: Beyond polarization.

The COVID-19 pandemic brought upon a massive wave of disinformation, exacerbating polarization in the increasingly divided landscape of online discourse. In this context, popular social media users play a major role, as they have the ability to broadcast messages to large audiences and influence public opinion. In this article, we make use of openly available data to study the behavior of popular users discussing the pandemic on Twitter. We tackle the issue from a network perspective, considering users as nodes and following relationships as directed edges. The resulting network structure is modeled by embedding the actors in a latent social space, where users closer to one another have a higher probability of following each other. The results suggest the existence of two distinct communities, which can be interpreted as "generally pro" and "generally against" vaccine mandates, corroborating existing evidence on the pervasiveness of echo chambers on the platform. By focusing on a number of notable users, such as politicians, activists, and news outlets, we further show that the two groups are not entirely homogeneous, and that not just the two poles are represented. To the contrary, the latent space captures an entire spectrum of beliefs between the two extremes, demonstrating that polarization, while present, is not the only driver of the network, and that more moderate, "central" users are key players in the discussion.

Understanding information disclosures and privacy sensitivity on short-form video platforms: An empirical investigation

As a new social media platform, short-form video apps integrate the dual functions of e-commerce and social networking. The proliferation and high penetration rate of this kind of app have resulted in online user concerns about the privacy security problems exposed by such platforms. This study focuses on better understanding the impact mechanism of private information disclosure behaviors and obtained benefits on privacy concerns of short-form video platform users. We analyzed the behavioral and privacy setting data of 35,456 users registered on the TikTok platform. The results demonstrated a correlation between use behaviors (e.g., participation and interest) and privacy sensitivity, among which popularity, as a form of external feedback, played a mediating role. In addition, the official certification provided to users by the platform moderated the impact of user popularity on privacy sensitivity. This study enriches the understanding of the correlation between privacy concerns and information disclosure behavior and suggests practical implications for the design of privacy settings and protections by short-form video platforms.

The Daily Me Versus the Daily Others: How Do Recommendation Algorithms Change User Interests? Evidence from a Knowledge-Sharing Platform

Recommender systems on online platforms are often accused of polarizing user attention and consumption. The authors examine this phenomenon using a quasi-experiment conducted by Zhihu, the largest online knowledge-sharing platform (or Q&A community) in China. Zhihu originally used a content-based filtering algorithm, which recommends content to users on the basis of the topics to which each user has subscribed. After more than a year, Zhihu moved to a social filtering algorithm, which recommends content with which users’ social connections are already engaged. The authors find that this algorithm change increased the creation of social ties by approximately 15% but decreased question subscriptions by 20% and answer contributions by 23%. The authors show that users’ increased social interests mainly involved following popular users, leading to a greater concentration of social interests on the platform. However, users’ topical interests became less concentrated, as popular topics received significantly fewer subscribers than unpopular topics. The authors explain these findings by exploring the underlying mechanism. They show that compared with content-based filtering algorithms, social filtering algorithms are more likely to expose general users to content consumed by their followees, who are more interested in niche topics than general users are.

Cash in the Trash? An Austrian Perspective on Mobile Payment Adoption

Despite offering many benefits to consumers, merchants, banks, and other providers, mobile payment still has not found widespread acceptance in Austria, for example, in 2015, 15% of Austrian consumers used the Internet or a mobile device for payments and 16% made contactless payments at least once a week. This study sheds light on this issue by taking a consumer perspective and investigating the factors that foster or hinder mobile payment adoption. Three popular user acceptance models were compared and in the end, a unified theory of acceptance and use of technologybased model (UTAUT2) was chosen. The developed model was composed of 12 factors (behavioral intention, utilitarian performance expectancy, hedonic performance expectancy, effort expectancy, social influence, facilitating conditions, perceived risk, perceived security, privacy concerns, trust, cost, personal innovativeness) and three moderators (age, gender, experience). The proposed model was tested using data from 158 Austrian consumers and analyzed with partial least squares structural equation modeling (PLS-SEM). The results showed that 68% of consumers’ intention to use mobile payments could be explained, making it a promising model in the mobile payment research area based on the baseline data. Perceived risk and hedonic performance expectancy are the greatest drivers with psychological risk (a lack of fit with one’s self-image) as the most important risk dimension. The results suggest that mobile payment possesses lifestyle characteristics and its usage needs to be fun in order for consumers to prefer it to cash and cards.
 Keywords: mobile payment adoption, unified theory of acceptance and use of technology, user acceptance models, partial least squares structural equation modeling

Hashtag recommendation for enhancing the popularity of social media posts.

Social media has gained huge importance in our lives wherein there is an enormous demand of getting high social popularity. With the emergence of many social media platforms and an overload of information, attaining high popularity requires efficient usage of hashtags, which can increase the reachability of a post. However, with little awareness about using appropriate hashtags, it becomes the need of the hour to build an efficient system to recommend relevant hashtags which in turn can enhance the social popularity of a post. In this paper, we thus propose a novel method hashTag RecommendAtion for eNhancing Social popularITy to recommend context-relevant hashtags that enhance popularity. Our proposed method utilizes the trending nature of hashtags by using post keywords along with the popularity of users and posts. With the prevalent evaluation techniques of this field being quite unreliable and non-uniform, we have devised a novel evaluation algorithm that is more robust and reliable. The experimental results show that our proposed method significantly outperforms the current state-of-the-art methods.