scholarly journals Mutual Influence of Users Credibility and News Spreading in Online Social Networks

2021 ◽  
Vol 13 (5) ◽  
pp. 107
Author(s):  
Vincenza Carchiolo ◽  
Alessandro Longheu ◽  
Michele Malgeri ◽  
Giuseppe Mangioni ◽  
Marialaura Previti
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Mutual Influence ◽  
Collective Intelligence ◽  
Online Information ◽  
Fake News ◽  
Potential Growth ◽  
Multiplex Network ◽  
Time News ◽  
Information Providers

A real-time news spreading is now available for everyone, especially thanks to Online Social Networks (OSNs) that easily endorse gate watching, so the collective intelligence and knowledge of dedicated communities are exploited to filter the news flow and to highlight and debate relevant topics. The main drawback is that the responsibility for judging the content and accuracy of information moves from editors and journalists to online information users, with the side effect of the potential growth of fake news. In such a scenario, trustworthiness about information providers cannot be overlooked anymore, rather it more and more helps in discerning real news from fakes. In this paper we evaluate how trustworthiness among OSN users influences the news spreading process. To this purpose, we consider the news spreading as a Susceptible-Infected-Recovered (SIR) process in OSN, adding the contribution credibility of users as a layer on top of OSN. Simulations with both fake and true news spreading on such a multiplex network show that the credibility improves the diffusion of real news while limiting the propagation of fakes. The proposed approach can also be extended to real social networks.

scholarly journals Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks

2021 ◽  
Vol 13 (3) ◽  
pp. 76
Author(s):  
Quintino Francesco Lotito ◽  
Davide Zanella ◽  
Paolo Casari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Simulation Models ◽  
Epidemic Models ◽  
Time Varying ◽  
Fake News ◽  
Network Access ◽  
Spreading Process ◽  
Network Topologies ◽  
Access Patterns

The pervasiveness of online social networks has reshaped the way people access information. Online social networks make it common for users to inform themselves online and share news among their peers, but also favor the spreading of both reliable and fake news alike. Because fake news may have a profound impact on the society at large, realistically simulating their spreading process helps evaluate the most effective countermeasures to adopt. It is customary to model the spreading of fake news via the same epidemic models used for common diseases; however, these models often miss concepts and dynamics that are peculiar to fake news spreading. In this paper, we fill this gap by enriching typical epidemic models for fake news spreading with network topologies and dynamics that are typical of realistic social networks. Specifically, we introduce agents with the role of influencers and bots in the model and consider the effects of dynamical network access patterns, time-varying engagement, and different degrees of trust in the sources of circulating information. These factors concur with making the simulations more realistic. Among other results, we show that influencers that share fake news help the spreading process reach nodes that would otherwise remain unaffected. Moreover, we emphasize that bots dramatically speed up the spreading process and that time-varying engagement and network access change the effectiveness of fake news spreading.

Fake News Polarization for Sentiment Analysis

2022 ◽  
pp. 255-263
Author(s):  
Chirag Visani ◽  
Vishal Sorathiya ◽  
Sunil Lavadiya
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Sentiment Analysis ◽  
Online Social Networks ◽  
Opinion Mining ◽  
Easy Access ◽  
The Internet ◽  
Fake News ◽  
Social Media Networks ◽  
Sentiment Mining

The popularity of the internet has increased the use of e-commerce websites and news channels. Fake news has been around for many years, and with the arrival of social media and modern-day news at its peak, easy access to e-platform and exponential growth of the knowledge available on social media networks has made it intricate to differentiate between right and wrong information, which has caused large effects on the offline society already. A crucial goal in improving the trustworthiness of data in online social networks is to spot fake news so the detection of spam news becomes important. For sentiment mining, the authors specialise in leveraging Facebook, Twitter, and Whatsapp, the most prominent microblogging platforms. They illustrate how to assemble a corpus automatically for sentiment analysis and opinion mining. They create a sentiment classifier using the corpus that can classify between fake, real, and neutral opinions in a document.

A Study on Online Social Networks Theme Semantic Computing Model

2016 ◽  
Vol 13 (4) ◽  
pp. 67-90
Author(s):  
Chen Fu ◽  
Xu Yuemei ◽  
Ni Yihan
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Semantic Analysis ◽  
Great Influence ◽  
Semantic Distance ◽  
Spreading Rate ◽  
Online Information ◽  
Network Information ◽  
Huge Impact ◽  
Semantic Computing

The widespread use of Mobile Intelligent Terminals and ubiquitous access to networks has enabled online information sources including Weibo and Wechat to bring huge impact to the society. Only a few words of network information can expand rapidly and catalyze the generation of a huge amount of information. The highly real-time content, fission-like spreading rate and enormous public opinion guiding forces created in this process will cast great influence on the society. Thus, semantic computing on online social networks and research on topics about emergencies have great significance. In this article, a numerical model of text semantic analysis based on artificial neural network is proposed, and a semantic computational algorithm for social network texts as well as a discovery algorithm for emergencies is provided with reference to the information provided by the social nodes itself and the semantic of the text. Through the numerization of text, the calculation and comparison of semantic distance, the classification of nodes and the discovery of community can be realized. In this article, semantic vector of micro-information for nodes and closure extension of semantic extensions are defined in order to build up an equivalence of short sentences, and in turn realize the discovery of emergencies. Then, huge quantities of Sina Weibo contents are collected to verify the model and algorithm put forward in this article. In the end, outlooks for future jobs are provided.

A factor-identifying study of the user-perceived value of collective intelligence based on online social networks

2018 ◽  
Vol 28 (3) ◽  
pp. 696-715 ◽  
Author(s):  
Sung-Shun Weng ◽  
Ming-Hsien Yang ◽  
Pei-I Hsiao
Keyword(s):  
Social Networks ◽  
Predictive Models ◽  
Online Social Networks ◽  
Collective Intelligence ◽  
Perceived Value ◽  
Research Strategy ◽  
Measurement Scale ◽  
Measurement Instruments ◽  
Content Type ◽  
Quantitative Research Methods

Purpose An important issue for researchers and managers of organizations is the understanding of user-perceived values of collective intelligence (UPVoCI) in online social networks (OSNs) with the purpose of helping organizations identify the values that cause internet users and members of OSNs to share information and knowledge during they participate in collective intelligence (co-intelligence) activities. However, the development of measurement instruments and predictive models and rules for predicting UPVoCI are inadequate. The paper aims to discuss these issues. Design/methodology/approach A novel measurement scale was developed to measure UPVoCI using a user-oriented research strategy that is based on qualitative and quantitative research methods. This work also identified critical indicators and constructed predictive models and rules for forecasting UPVoCI by multivariate statistical methods and data mining. Findings A 17-item scale of UPVoCI was developed and 17 measurement items were associated with two major dimensions, which are the user-perceived social value of co-intelligence and the user-perceived problem-solving value of co-intelligence. Ten critical indicators of UPVoCI that are important in predicting UPVoCI and 12 rules for predicting UPVoCI were identified and a refined model for predicting UPVoCI was constructed. Research limitations/implications The results in this work allow organizations to determine the perceived value of members of OSNs and the benefits of their participating in co-intelligence activities, as a basis for adjusting user-oriented online co-intelligence and service strategies with the goal of improving collaborative innovation performance. Originality/value This work systematically developed a novel scale for measuring UPVoCI in OSNs and constructed new models and rules for predicting UPVoCI in OSNs.

scholarly journals Defensive Modeling of Fake News Through Online Social Networks

2020 ◽  
Vol 7 (5) ◽  
pp. 1159-1167
Author(s):  
Gulshan Shrivastava ◽  
Prabhat Kumar ◽  
Rudra Pratap Ojha ◽  
Pramod Kumar Srivastava ◽  
Senthilkumar Mohan ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Fake News

scholarly journals Link prediction in multiplex online social networks

2017 ◽  
Vol 4 (2) ◽  
pp. 160863 ◽  
Author(s):  
Mahdi Jalili ◽  
Yasin Orouskhani ◽  
Milad Asgari ◽  
Nazanin Alipourfard ◽  
Matjaž Perc
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Link Prediction ◽  
Support Vector ◽  
Svm Classifier ◽  
Multiplex Network ◽  
Average Accuracy ◽  
Potential Applications ◽  
Meta Path ◽  
Highly Correlated

Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

scholarly journals An Enhanced Sybil Guard to Detect Bots in Online Social Networks

2021 ◽  
Author(s):  
Nisha P. Shetty ◽  
Balachandra Muniyal ◽  
Arshia Anand ◽  
Sushant Kumar
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Networking ◽  
Online Social Networks ◽  
Social Networking Sites ◽  
Easy Access ◽  
Classifier Combination ◽  
Fake News ◽  
Wide Scale ◽  
Set Up

Sybil accounts are swelling in popular social networking sites such as Twitter, Facebook etc. owing to cheap subscription and easy access to large masses. A malicious person creates multiple fake identities to outreach and outgrow his network. People blindly trust their online connections and fall into trap set up by these fake perpetrators. Sybil nodes exploit OSN’s ready-made connectivity to spread fake news, spamming, influencing polls, recommendations and advertisements, masquerading to get critical information, launching phishing attacks etc. Such accounts are surging in wide scale and so it has become very vital to effectively detect such nodes. In this research a new classifier (combination of Sybil Guard, Twitter engagement rate and Profile statistics analyser) is developed to combat such Sybil nodes. The proposed classifier overcomes the limitations of structure based, machine learning based and behaviour-based classifiers and is proven to be more accurate and robust than the base Sybil guard algorithm.

scholarly journals Semantic Social Network Analysis

2011 ◽  
pp. 122-156 ◽  
Author(s):  
Guillaume Erétéo ◽  
Freddy Limpens ◽  
Fabien Gandon ◽  
Olivier Corby ◽  
Michel Buffa ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Web Sites ◽  
Online Social Networks ◽  
Collective Intelligence ◽  
Enterprise 2.0 ◽  
Web Framework ◽  
Collaborative Interactions ◽  
Key Features ◽  
Social Applications

In this chapter we present our approach to analyzing such semantic social networks and capturing collective intelligence from collaborative interactions to challenge requirements of Enterprise 2.0. Our tools and models have been tested on an anonymized dataset from Ipernity.com, one of the biggest French social web sites centered on multimedia sharing. This dataset contains over 60,000 users, around half a million declared relationships of three types, and millions of interactions (messages, comments on resources, etc.). We show that the enriched semantic web framework is particularly well-suited for representing online social networks, for identifying their key features and for predicting their evolution. Organizing huge quantity of socially produced information is necessary for a future acceptance of social applications in corporate contexts.

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