Popularity Of Online Social Networks Research Articles

Exploring equity perception of electric vehicles from a social media perspective

Electric vehicles (EVs) are considered a promising solution for climate change. However, their introduction has raised uncertainties in transportation equity, reflecting the diverse public perceptions and discussions on this issue. Understanding these perceptions is crucial for integrating equity into EV adoption effectively. To bridge this gap, our study used a social media approach to conduct a cross-platform equity analysis on two popular online social networks (OSNs): Twitter and Reddit. We identified 22,533 tweets and 12,318 Reddit comments relevant to equity from all EV-related discussions from 2011 to 2022. Our analysis utilized different features of the two OSNs, including verified account identity and Reddit communities, to present a comprehensive picture of equity perception in social media. Additionally, we used machine learning methods to infer Twitter users’ demographic attributes and integrate user demographics into equity analysis. Our findings show that political accounts are the center of attention on Twitter, but they are accompanied by negative attitudes. Many fringe communities are highly active in equity discussions on Reddit. Our study also found that younger people pay less attention and hold more negative sentiments towards equity discussions, while male and female users have similar interests in equity and similar sentiment. Notably, the proportion of equity discussions remains low in the overall EV corpus, despite a significant increase in recent years.

Community-enhanced Link Prediction in Dynamic Networks

The growing popularity of online social networks is quite evident nowadays and provides an opportunity to allow researchers in finding solutions for various practical applications. Link prediction is the technique of understanding network structure and identifying missing and future links in social networks. One of the well-known classes of methods in link prediction is a similarity-based method, which uses local and global topological information of the network to predict missing links. Some methods also exist based on quasi-local features to achieve a trade-off between local and global information on static networks. These quasi-local similarity-based methods are not best suited for considering community information in dynamic networks, failing to balance accuracy and efficiency. Therefore, a community-enhanced framework is presented in this article to predict missing links on dynamic social networks. First, a link prediction framework is presented to predict missing links using parameterized influence regions of nodes and their contribution in community partitions. Then, a unique feature set is generated using local, global, and quasi-local similarity-based as well as community information-based features. This feature set is further optimized using scoring-based feature selection methods to select only the most relevant features. Finally, four machine learning-based classification models are used for link prediction. The experiments are performed on six well-known dynamic networks and three performance metrics, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.

DIPA2

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

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

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

Competitive information propagation considering local-global prevalence on multi-layer interconnected networks

The popularity of online social networks (OSNs) promotes the co-propagation of multiple types of information. And there exist inevitably competitive interactions between these information, which will significantly affect the spreading trend of each information. Besides, the coupled topology of multi-layer interconnects exhibited in OSNs will also increase the research complexity of information propagation dynamics. To effectively address these challenges, we propose a novel competitive information propagation model on multi-layer interconnected networks, where the tendency of an individual to become a positive or negative spreader depends on the weighted consideration of local and global prevalence. Then the basic reproduction number is calculated via next-generation matrix method. And under the critical conditions of the basic reproduction number, the asymptotic stability of information-free and information-endemic equilibria is theoretically proven through Lyapunov stability theory. Besides, an optimal control problem involving two heterogeneous controls is formulated, aiming at achieving the best suppression performance of negative information with the minimum control cost. According to Cesari theorem and Pontryagin minimum principle, the existence and analytical formulation of optimal solutions are derived. Extensive numerical experiments are conducted to prove the correctness of our theoretical results, and evaluate the effectiveness of our proposed control strategies. This study can provide useful insights into the modeling and control of multiple information propagation considering multi-layer network topology and individual adaptive behavior.

Enhanced Spam Detection System for Twitter Social Networking Platform

Twitter social site is one of the most popular Online Social Networking Site (OSN) used by popular people such as Ministers, businessman, large companies, actors to share their information. In this site, around 500 million of tweets are posted monthly by the total 313 million Twitter active users. The widespread of Twitter has drawn the interest of spammers. These malicious actors exploit the platform for various nefarious purposes, including monitoring authentic users, disseminating harmful software, and promoting their agendas through URLs embedded in tweets. They engage in tactics like secret following and unfollowing legitimate users, all with the intent of gathering sensitive information.To resolve this problem, a secure spam detection based on machine learning approach is designed. The designed used stop word removal, word to vector model to refined and dimensionally reduced the data. To enhance the quality of the data Cosine similarity is also been applied to measure the similarity score among the tweets and based upon that Artificial Neural Network (ANN) is trained. Later on, it is used to test the efficiency by examining the performance parameters in terms of precision, recall and F-measure. Also, the comparative analysis has been performed to present the efficiency of the work. The average precision, recall and F measure of proposed spam detection model of 0.9252, 0.6107 and 0.734 are obtained.

Social Network Mental Disorders Detection

Abstract: With the rise in popularity of online social networks (OSNs), new terms such as Phubbing and Nomophobia have been introduced, and Social network mental illnesses (SNMDs) such as Information Overload and Net Compulsion have been noted Studies show that 1 in 8 Americans experience it suffer from problems internet use, as well as SNMD Overuse, depression, social withdrawal, and other negative behaviors can occur if SNMDs are socially relevant and frequent for them interactive users through online social media. Internet addiction (IAD) is a behavioral disorder, and research on depression in online social networks is increasing unlike most previous attention-grabbing research emphasizes individual behaviors and entries but does not fully explore social network structures and psychological possibilities we propose a learning approach for those that require a thorough examination of OSN topologies

Modeling communication asymmetry and content personalization in online social networks

The increasing popularity of online social networks (OSNs) attracted growing interest in modeling social interactions. On online social platforms, a few individuals, commonly referred to as influencers, produce the majority of content consumed by users and hegemonize the landscape of the social debate. However, classical opinion models do not capture this communication asymmetry. We develop an opinion model inspired by observations on social media platforms with two main objectives: first, to describe this inherent communication asymmetry in OSNs, and second, to model the effects of content personalization. We derive a Fokker–Planck equation for the temporal evolution of users’ opinion distribution and analytically characterize the stationary system behavior. Analytical results, confirmed by Monte-Carlo simulations, show how strict forms of content personalization tend to radicalize user opinion, leading to the emergence of echo chambers, and favor structurally advantaged influencers. As an example application, we apply our model to Facebook data during the Italian government crisis in 2019. Our work provides a flexible framework to evaluate the impact of content personalization on the opinion formation process, focusing on the interaction between influential individuals and regular users. This framework is interesting in the context of marketing and advertising, misinformation spreading, politics and activism.

Public perception of electric vehicles on Reddit and Twitter: A cross-platform analysis

Electrified mobility such as electric vehicles (EVs) is a promising solution to reduce carbon emissions in transportation and mitigate global warming. Understanding public perception of EVs can help better support their adoption. A previous study shows that online social networks (OSNs) such as Reddit can be a valuable source for studying public perceptions of EVs and provide different perspectives from traditional methods that leverage surveys, questionnaires, or interviews (Ruan and Lv, 2022). Our work aims to investigate this direction further through the following research question: Given the distinct mechanisms of various OSNs, can we obtain a more comprehensive picture of public perception of EVs by integrating the analysis from different platforms? Specifically, our study is based on EV-related discussions on two popular OSN platforms: Twitter and Reddit. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) and 7,383,327 Tweets between January 2011 and December 2020 and analyzed them from several perspectives. Our analysis shows that users have had different topic and sentiment patterns in EV-related discussions on the two platforms over the past decade. We also leverage the verified account information on Twitter to reveal that the most influential users are politicians and news media; however, the general public has very different conversation patterns with the two types of accounts — politicians seem to be increasingly (over) optimistic about EVs while the public may think differently.

Deep learning for comment spam and scams

As the popularity of online social networks has increased, so has the manner in which people purchase online. Online reviews on the purchasing of items or the provision of services have become the primary source of user opinion. When a substantial body of knowledge assesses a product or service, that body's effect on the market is significant. Because of this, concerns have been raised among manufacturers and merchants, who frequently compose rubbish reviews to promote or denigrate the quality of specific items or services in order to make a profit or maintain their reputation. You may choose to promote or denigrate certain products or services that have been targeted. This kind of commentary is known as "trash remark." Comment spam violates the interests of enterprises to a significant level, and it possesses a defensive mechanism that combines the "Deepfake" of artificial intelligence technology, which renders conventional security measures inapplicable. Deep learning is the approach that will be most effective in resolving this issue. Researchers assess this research according on how they extract features from the commentary dataset and how they extract features from the commentary dataset, employing a variety of methodologies and strategies to find solutions to the problems. The authors also looked at the key deep learning technologies that have been suggested as a solution to the issue of spam detection in the primary machine, as well as the performance of various deep learning technologies. At the same time, the fact that there is not a lot of spam online review data makes this sort of research a highly significant one. The purpose of this article is to offer an insightful and all-encompassing analysis of comparative research on the topic of spam detection that has recently been conducted.

Social Network Analysis: A Survey on Measure, Structure, Language Information Analysis, Privacy, and Applications

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

Hatemongers ride on echo chambers to escalate hate speech diffusion.

Recent years have witnessed a swelling rise of hateful and abusive content over online social networks. While detection and moderation of hate speech have been the early go-to countermeasures, the solution requires a deeper exploration of the dynamics of hate generation and propagation. We analyze more than 32 million posts from over 6.8 million users across three popular online social networks to investigate the interrelations between hateful behavior, information dissemination, and polarized organization mediated by echo chambers. We find that hatemongers play a more crucial role in governing the spread of information compared to singled-out hateful content. This observation holds for both the growth of information cascades as well as the conglomeration of hateful actors. Dissection of the core-wise distribution of these networks points towards the fact that hateful users acquire a more well-connected position in the social network and often flock together to build up information cascades. We observe that this cohesion is far from mere organized behavior; instead, in these networks, hatemongers dominate the echo chambers-groups of users actively align themselves to specific ideological positions. The observed dominance of hateful users to inflate information cascades is primarily via user interactions amplified within these echo chambers. We conclude our study with a cautionary note that popularity-based recommendation of content is susceptible to be exploited by hatemongers given their potential to escalate content popularity via echo-chambered interactions.

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> ) is calculated, which is an important parameter in the study of message propagation in OSN. If <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> < 1, the propagation of rumor in the OSN will be minimal; nevertheless, if <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> > 1, the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models. Extensive theoretical study and computation analysis have also been used to validate the proposed model.

Unveiling Qzone: A measurement study of a large-scale online social network

Nowadays, Online Social Network (OSN) is very important in maintaining relationships and disseminating information. Qzone, as one of the most popular OSNs in China, lacks detailed quantitative measurement and analysis. To the best of our knowledge, we are the first to conduct a large-scale measurement on Qzone. Firstly, we develop a dedicated Qzone Crawler System (Qzone-CS) and collect over a billion user and topology data. Subsequently, we measure user behavior and network topology characteristics by analyzing their posting and interactive behaviors. The results show that: 1) the temporal distribution of user behavior follows the periodic distribution, while user posting behavior follows the segmented power-law distribution. Among all the user behaviors on Qzone, the probability of posting Shuoshuo is the highest. Meanwhile, users prefer to comment on others’ blogs than to forward them. Unlike other OSNs, Qzone is a relatively closed system, which is not conducive to the dissemination of information. 2) Qzone is a small-world and scale-free community; the friendship network has higher clustering characteristic compared with the interactive network; out-degree in the interactive network has an obvious correlation with in-degree; and unlike other OSNs, Qzone is a heterogeneous network. Our results can help researchers better understand the user behavior and network topology characteristics of Qzone.

A survey on cross-media search based on user intention understanding in social networks

With the increasing popularity of online social networks, more and more people are posting information, updating their statuses, and searching for topics there. Massive cross-media big data has been gathered by online social networks, with high dynamics, context-sparsity, and cross-media semantic gaps. In addition, it can be challenging to understand users' search intentions in the setting of social networks. The above problems have brought severe challenges and obstacles to cross-media searches, which also attracted more and more attention on social networks. As a relatively new research topic and interest, the concept, methodology, and overall research idea of cross-media search based on user search intention understanding are not evident in the literature. The research also lacks a unified paradigm and relatively complete research ideas on social networks. To solve these problems, we reviewed more than 100 references based on our preliminary exploration and research experience in this field from the whole process involved. We also detailed methodology, datasets, evaluation indicators, experiment evaluation, and research trends and analyzed the challenges. These works will help beginners quickly establish research ideas and processes in this field, and enable them to focus on algorithm design without paying too much attention to datasets, evaluation metrics, and research frameworks. We believe this review will attract more researchers to focus on social network cross-media search based on user search intention understanding and benefit their work.

BotFinder: a novel framework for social bots detection in online social networks based on graph embedding and community detection

With the widespread popularity of online social networks (OSNs), the number of users has also increased exponentially in recent years. At the same time, Social bots, i.e. accounts that controlled by program, are also on the rise. Service providers of OSNs often use them to keep social networks active. Meanwhile, some social bots are also registered for malicious purposes. It is necessary to detect these malicious social bots to present a real public opinion environment. We propose BotFinder, a framework to detect malicious social bots in OSNs. Specifically, it combines machine learning and graph methods so that the potential features of social bots can be effectively extracted. Regarding the feature engineering, we generate second order features and use coding methods to encode variables that have high cardinality. These features make full use of both labelled and unlabeled samples. With respect to the graphs, we firstly generate node vectors through embedding method, following which the similarity between vectors of humans and bots can be further calculated; Then, we use an unsupervised method to diffuse labels and thus the performance can be improved again. To valid the performance of the proposed method, we conduct extensive experiments on the dataset provided by an artificial intelligence contest which is composed of over eight million records of users. Results show that our approach reaches a F1-score of 0.8850, which is much better compared to the state of the art.

Toward pragmatic modeling of privacy information propagation in online social networks

The growing popularity of online social networks (OSNs) in recent years has generated a lot of concern on personal privacy. One approach of protecting privacy in OSNs is to intervene in the flow of privacy information, making the study of the dynamics of privacy information propagation necessary for the design of effective privacy protection mechanisms. Although previous work on information propagation has produced some models, these models are not adequate for privacy information since they do not reflect the main characteristics of privacy information. In this paper, we propose a model for privacy information propagation. We first analyze the structural characteristics of privacy information and then design the model by incorporating these characteristics. A unique feature of the model is that it infers the privacy attitudes of the information recipients to the privacy concerning subject implicated in the privacy information to determine the forwarding decisions of the recipients. Thus, by mapping the heterogeneous tendency of information forwarding by the recipients to a limited number of privacy attitudes, the model can predict the decisions on forwarding privacy information and thus describe the macroscopic process of privacy information propagation. Results of the experiment based on real OSN datasets show that the proposed model can be used to learn both the scope and the trend of privacy information propagation in OSNs, demonstrating the importance of the privacy attitudes of recipients on privacy information propagation. The properties of the model are also studied through experiment to examine the impact of various factors on privacy information propagation in OSNs.

Systematic Machine Translation of Social Network Data Privacy Policies

With the growing popularity of online social networks, one common desire of people is to use of social networking services for establishing social relations with others. The boom of social networking has transformed common users into content (data) contributors. People highly rely on social sites to share their ideas and interests and express opinions. Social network sites store all such activities in a data form and exploit the data for various purposes, e.g., marketing, advertisements, product delivery, product research, and even sentiment analysis, etc. Privacy policies primarily defined in Natural Language (NL) specify storage, usage, and sharing of the user’s data and describe authorization, obligation, or denial of specific actions under specific contextual conditions. Although these policies expressed in Natural Language (NL) allow users to read and understand the allowed (or obliged or denied) operations on their data, the described policies cannot undergo automatic control of the actual use of the data by the entities that operate on them. This paper proposes an approach to systematically translate privacy statements related to data from NL into a controlled natural one, i.e., CNL4DSA to improve the machine processing. The methodology discussed in this work is based on a combination of standard Natural Language Processing (NLP) techniques, logic programming, and ontologies. The proposed technique is demonstrated with a prototype implementation and tested with policy examples. The system is tested with a number of data privacy policies from five different social network service providers. Predominantly, this work primarily takes into account two key aspects: (i) The translation of social networks’ data privacy policy and (ii) the effectiveness and efficiency of the developed system. It is concluded that the proposed system can successfully and efficiently translate any common data policy based on an empirical analysis performed of the obtained results.

Hybrid fake news detection technique with genetic search and deep learning

In recent years, there has been significant growth in the popularity of online social networks, with a corresponding increase in the volume of information shared over the web. Text, audio, and video content have been used as tools for spreading fake news on social networks, making it difficult to detect. In this paper, a fake news detection technique that uses genetic search for neural architecture selection and deep learning to classify news instances is proposed. From experimental results, our model achieved a detection rate of 89.6%, a false positive rate of 0.2, and a loss of 0.0982837, which is close to 0. To further verify our claims, statistical analysis showed a mean squared error of 0.258974358974359 and a root mean square error of 0.5088952337901771. These error rates are low relative to the size of the input and prove the effectiveness of our approach for fake news detection.

The effectiveness of Facebook as a social network intervention to increase physical activity in Chinese young adults.

IntroductionFacebook, one of the worlds' most popular online social networks, is easy to access and cost-effective. Its use to enhance individual's physical activity (PA) participation should be examined. This research reviews the effectiveness and successful features of Facebook for influencing PA behaviors in young adults (Study 1), and empirically examines the efficacy of the features purported in increase PA via a Facebook intervention (Study 2).MethodsIn Study 1, PubMed, Medline, SPORT Discus, ERIC, and Embase were searched for articles that identified successful features and effectiveness of Facebook PA interventions published between January 2005 and February 2022. In Study 2, a 4-week Facebook PA intervention with University students was conducted using features identified in Study 1. The PA behaviors with objective (ActiGraph) and subjective (questionnaire) measures, perceived PA level, stage of readiness, effectiveness, and efficiency of Facebook were examined.ResultsStudy 1 concluded that the most effective strategies for producing significant PA changes in young adults using a PA Facebook intervention included the following: Adding behavior modification (goal setting and self-monitoring), using influence agents, recruiting members of an existing network with the snowball technique, being attentive to group size, enhancing social support with motivational quotes, interactive posts, opinion polls, increasing tailored feedback, and providing educational information. Study 2 found no significant difference in PA between the intervention and the control groups, as measured objectively, but the subjective reporting of PA behavior was higher in the intervention group. Compared to the control group, the Facebook PA intervention group reported more positive change in perceived stage of readiness in PA participation, commuting type, sport type, sport venue, sport emotion, and fast breathing or sweating. When features were ranked by the Facebook PA intervention group, motivation (supports from your friends) and tailored feedback (the responses from your friends are really personal and fits you) were the top two ranked features.ConclusionThe use of influence agents in the Facebook PA intervention could address exercise preference and facilitate higher program engagement. Significant differences related to commuting type, sport types, sport venue barriers, and exercise intensity across groups were noteworthy and warrant additional investigation in the future.