Journal articles on the topic 'Online social networks – Mathematical models'
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1
Bonato, Anthony, Noor Hadi, Paul Horn, Paweł Prałat, and Changping Wang. "Models of Online Social Networks." Internet Mathematics 6, no. 3 (January 2009): 285–313. http://dx.doi.org/10.1080/15427951.2009.10390642.
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Jiang, Ping, and Xiangbin Yan. "Stability analysis and control models for rumor spreading in online social networks." International Journal of Modern Physics C 28, no. 05 (March 9, 2017): 1750061. http://dx.doi.org/10.1142/s0129183117500619.
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This paper establishes a novel Susceptible-Infected-Removed (SIR) rumor spreading model for online social networks (OSNs). The model utilizes the node degree to describe the dynamic changes of the number of rumor spreaders and it can be regarded as an extension of the traditional SIR model. Stability analysis of the model reveals that the spreader in social networks has a basic reproduction number. If the basic reproduction number is less than 1, then rumors will disappear. Otherwise, rumors will persist. According to this result, we can predict the trend of rumor spreading. Then we propose an immune-structure SIR model to explore the control method of rumor spreading. Stability analysis and numerical simulation of the model indicate that immunizing susceptible individual is an effective method to control rumors. Further, the immune-structure model explains that the network structure decides the choice of immune methods. Our findings offer some new insights to control the spread of rumors on OSNs.
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Gabdrakhmanova, Nailia, and Maria Pilgun. "Intelligent Control Systems in Urban Planning Conflicts: Social Media Users’ Perception." Applied Sciences 11, no. 14 (July 17, 2021): 6579. http://dx.doi.org/10.3390/app11146579.
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The relevance of this study is determined by the need to develop technologies for effective urban systems management and resolution of urban planning conflicts. The paper presents an algorithm for analyzing urban planning conflicts. The material for the study was data from social networks, microblogging, blogs, instant messaging, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the construction of the North-Eastern Chord (NEC) in Moscow (RF). To analyze the content of social media, a multimodal approach was used. The paper presents the results of research on the development of methods and approaches for constructing mathematical and neural network models for analyzing the social media users’ perceptions based on their digital footprints. Artificial neural networks, differential equations, and mathematical statistics were involved in building the models. Differential equations of dynamic systems were based on observations enabled by machine learning. Mathematical models were developed to quickly detect, prevent, and address conflicts in urban planning in order to manage urban systems efficiently. In combination with mathematical and neural network model the developed approaches, made it possible to draw a conclusion about the tense situation around the construction of the NEC, identify complaints of residents to constructors and city authorities, and propose recommendations to resolve and prevent conflicts. Research data could be of use in solving similar problems in sociology, ecology, and economics.
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Govindankutty, Sreeraag, and Shynu Padinjappurathu Gopalan. "SEDIS—A Rumor Propagation Model for Social Networks by Incorporating the Human Nature of Selection." Systems 11, no. 1 (December 29, 2022): 12. http://dx.doi.org/10.3390/systems11010012.
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The explosive evolution of the internet has paved the path for the rise of social networks, which can help people connect remotely. Currently, social networks are commonly used for sharing thoughts, feelings, information, and personal life, which vary from individual to individual. The world has witnessed a tremendous increase in social media usage in the last decade, and more people are expected to spend their time online after the COVID-19 pandemic. This increases the rapid propagation of rumors and fake news within societies and communities. On one end, social networks act as an excellent platform for digital marketing and sharing information. However, on the other end, social network rumors and fake news create a significant impact on society, including riots. To study and analyze social network rumors, several mathematical rumor propagation epidemic models have been proposed. The majority are related to disease-spreading epidemic models and reject the human aspect of social selection. This paper introduces a new mathematical rumor propagation model for social networks by incorporating the human psychological aspect of selection as a separate state. Our mathematical analysis and computational simulation proved that the model exists within the system. It was also proven that the system is always non-negative and there always exists a solution in the system. Our implementation of an intervention mechanism within the discrete compartmental model simulation proved the necessity of an effective interference that can help to prevent the implications of uncontrolled rumor dissemination within social networks.
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Gnedash, Anna, and Veronika Katermina. "Abortion Ban in English Social Media in 2022: Pragmatic Linguistics of Online Communications." Virtual Communication and Social Networks 2022, no. 4 (December 22, 2022): 172–78. http://dx.doi.org/10.21603/2782-4799-2022-1-4-172-178.
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The present research featured the conflict discourse of online communication in social networks on the topic of abortion ban. The empirical base included 3,000,000 Twitter messages in English. The sampling by keywords women and abortion covered the period from May 1 to July 31, 2022, which saw an outburst of online and offline civic activities regarding some national anti-abortion policies. The resulting web corpus of network linguistic data (datasets) was subjected to multidimensional analysis using such methods as Data Science, mathematical modeling, relational sociology, corpus analysis, discourse analysis, etc. All these procedures combined resulted in a multidimensional comprehensive analysis of the simulated English asynchronous multimodal discursive field in Twitter. The models made it possible to visualize online communications in social networks, as well as to describe the discourse of online communication between pro-choice and pro-life. The authors analyzed the pragmatic potential of network communities on the current political agenda. The method might help to identify the conflict potential that can evolve from online communication into offline socio-political actions.
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DU, FANG, QI XUAN, and TIE-JUN WU. "EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS." International Journal of Modern Physics C 21, no. 07 (July 2010): 955–71. http://dx.doi.org/10.1142/s0129183110015592.
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Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.
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Attri, Vikas. "Comparative study of Existing Models for Online Social Network." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 11, 2021): 483–90. http://dx.doi.org/10.17762/turcomat.v12i2.856.
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Today, Online Social Networks becomes the first choice for businesses to broadcast their campaigns for branding, publicity, strategies, advertising, marketing, social influence and so many other areas. Social Network is a platform for communicating with social actors and Social Media is used by companies for broadcasting information. Online Social Network used by businesses for number of purposes but the primary concern is build new social connections that helps to target most audiences for successful campaign purposes. In OSNs sites the social objects are represented by nodes and the term edge used for connection between nodes under the graph theory. Today Social Network sites have becomes most exploded as compared to traditional sites because of impact of so many influence models over traditional models.Some of popular websites of OSN such as MySpace, Facebook, Flickr, YouTube, Google Video, Orkut, LinkedIn, Live Journal and BlogSpot have great impact on customer when targeting the sales marketing funnel for businesses. Adjacent users sometimes called engaged users tend to have more trust level as compared to random pairs users on the social media sites. Already have so much research that helps to calculate the trust factor using influence modeling. So influence models play a vital role to predict the behavior of the customer that helps to fulfill the goal of the business. The key contribution of this work is study of online social networking models.
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Luo, Peng, Chong Wu, and Yongli Li. "Link prediction measures considering different neighbors’ effects and application in social networks." International Journal of Modern Physics C 28, no. 03 (March 2017): 1750033. http://dx.doi.org/10.1142/s0129183117500334.
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Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.
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Liu, Xiaoyang, Chao Liu, and Xiaoping Zeng. "Online Social Network Emergency Public Event Information Propagation and Nonlinear Mathematical Modeling." Complexity 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/5857372.
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Emergency public event arises everyday on social network. The information propagation of emergency public event (favorable and harmful) is researched. The dynamics of a susceptible-infected-susceptible and susceptible-infected-removed epidemic models incorporated with information propagation of emergency public event are studied. In particular, we investigate the propagation model and the infection spreading pattern using nonlinear dynamic method and results obtained through extensive numerical simulations. We further generalize the model for any arbitrary number of infective network nodes to mimic existing scenarios in online social network. The simulation results reveal that the inclusion of multiple infective node achieved stability and equilibrium in the proposed information propagation model.
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Kuzmenko, O., H. Yarovenko, and L. Skrynka. "ANALYSIS OF MATHEMATICAL MODELS FOR COUNTERING CYBER FRAUD IN BANKS." Vìsnik Sumsʹkogo deržavnogo unìversitetu 2022, no. 2 (2022): 111–20. http://dx.doi.org/10.21272/1817-9215.2022.2-13.
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The article is devoted to the current topic of analysis of mathematical models for countering cyber fraud in banks. This problem is due to the security risks growth in the banking system, which are formed by fraudsters' cyberattacks and cybercrimes implementation. Therefore, the priority task for cyberbanking security is the application of modern mathematical methods to analyse the sources of cyber attacks, identify threats and losses in the banking services market, identify cyber-attacks and assess the scenario of potential cyber risk, etc. The article analyses the most widespread types of cyber fraud: social engineering, phishing, stalking, farming, DoS attacks, online fraud, potentially unwanted programs, etc. The study also considered a model of cognitive computing and detection of suspicious transactions in banking cyber-physical systems based on quantum computing in BCPS for the post-quantum era. The advantages, disadvantages and results of the model are defined. Predictive modelling is proposed to detect fraud in real-time by analysing incoming bank transactions with payment cards. Within the framework of this method, such models are used for the classification of fraud detection as logistic regression, a decision tree, and a narrower technique - a random forest decision tree. The study also considered using the harmonic search algorithm in neural networks to improve fraud detection in the banking system. It is found that although this model has the advantage of learning ability based on past behaviour, there are difficulties in the long-term processing of many neural networks. The stages of model implementation are also given. In addition, the modelling of credit card fraud detection is based on using two types of models: supervised and unsupervised. Supervised models include logistic regression, K-nearest neighbours, and extreme gradient boosting. The one-class support vector model, restricted Boltzmann model, and generative-competitive network are considered among uncontrolled generative models.
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Khan, Jebran, and Sungchang Lee. "Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment." Symmetry 10, no. 11 (November 19, 2018): 654. http://dx.doi.org/10.3390/sym10110654.
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In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.
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Divak, M. P., A. M. Melnyk, Ye S. Kedrin, and Frank Avalon Otoo. "Interval model of the portrait of users of the thematic group on environmental issues in the social network." Optoelectronic Information-Power Technologies 41, no. 1 (May 2, 2022): 78–88. http://dx.doi.org/10.31649/1681-7893-2021-41-1-78-88.
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Mathematical models of dynamics of efficiency of information social networks are considered in the work. An approach to estimating model parameters is proposed. A number of experimental studies were conducted on the basis of data on the functioning of a special online group Facebook. The indicator of the characteristics of the information message was studied. An interval discrete model in the form of a difference equation is obtained, which describes the dynamics of users' reactions to messages in thematic groups of social networks. On the basis of the conducted experiments, the efficiency of application of the offered model is confirmed
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Pham, Phuong N. H., Bich-Ngan T. Nguyen, Quy T. N. Co, and Václav Snášel. "Multiple Benefit Thresholds Problem in Online Social Networks: An Algorithmic Approach." Mathematics 10, no. 6 (March 9, 2022): 876. http://dx.doi.org/10.3390/math10060876.
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An important problem in the context of viral marketing in social networks is the Influence Threshold (IT) problem, which aims at finding some users (referred to as a seed set) to begin the process of disseminating their product’s information so that the benefit gained exceeds a predetermined threshold. Even though, marketing strategies exhibit different in several realistic scenarios due to market dependence or budget constraints. As a consequence, picking a seed set for a specific threshold is not enough to come up with an effective solution. To address the disadvantages of previous works with a new approach, we study the Multiple Benefit Thresholds (MBT), a generalized version of the IT problem, as a result of this phenomenon. Given a social network that is subjected to information distribution and a set of thresholds, T={T1,T2,…,Tk},Ti>0, the issue aims to seek the seed sets S1,S2,…,Sk with the lowest possible cost so that the benefit achieved from the influence process is at the very least T1,T2,…,Tk, respectively. The main challenges of this problem are a #NP-hard problem and the estimation of the objective function #P-Hard under traditional information propagation models. In addition, adapting the exist algorithms many times to different thresholds can lead to large computational costs. To address the abovementioned challenges, we introduced Efficient Sampling for Selecting Multiple Seed Sets, an efficient technique with theoretical guarantees (ESSM). At the core of our algorithm, we developed a novel algorithmic framework that (1) can use the solution to a smaller threshold to find that of larger ones and (2) can leverage existing samples with the current solution to find that of larger ones. The extensive experiments on several real social networks were conducted in order to show the effectiveness and performance of our algorithm compared with current ones. The results indicated that our algorithm outperformed other state-of-the-art ones in terms of both the total cost and running time.
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S, Neelakandan, Sridevi M, Saravanan Chandrasekaran, Murugeswari K, Aditya Kumar Singh Pundir, Sridevi R, and T. Bheema Lingaiah. "Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media." Computational Intelligence and Neuroscience 2022 (June 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/2163458.
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As a result of the ease with which the internet and cell phones can be accessed, online social networks (OSN) and social media have seen a significant increase in popularity in recent years. Security and privacy, on the other hand, are the key concerns in online social networks and other social media platforms. On the other hand, cyberbullying (CB) is a serious problem that needs to be addressed on social media platforms. Known as cyberbullying (CB), it is defined as a repetitive, purposeful, and aggressive reaction performed by individuals through the use of information and communication technology (ICT) platforms such as social media platforms, the internet, and cell phones. It is made up of hate messages that are sent by e-mail, chat rooms, and social media platforms, which are accessed through computers and mobile phones. The detection and categorization of CB using deep learning (DL) models in social networks are, therefore, crucial in order to combat this trend. Feature subset selection with deep learning-based CB detection and categorization (FSSDL-CBDC) is a novel approach for social networks that combines deep learning with feature subset selection. The suggested FSSDL-CBDC technique consists of a number of phases, including preprocessing, feature selection, and classification, among others. Additionally, a binary coyote optimization (BCO)-based feature subset selection (BCO-FSS) technique is employed to select a subset of features that will increase classification performance by using the BCO algorithm. Additionally, the salp swarm algorithm (SSA) is used in conjunction with a deep belief network (DBN), which is known to as the SSA-DBN model, to detect and characterize cyberbullying in social media networks and other online environments. The development of the BCO-FSS and SSA-DBN models for the detection and classification of cyberbullying highlights the originality of the research. A large number of simulations were carried out to illustrate the superior classification performance of the proposed FSSDL-CBDC technique. The SSA-DBN model has exhibited superior accuracy to the other algorithms, with a 99.983 % accuracy rate. Overall, the experimental results revealed that the FSSDL-CBDC technique beats the other strategies in a number of different aspects.
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Shang, Yingdan, Bin Zhou, Ye Wang, Aiping Li, Kai Chen, Yichen Song, and Changjian Lin. "Popularity Prediction of Online Contents via Cascade Graph and Temporal Information." Axioms 10, no. 3 (July 23, 2021): 159. http://dx.doi.org/10.3390/axioms10030159.
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Predicting the popularity of online content is an important task for content recommendation, social influence prediction and so on. Recent deep learning models generally utilize graph neural networks to model the complex relationship between information cascade graph and future popularity, and have shown better prediction results compared with traditional methods. However, existing models adopt simple graph pooling strategies, e.g., summation or average, which prone to generate inefficient cascade graph representation and lead to unsatisfactory prediction results. Meanwhile, they often overlook the temporal information in the diffusion process which has been proved to be a salient predictor for popularity prediction. To focus attention on the important users and exclude noises caused by other less relevant users when generating cascade graph representation, we learn the importance coefficient of users and adopt sample mechanism in graph pooling process. In order to capture the temporal features in the diffusion process, we incorporate the inter-infection duration time information into our model by using LSTM neural network. The results show that temporal information rather than cascade graph information is a better predictor for popularity. The experimental results on real datasets show that our model significantly improves the prediction accuracy compared with other state-of-the-art methods.
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Jin, Rong, and Weili Wu. "Schemes of propagation models and source estimators for rumor source detection in online social networks: A short survey of a decade of research." Discrete Mathematics, Algorithms and Applications 13, no. 04 (April 19, 2021): 2130002. http://dx.doi.org/10.1142/s1793830921300022.
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Recent years have seen various rumor diffusion models being assumed in detection of rumor source research of the online social network. Diffusion model is arguably considered as a very important and challenging factor for source detection in networks, but it is less studied. This paper provides an overview of three representative schemes of modeling the pattern of rumor propagation as well as three major schemes of rumor source estimator in the Independent Cascade-based model, the Epidemic-based model, and the Learning-based model, respectively, since their inception a decade ago.
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Novikov, Dmitry. "Models of Strategic Decision-Making under Informational Control." Mathematics 9, no. 16 (August 9, 2021): 1889. http://dx.doi.org/10.3390/math9161889.
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A general complex model is considered for collective dynamical strategic decision-making with explicitly interconnected factors reflecting both psychic (internal state) and behavioral (external-action, result of activity) components of agents’ activity under the given environmental and control factors. This model unifies and generalizes approaches of game theory, social psychology, theories of multi-agent systems, and control in organizational systems by simultaneous consideration of both internal and external parameters of the agents. Two special models (of informational control and informational confrontation) contain formal results on controllability and properties of equilibriums. Interpretations of a general model are conformity (threshold behavior), consensus, cognitive dissonance, and other effects with applications to production systems, multi-agent systems, crowd behavior, online social networks, and voting in small and large groups.
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Yie, Kyent-Yon, Tsair-Wei Chien, Yu-Tsen Yeh, Willy Chou, and Shih-Bin Su. "Using Social Network Analysis to Identify Spatiotemporal Spread Patterns of COVID-19 around the World: Online Dashboard Development." International Journal of Environmental Research and Public Health 18, no. 5 (March 3, 2021): 2461. http://dx.doi.org/10.3390/ijerph18052461.
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The COVID-19 pandemic has spread widely around the world. Many mathematical models have been proposed to investigate the inflection point (IP) and the spread pattern of COVID-19. However, no researchers have applied social network analysis (SNA) to cluster their characteristics. We aimed to illustrate the use of SNA to identify the spread clusters of COVID-19. Cumulative numbers of infected cases (CNICs) in countries/regions were downloaded from GitHub. The CNIC patterns were extracted from SNA based on CNICs between countries/regions. The item response model (IRT) was applied to create a general predictive model for each country/region. The IP days were obtained from the IRT model. The location parameters in continents, China, and the United States were compared. The results showed that (1) three clusters (255, n = 51, 130, and 74 in patterns from Eastern Asia and Europe to America) were separated using SNA, (2) China had a shorter mean IP and smaller mean location parameter than other counterparts, and (3) an online dashboard was used to display the clusters along with IP days for each country/region. Spatiotemporal spread patterns can be clustered using SNA and correlation coefficients (CCs). A dashboard with spread clusters and IP days is recommended to epidemiologists and researchers and is not limited to the COVID-19 pandemic.
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Cao, Huiru, Xiaomin Li, Yanfeng Lin, and Songyao Lian. "Hybrid Fake Information Containing Strategy Exploiting Multi-Dimensions Data in Online Community." Mathematics 10, no. 18 (September 8, 2022): 3265. http://dx.doi.org/10.3390/math10183265.
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It is well-established that, in the past few years, internet users have rapidly increased. Meanwhile, various types of fake information (such as fake news or rumors) have been flooding social media platforms or online communities. The effective containing or controlling of fake news or rumor has drawn wide attention from areas such as academia to social media platforms. For that reason, numerous studies have focused on this subject from different perspectives, such as employing complex networks and spreading models. However, in the real online community, misinformation usually spreads quickly to thousands of users within minutes. Conventional studies are too theoretical or complicated to be applied to practical applications, and show a lack of fast responsiveness and poor containing effects. Therefore, in this work, a hybrid strategy exploiting the multi-dimensional data of users and content was proposed for the fast containing of fake information in the online community. The strategy is mainly composed of three steps: the fast detection of fake information by continuously updating the content comparison dataset according to the specific hot topic and the fake contents; creating spreading force models and user divisions via historical data, and limiting the propagation of fake information based on the content and user division. Finally, an experiment was set up online with BBS (Bulletin Board System), and the acquired results were analyzed by comparison with other methods in different metrics. From the extracted results, it has been demonstrated that the proposed solution clearly outperforms traditional methods.
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Et. al., M. Arunkrishna. "Detecting Misleading Information on COVID-19 : A Machine Learning Perspective." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 3 (April 11, 2021): 4918–26. http://dx.doi.org/10.17762/turcomat.v12i3.2000.
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Online Social networks become a popular way for sharing information among people. With increasing technology like Wi-Fi, Wi-Max ,3G/4G along with handheld devices like smartphones and tablets, popular applications such as Instagram, Facebook, Twitter and YouTube, becomes a dominant platform for news and entertainment. The extensive use of these social networks has an incredible influence on sharing news among people It holds both positive and negative effects of its own. Because of it’s high popularity,Online Social Networks(OSNs),has become the target for spammers. Also, false news for different political and commercial purpose has been evolving in the large count and spread worldwide. After the spread of COVID-19, there had been a lot of confusion and pitfalls on the topic of who to believe and who should be rejected. With the advent of time, several companies like Facebook, and Twitter joined hands to identify the news and regard it authentic or not. This effort was very hard for people, as the news are spreading at a rapid pace, no matter how many people are upon the task, the rate of expansion of news is always faster than the rate of evaluation of whether the news is authentic or not. Additionally, it can be observed that the news cannot be regarded as fake or true before careful evaluation. This evaluation is based on the results. So it is important to create a method for identifying fake news and distinguishing it from individuals. Thus, the paper evaluates several models in order to find the best fit with the highest level of accuracy.
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Rahman, Mahfujur, Mehedi Hasan, Md Masum Billah, and Rukaiya Jahan Sajuti. "Political Fake News Detection from Different News Source on Social Media using Machine Learning Techniques." AIUB Journal of Science and Engineering (AJSE) 21, no. 2 (November 23, 2022): 110–17. http://dx.doi.org/10.53799/ajse.v21i1.383.
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People are more dependable on online news systems than ever in this modern time and day. The more people depend on online news, magazines, and journals, the more likely it will have more significant consequences of fake news or rumors. In the era of social networking, it has become a significant problem that negatively influences society. The fact is that the internet has become more accessible than ever, and its uses have increased exponentially. From 2005 to 2020, overall web users have increased from 1.1 billion to 3.96 billion. As most individuals' primary sources are microblogging networks, fake news spreads faster than ever. Thus it has become very complicated to detect fake news over the internet. For that purpose, we have used four traditional machine learning (ML) algorithms and long short-term memory (LSTM) methods. The four traditional methods are as follows logistic regression (LR), decision tree (DT) classification, k-nearest neighbors (KNN) classification, and naive bayes (NB) classification. To conduct this experiment, we first implemented four traditional machine learning methods. Then we trained our dataset with LSTM and Bi-LSTM (bidirectional long-short term memory) to get the best-optimized result. This paper experimented with four traditional methods and two deep learning models to find the best models for detecting fake news. In our research, we can see that, from four traditional methods, logistic regression performs best and generate 96% accuracy, and the Bi-LSTM model can generate 99% accuracy, which outbreaks all previous scores.
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Yu, Shuzhen, Zhiyong Yu, and Haijun Jiang. "Stability, Hopf Bifurcation and Optimal Control of Multilingual Rumor-Spreading Model with Isolation Mechanism." Mathematics 10, no. 23 (December 1, 2022): 4556. http://dx.doi.org/10.3390/math10234556.
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The propagation of rumors on online social networks (OSNs) brings an awful lot of trouble to people’s life and society. Aiming at combating rumors spreading on OSNs, two novel rumor-propagation models without and with time delays are proposed, which combine with the influence of the immune mechanism, isolation mechanism and network structure. Firstly, we analyze the existence of rumor equilibria and obtain some existence conditions of backward bifurcation. Secondly, the local stabilities of rumor-free and rumor equilibria are proved by using the Jacobian matrix method, and some critical conditions for the existence of Hopf bifurcation are acquired by selecting critical parameters and delays as bifurcation parameters. Furthermore, an optimal control method is proposed, which can prevent the spread of rumors within an expected time period and minimize the cost of control. Finally, some numerical simulations are provided to verify the effectiveness of the proposed theoretical results.
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Kobayashi, Ryota, and Renaud Lambiotte. "TiDeH: Time-Dependent Hawkes Process for Predicting Retweet Dynamics." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 1 (August 4, 2021): 191–200. http://dx.doi.org/10.1609/icwsm.v10i1.14717.
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Online social networking services allow their users to post content in the form of text, images or videos. The main mechanism driving content diffusion is the possibility for users to re-share the content posted by their social connections, which may then cascade across the system. A fundamental problem when studying information cascades is the possibility to develop sound mathematical models, whose parameters can be calibrated on empirical data, in order to predict the future course of a cascade after a window of observation. In this paper, we focus on Twitter and, in particular, on the temporal patterns of retweet activity for an original tweet. We model the system by Time-Dependent Hawkes process (TiDeH), which properly takes into account the circadian nature of the users and the aging of information. The input of the prediction model are observed retweet times and structural information about the underlying social network. We develop a procedure for parameter optimization and for predicting the future profiles of retweet activity at different time resolutions. We validate our methodology on a large corpus of Twitter data and demonstrate its systematic improvement over existing approaches in all the time regimes.
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Akhremenko, A. S., A. P. Ch Petrov, D. K. Stukal, S. A. Zheglov, and M. V. Khavronenko. "What Bots Can (and Can’t) Do? (Model of Protest and Counter-Protest Political Mobilization)." Journal of Political Theory, Political Philosophy and Sociology of Politics Politeia 102, no. 3 (September 23, 2021): 172–94. http://dx.doi.org/10.30570/2078-5089-2021-102-3-172-194.
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Despite the increasing interest among scholars in the effect of Internet bots, or automated social media accounts, on the processes of political communication and mobilization in the online sphere, the extent of bots’ effectiveness and the specific mechanisms of their use remain largely understudied. The deficit of the overarching conceptual understanding and concrete results is arguably due to researchers’ aspiration to solve a problem in the empirical way, without attempting to combine data analysis with mathematical and computational modeling. Having analyzed the existing models on the topic, the authors offer their own model that is based on the spiral-of-silence theory. The key features of the model that set it apart from the existing ones are the following: a) taking into account differences in the types of motivation and costs associated with expressing protest and loyalist sentiments; b) including “partner effect” into the spiral-ofsilence mechanism; c) employing a neurological decision-ma king scheme according to which the same stimulus can prompt action and be a deterrent. On the basis of a series of computational experiments with the model, the authors demonstrate that bots are more effective in mobilizing opposition members when an individual motivated for political participation refrains from it because his local social community does not share his views. In this case, the emergence of a like-minded partner bot can destroy the spiral of silence created by this community and encourage this individual to openly express his position. On the contrary, when mobilizing loyalists, bots are most effective in relation to poorly motivated individuals. The model elaborated by the authors not only allows us to evaluate bots’ effects in a new way, but it also sheds light on how people make decisions in the framework of political communication and mobilization in social networks.
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Sagadevan, Saravanan, Nurul Hashimah Ahamed Hassain Malim, and Mohd Heikal Husin. "A Seed-Guided Latent Dirichlet Allocation Approach to Predict the Personality of Online Users Using the PEN Model." Algorithms 15, no. 3 (March 8, 2022): 87. http://dx.doi.org/10.3390/a15030087.
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There is a growing interest in topic modeling to decipher the valuable information embedded in natural texts. However, there are no studies training an unsupervised model to automatically categorize the social networks (SN) messages according to personality traits. Most of the existing literature relied on the Big 5 framework and psychological reports to recognize the personality of users. Furthermore, collecting datasets for other personality themes is an inherent problem that requires unprecedented time and human efforts, and it is bounded with privacy constraints. Alternatively, this study hypothesized that a small set of seed words is enough to decipher the psycholinguistics states encoded in texts, and the auxiliary knowledge could synergize the unsupervised model to categorize the messages according to human traits. Therefore, this study devised a dataless model called Seed-guided Latent Dirichlet Allocation (SLDA) to categorize the SN messages according to the PEN model that comprised Psychoticism, Extraversion, and Neuroticism traits. The intrinsic evaluations were conducted to determine the performance and disclose the nature of texts generated by SLDA, especially in the context of Psychoticism. The extrinsic evaluations were conducted using several machine learning classifiers to posit how well the topic model has identified latent semantic structure that persists over time in the training documents. The findings have shown that SLDA outperformed other models by attaining a coherence score up to 0.78, whereas the machine learning classifiers can achieve precision up to 0.993. We also will be shared the corpus generated by SLDA for further empirical studies.
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AlFalahi, Kanna, Yacine Atif, and Ajith Abraham. "Models of Influence in Online Social Networks." International Journal of Intelligent Systems 29, no. 2 (November 17, 2013): 161–83. http://dx.doi.org/10.1002/int.21631.
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González-Uribe, Catalina, Nicolás Yañez, Alf Onshuus Niño, Nubia Velasco, Juan Manuel Cordovez, Mauricio Santos-Vega, Natalia Niño-Machado, et al. "A mixed-methods study on the design of Artificial Intelligence and data science-based strategies to inform public health responses to COVID-19 in different local health ecosystems: A study protocol for COLEV." F1000Research 11 (June 22, 2022): 691. http://dx.doi.org/10.12688/f1000research.110958.1.
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Background: Artificial Intelligence (AI) and data science research are promising tools to better inform public policy and public health responses, promoting automation and affordability. During the COVID-19 pandemic, AI has been an aid to forecast outbreak spread globally. The overall aim of the study is to contribute to the ongoing public health, socioeconomic, and communication challenges caused by COVID-19. Protocol: COLEV is a five-pronged interdisciplinary mixed methods project based on AI and data science from an inclusive perspective of age and gender to develop, implement, and communicate useful evidence for COVID-19-related response and recovery in Colombia. The first objective is identification of stakeholders’ preferences, needs, and their use of AI and data science relative to other forms of evidence. The second objective will develop locally relevant mathematical models that will shed light on the possible impact, trajectories, geographical spread, and uncertainties of disease progression as well as risk assessment. The third objective focuses on estimating the effect of COVID-19 on other diseases, gender disparities and health system saturation. The fourth objective aims to analyze popular social networks to identify health-related trending interest and users that act as ‘super spreaders’ for information and misinformation. Finally, the fifth objective, aims at designing disruptive cross-media communication strategies to confront mis- and dis-information around COVID-19. To understand stakeholders’ perspectives, we will use semi-structured interviews and ethnographic work. Daily cases and deaths of COVID-19 reported from the National Surveillance System (INS) of Colombia will be used for quantitative analysis, and data regarding the online conversation will be obtained from Facebook and Twitter. Conclusions: COLEV intends to facilitate the dialogue between academia and health policymakers. The results of COLEV will inform on the responsible, safe and ethical use of AI and data science for decision-making in the context of sanitary emergencies in deeply unequal settings.
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Лазаренко, Ірина Сергіївна, Станіслав Васильович Салоїд, Світлана Олександрівна Тульчинська, Сергій Олександрович Кириченко, and Ростислав Володимирович Тульчинський. "NECESSITY OF IMPLEMENTATING DATA SCIENCE COURSE IN ECONOMICS CURRICULA." Information Technologies and Learning Tools 78, no. 4 (September 11, 2020): 132–44. http://dx.doi.org/10.33407/itlt.v78i4.3505.
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The article describes the relevance and feasibility of implementation Data Science courses for leading economics majors: 051 Economics, 075 Marketing, 073 Management. Application of computer technology, mathematical methods and models, statistical analysis in the study process for economics students became routine long time ago, then why is Data Science linked mostly only to the faculties of information technologies? The specificity of economic professions requires the acquisition of skills in the work with large data sets, qualitative evaluation of statistics, predicting a large number of economic phenomena, so the economist of the future should be not only a specialist in the main subject area, but also a specialist in Big Data and Data Mining. The study outlines the underlying background for essential changes. The article analyzes relevant educational and professional programs, blocks of disciplines, providing qualitative assimilation of new information by students and acquisition of those abilities and skills that are needed by the modern specialist in the field of economy and will form student as a serious competitor in the labor market. It has been conducted the analysis of modern international commercial on-line courses, specifying the topics and aspects necessary for the future economics graduates. The logical scheme of Data Science specialties introduction which follows the relevant cycle of the existing disciplines of general and professional training is proposed. Mastering the knowledge of qualitative data analysis and tools for optimal work with them should be one of the main tasks of the methodological system of education and research at the faculties of economics. Modern educational technologies and scientific facilities of universities should help to expand the understanding and perception of the economist, marketologist, and manager profession, because the digital advertising, SMM, social networks, online applications, project management, State in a Smartphone, and other rapid transformations encourage to train not classic specialists, but universals who will be able to adapt quickly to the needs of the future.
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Anderson, Brian D. O., and Mengbin Ye. "Mathematical Models of Self-Appraisal in Social Networks." Journal of Systems Science and Complexity 34, no. 5 (October 2021): 1604–33. http://dx.doi.org/10.1007/s11424-021-1193-y.
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Angiani, Giulio, Paolo Fornacciari, Eleonora Iotti, Monica Mordonini, and Michele Tomaiuolo. "Participation in Online Social Networks." International Journal of Interactive Communication Systems and Technologies 8, no. 2 (July 2018): 36–55. http://dx.doi.org/10.4018/ijicst.2018070103.
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Why and how more and more people get involved and use social networking systems are critical topics in social network analysis (SNA). As a matter of fact, social networking systems bring online a growing number of acquaintances, for many different purposes. Both business interests and personal recreational goals are motivations for using online social networks (OSN) or other social networking systems. The participation in social networks is a phenomenon which has been studied with several theories, and SNA is useful for common business problems, e.g., launching distributed teams, retaining people with vital knowledge for the organization, improving access to knowledge and spreading ideas and innovation. Nevertheless, there are some difficulties, such as anti-social behaviors of participants, lack of incentives, organizational costs and risks. In this article, a survey of the basic features of SNA, participation theories and models are discussed, with emphasis on social capital, information spreading, motivations for participation, and anti-social behaviors of social network users.
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Chen, Cheng, Bin Xu, Jong-Hoon Yang, and Mi Liu. "Sentiment Analysis of Animated Film Reviews Using Intelligent Machine Learning." Computational Intelligence and Neuroscience 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/8517205.
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Film is an essential expression of a country’s cultural soft power in terms of cross-cultural exchange. In addition, film is also the most direct and favourable means of communication. Along with the expansion and development of the Chinese film market, outstanding animation films have emerged in recent years. Animated films have both artistic and commercial properties and can not only have a cultural impact but can also contribute to economic growth. For this reason, our country is now paying more and more attention to the development of animated films. Specifically, animated films not only represent a country’s cultural soft power and national image, but they are also a symbol of the strength of a country’s cultural industry. As a reflection and extension of China’s culture and ideology, animated films play an important role in enhancing cultural confidence and cultural export. In recent years, China’s economy has shown a steady and sustained growth trend. At the same time, with the rapid development of internet technology, social networking has gradually penetrated into all aspects of people’s lives. Various social networking forums, websites, and sites have emerged. While satisfying a wide range of needs, they also provide information on product reviews, social reviews, and service reviews. These reviews contain feedback from the reviewer about the subject of the review. Tapping into the emotions in these reviews can provide consumers with shopping references and help businesses to optimise their products and improve their business strategies. With the help of modern internet technology and information technology, the modern movie industry, such as Cat’s Eye Movies and other internet entertainment service platforms, has developed a model of online ticketing, offline movie viewing, and online reviews and feedback. The content of the reviews on these movie websites fully reflects the attitudinal views of the movie-going community. These reviews play a decisive role in the box office and the further spread of culture. As a result, in order to better understand the audience’s emotional tendencies and needs, it is necessary to carry out sentiment analysis and deep semantic mining of animated film reviews. As the evaluation of film works considers many factors and is complex and variable, the choice of model is crucial in the process of sentiment analysis. Machine learning models represented by deep neural networks are more tolerant of sentence noise and have strong information discrimination and feature self-learning capabilities. As a result, intelligent machine learning is more advantageous for sentiment classification tasks. This study is a combination of textual data mining and statistical analysis from the perspective of viewers’ comments to study the online reviews of animation films from different countries. At the same time, this research hopes to uncover meaningful information from the film reviews and the gap between Chinese and other countries’ animation films, in order to provide a little help for the rise of domestic animation films.
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TwinkleMathew, Amal, S. Saravana Kumar, and Karthikeyan M. "User Intended Privacy Preserving Models in Online Social Networks." International Journal of Computer Applications 113, no. 15 (March 18, 2015): 28–32. http://dx.doi.org/10.5120/19903-2014.
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Kong, Jong-Hwan, Ik Kyun Kim, and Myung-Mook Han. "Propagation Models for Structural Parameters in Online Social Networks." Journal of Korean Society for Internet Information 15, no. 1 (February 28, 2014): 125–34. http://dx.doi.org/10.7472/jksii.2014.15.1.125.
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Jelassi, Mariem, Kayode Oshinubi, Mustapha Rachdi, and Jacques Demongeot. "Epidemic dynamics on social interaction networks." AIMS Bioengineering 9, no. 4 (2022): 348–61. http://dx.doi.org/10.3934/bioeng.2022025.
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<abstract> <p>The present paper aims to apply the mathematical ideas of the contagion networks in a discrete dynamic context to the modeling of two current pandemics, i.e., COVID-19 and obesity, that are identified as major risks by the World Health Organization. After providing a reminder of the main tools necessary to model epidemic propagation in a Boolean framework (Hopfield-type propagation equation, notion of centrality, existence of stationary states), we present two applications derived from the observation of real data and involving mathematical models for their interpretation. After a discussion of the obtained results of model simulations, multidisciplinary work perspectives (both on mathematical and biomedical sides) are proposed in order to increase the efficiency of the models currently used and improve both the comprehension of the contagion mechanism and the prediction of the dynamic behaviors of the pandemics' present and future states.</p> </abstract>
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Keijzer, Marijn A., Michael Mäs, and Andreas Flache. "Communication in Online Social Networks Fosters Cultural Isolation." Complexity 2018 (November 4, 2018): 1–18. http://dx.doi.org/10.1155/2018/9502872.
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Online social networks play an increasingly important role in communication between friends, colleagues, business partners, and family members. This development sparked public and scholarly debate about how these new platforms affect dynamics of cultural diversity. Formal models of cultural dissemination are powerful tools to study dynamics of cultural diversity but they are based on assumptions that represent traditional dyadic, face-to-face communication, rather than communication in online social networks. Unlike in models of face-to-face communication, where actors update their cultural traits after being influenced by one of their network contacts, communication in online social networks is often characterized by a one-to-many structure, in that users emit messages directly to a large number of network contacts. Using analytical tools and agent-based simulation, we show that this seemingly subtle difference can have profound implications for emergent dynamics of cultural dissemination. In particular, we show that within the framework of our model online communication fosters cultural diversity to a larger degree than offline communication and it increases chances that individuals and subgroups become culturally isolated from their network contacts.
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Li, Pei, Yini Zhang, Fengcai Qiao, and Hui Wang. "Information diffusion in structured online social networks." Modern Physics Letters B 29, no. 13 (May 18, 2015): 1550063. http://dx.doi.org/10.1142/s0217984915500633.
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Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.
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Lavenant, H., and B. Maury. "Opinion propagation on social networks: a mathematical standpoint." ESAIM: Proceedings and Surveys 67 (2020): 285–335. http://dx.doi.org/10.1051/proc/202067016.
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These lecture notes address mathematical issues related to the modeling of opinion propagation on networks of the social type. Starting from the behavior of the simplest discrete linear model, we develop various standpoints and describe some extensions: stochastic interpretation, monitoring of a network, time continuous evolution problem, charismatic networks, links with discretized Partial Differential Equations, nonlinear models, inertial version and stability issues. These developments rely on basic mathematical tools, which makes them accessible at an undergraduate level. In a last section, we propose a new model of opinion propagation, where the opinion of an agent is described by a Gaussian density, and the (discrete) evolution equation is based on barycenters with respect to the Fisher metric.
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Sarkar, Dhrubasish, Dipak K. Kole, and Premananda Jana. "Survey of Influential Nodes Identification in Online Social Networks." International Journal of Virtual Communities and Social Networking 8, no. 4 (October 2016): 57–69. http://dx.doi.org/10.4018/ijvcsn.2016100104.
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The increasing popularity of Online Social Networks (OSNs), its complex and rapidly growing structure, its potential impact on business, politics, healthcare and society has led to a great deal of interest in the development of efficient influential nodes identification algorithms. Different kind of business organizations are trying to use the social networking sites to reach the target audience quickly and efficiently in terms of cost and time. The influential nodes can play an important role in it. In this paper the features of social network, models of social networks and different models or approaches used for identifying influential nodes in online social networks have been studied.
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Kuhnle, Alan, Md Abdul Alim, Xiang Li, Huiling Zhang, and My T. Thai. "Multiplex Influence Maximization in Online Social Networks With Heterogeneous Diffusion Models." IEEE Transactions on Computational Social Systems 5, no. 2 (June 2018): 418–29. http://dx.doi.org/10.1109/tcss.2018.2813262.
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Baydeti, Nagaraju, Ramachandran Veilumuthu, and Mariappan Vaithilingam. "Scalable Models for Redundant Data Flow Analysis in Online Social Networks." Wireless Personal Communications 107, no. 4 (April 23, 2019): 2123–42. http://dx.doi.org/10.1007/s11277-019-06375-1.
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Wang, Linna, Xiaoyi Wang, Mei Song, and Tianjiao Niu. "Marketing Model of Tourism Enterprises Based on New Media Environment." International Journal of Antennas and Propagation 2022 (July 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/5273167.
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New media is a scientific and technological product under the background of the new era, and now new media technology has been widely used in all aspects of social development, and it has spawned the arrival of the new media era. In the new media environment, the survival and development environment faced by enterprises have undergone earth-shaking changes; if you still follow the traditional marketing methods, it will not only reduce the effectiveness of marketing but also because of the deviation from the development requirements of the times be eliminated, bringing threats to the survival and development of enterprises. The development of new media, represented by the Internet and mobile phones, has brought great changes to various industries in the world including tourism. The market size of the travel network is expanding, and online sales are soaring. At the same time, all kinds of tourism organizations often have blind optimism in the construction of tourism networks and lack of reasonable application means, so that tourism network marketing is far from exerting its potential in reality; on the one hand, it cannot provide transaction services that satisfy both supply and demand; on the other hand, it also affects the economic benefits of tourism organizations. Compared with the rapid development of the tourism media industry, the theoretical research related to the actual situation lags significantly. Although the number of related studies at home and abroad is increasing, most of them stay at the level of explanation and introduction, and the analysis of the tourism industry chain is not enough. The breadth and depth of the study are sorely lacking. The future letter needs to summarize the successes and failures of the development of the tourism media industry from a theoretical height and carry out theoretical improvement to guide practice, and it also needs to strengthen forward-looking research, summarize the laws and models, and put forward practical implementation plans to improve the overall development level of the tourism and media industry. Aiming at the problem of new media marketing strategy in shopping tourism scenic spots, this paper applies descriptive research, questionnaire survey, statistical processing, mathematical model, and other research methods to analyze the characteristics of new media such as prominence of personalization, increased audience selectivity, and diversified forms of expression presented by new media compared with traditional media, as well as the differences between the two in terms of communication status, dominant state, and audience state. 4I Marketing theory is applied to new media marketing, the changes in the marketing environment and marketing needs under the background of new media, and the extensive impact of these changes on the marketing mix strategy; the basic strategies and expected effects of new media marketing of tourism enterprises are studied from the aspects of product, price, channel, and promotion; the IPA model is constructed, and the empirical analysis of the shopping tourist attractions of Changshu Garment City in China is carried out. Through the research of this paper, in theory, the understanding of new media marketing is enriched, and in fact, it provides guidance for optimizing the new media marketing strategy of tourist attractions.
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Ahsan, Mohammad, Madhu Kumari, Tajinder Singh, and Triveni Lal Pal. "Sentiment Based Information Diffusion in Online Social Networks." International Journal of Knowledge Discovery in Bioinformatics 8, no. 1 (January 2018): 60–74. http://dx.doi.org/10.4018/ijkdb.2018010105.
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This article describes how social media has emerged as a main vehicle of information diffusion among people. They often share their experience, feelings and knowledge through these channels. Some pieces of information quickly reach a large number of people, while others not. The authors analyzed this variation by collecting tweets on 2016 U.S. presidential election. This article gives a comprehensive understanding of how sentiment encoded in the textual contents can affects the information diffusion, along with the effect of content features, i.e., URLs, hashtags, and contextual features, i.e., number of followers, followees, tweets generated by the user so far, account age, tweet age. In order to explore the relationship between sentiment content and information diffusion, the authors first checked the features' significance as an indicator of diffusibility by using random forests. Finally, support vectors and k-Neighbors regression models are used to capture the complete dynamics of information diffusion. Experiments and results clearly reveal that sentiment prominently helps in making a better prediction of information diffusion.
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Hao, Yajing, Shaoting Tang, Longzhao Liu, Hongwei Zheng, Xin Wang, and Zhiming Zheng. "Local-Forest Method for Superspreaders Identification in Online Social Networks." Entropy 24, no. 9 (September 11, 2022): 1279. http://dx.doi.org/10.3390/e24091279.
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Identifying the most influential spreaders in online social networks plays a prominent role in affecting information dissemination and public opinions. Researchers propose many effective identification methods, such as k-shell. However, these methods are usually validated by simulating propagation models, such as epidemic-like models, which rarely consider the Push-Republish mechanism with attenuation characteristic, the unique and widely-existing spreading mechanism in online social media. To address this issue, we []proposefirst adopt the Push-Republish (PR) model []to describe the spreading processes in online social networksas the underlying spreading process to check the performance of identification methods. Then, we find that the performance of classical identification methods significantly []reducesdecreases in the PR model compared to epidemic-like models, especially when identifying the top 10% of superspreaders. Furthermore, inspired by the local tree-like structure caused by the PR model, we propose a new identification method, namely the Local-Forest (LF) method, and conduct extensive experiments in four real large networks to evaluate it. Results highlight that the Local-Forest method has the best performance in accurately identifying superspreaders compared with the classical methods.
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Shah, Jaymeen R., and Hsun-Ming Lee. "Building Online Social Networks to Engage Female Students in Information Systems." International Journal of Web-Based Learning and Teaching Technologies 10, no. 4 (October 2015): 33–51. http://dx.doi.org/10.4018/ijwltt.2015100103.
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During the next decade, enrollment growth in Information Systems (IS) related majors is unlikely to meet the predicted demand for qualified IS graduates. Gender imbalance in the IS related program makes the situation worse as enrollment and retention of women in the IS major has been proportionately low compared to male. In recent years, majority of high school and college students have integrated social networking sites in their daily life and habitually use these sites. Providing female students access to role models via an online social network may enhance their motivation to continue as an IS major and pursue a career in IS field. For this study, the authors follow the action research process – exploration of information systems development. In particular, a Facebook application was developed to build the social network connecting role models and students. Using the application, a basic framework is tested based on the gender of participants. The results suggest that it is necessary to have adequate number of role models accessible to students as female role-models tend to select fewer students to develop relationships with a preference for female students. Female students likely prefer composite role models from a variety of sources. This pilot study yields valuable lessons to provide informal learning fostered by role modeling via online social networks. The Facebook application may be further expanded to enhance female students' interests in IS related careers.
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Ai, Chuan, Bin Chen, Hailiang Chen, Weihui Dai, and Xiaogang Qiu. "Geographical Structural Features of the WeChat Social Networks." ISPRS International Journal of Geo-Information 9, no. 5 (May 1, 2020): 290. http://dx.doi.org/10.3390/ijgi9050290.
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Recently, spatial interaction analysis of online social networks has become a big concern. Early studies of geographical characteristics analysis and community detection in online social networks have shown that nodes within the same community might gather together geographically. However, the method of community detection is based on the idea that there are more links within the community than that connect nodes in different communities, and there is no analysis to explain the phenomenon. The statistical models for network analysis usually investigate the characteristics of a network based on the probability theory. This paper analyzes a series of statistical models and selects the MDND model to classify links and nodes in social networks. The model can achieve the same performance as the community detection algorithm when analyzing the structure in the online social network. The construction assumption of the model explains the reasons for the geographically aggregating of nodes in the same community to a degree. The research provides new ideas and methods for nodes classification and geographic characteristics analysis of online social networks and mobile communication networks and makes up for the shortcomings of community detection methods that do not explain the principle of network generation. A natural progression of this work is to geographically analyze the characteristics of social networks and provide assistance for advertising delivery and Internet management.
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Bartal, Alon, and Kathleen M. Jagodnik. "Role-Aware Information Spread in Online Social Networks." Entropy 23, no. 11 (November 19, 2021): 1542. http://dx.doi.org/10.3390/e23111542.
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Understanding the complex process of information spread in online social networks (OSNs) enables the efficient maximization/minimization of the spread of useful/harmful information. Users assume various roles based on their behaviors while engaging with information in these OSNs. Recent reviews on information spread in OSNs have focused on algorithms and challenges for modeling the local node-to-node cascading paths of viral information. However, they neglected to analyze non-viral information with low reach size that can also spread globally beyond OSN edges (links) via non-neighbors through, for example, pushed information via content recommendation algorithms. Previous reviews have also not fully considered user roles in the spread of information. To address these gaps, we: (i) provide a comprehensive survey of the latest studies on role-aware information spread in OSNs, also addressing the different temporal spreading patterns of viral and non-viral information; (ii) survey modeling approaches that consider structural, non-structural, and hybrid features, and provide a taxonomy of these approaches; (iii) review software platforms for the analysis and visualization of role-aware information spread in OSNs; and (iv) describe how information spread models enable useful applications in OSNs such as detecting influential users. We conclude by highlighting future research directions for studying information spread in OSNs, accounting for dynamic user roles.
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Wang, Cheng, Bo Yang, Jipeng Cui, and Chaodong Wang. "Fusing Behavioral Projection Models for Identity Theft Detection in Online Social Networks." IEEE Transactions on Computational Social Systems 6, no. 4 (August 2019): 637–48. http://dx.doi.org/10.1109/tcss.2019.2917003.
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Wang, Yuda, and Gang Li. "The Spreading of Information in Online Social Networks through Cellular Automata." Complexity 2018 (November 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/1890643.
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Epidemic dynamics in complex networks have been extensively studied. Due to the similarity between information and disease spreading, most studies on information dynamics use epidemic models and merely consider the characteristics of online social networks and individual’s cognitive. In this paper, we propose an online social networks information spreading (OSIS) model combining epidemic models and individual’s cognitive psychology. Then we design a cellular automata (CA) method to provide a computational method for OSIS. Finally, we use OSIS and CA to simulate the spreading and evolution of information in online social networks. The experimental results indicate that OSIS is effective. Firstly, individual’s cognition affects online information spreading. When infection rate is low, it prevents the spreading, whereas when infection rate is sufficiently high, it promotes transmission. Secondly, the explosion of online social network scale and the convenience of we-media greatly increase the ability of information dissemination. Lastly, the demise of information is affected by both time and heat decay rather than probability. We believe that these findings are in the right direction for perceiving information spreading in online social networks and useful for public management policymakers seeking to design efficient programs.
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Scellato, Salvatore, Anastasios Noulas, Renaud Lambiotte, and Cecilia Mascolo. "Socio-Spatial Properties of Online Location-Based Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 329–36. http://dx.doi.org/10.1609/icwsm.v5i1.14094.
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The spatial structure of large-scale online social networks has been largely unaccessible due to the lack of available and accurate data about people’s location. However, with the recent surging popularity of location-based social services, data about the geographic position of users have been available for the first time, together with their online social connections. In this work we present a comprehensive study of the spatial properties of the social networks arising among users of three main popular online location-based services. We observe robust universal features across them: while all networks exhibit about 40% of links below 100 km, we further discover strong heterogeneity across users, with different characteristic spatial lengths of interaction across both their social ties and social triads. We provide evidence that mechanisms akin to gravity models may influence how these social connections are created over space. Our results constitute the first large-scale study to unravel the socio-spatial properties of online location-based social networks.
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Dai, Chenyun, Fang-Yu Rao, Traian Marius Truta, and Elisa Bertino. "Privacy-Preserving Assessment of Social Network Data Trustworthiness." International Journal of Cooperative Information Systems 23, no. 02 (June 2014): 1441004. http://dx.doi.org/10.1142/s0218843014410044.
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Extracting useful knowledge from social network datasets is a challenging problem. While large online social networks such as Facebook and LinkedIn are well known and gather millions of users, small social networks are today becoming increasingly common. Many corporations already use existing social networks to connect to their customers. Seeing the increasing usage of small social networks, such companies will likely start to create in-house online social networks where they will own the data shared by customers. The trustworthiness of these online social networks is essentially important for decision making of those companies. In this paper, our goal is to assess the trustworthiness of local social network data by referencing external social networks. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.