Characterizing and predicting fake news spreaders in social networks

2021 ◽  
Author(s):  
Anu Shrestha ◽  
Francesca Spezzano
Keyword(s):  
Social Networks ◽  
Fake News

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 im Wahlkampf

2021 ◽  
Author(s):  
Jessica Flint
Keyword(s):  
Social Networks ◽  
Public Opinion ◽  
Fundamental Rights ◽  
Election Campaigns ◽  
Fake News ◽  
Forming Process ◽  
Private Companies ◽  
The Future

The urgency of regulating fake news on social networks regarding election campaigns is more evident than ever. This poses considerable difficulties for legislative practice. It is important to consider the fundamental rights of the parties involved without the state's influence on the formation of public opinion becoming too great. The current options of reacting to fake news do not suffice to ensure a free opinion-forming process. This publication makes an innovative proposal as to how social networks – especially Facebook – can be regulated in the future in such a way that the discourse is strengthened and the alarming influence of private companies on the formation of opinion is limited.

Characterization of Fake News Based on Subjectivity Lexicons

2020 ◽  
Vol 1 (4) ◽  
pp. 419-441
Author(s):  
Caio L.M. Jeronimo ◽  
Leandro B. Marinho ◽  
Cclaudio E.C. Carmpelo ◽  
Adriano Veloso ◽  
Allan S. Da Costa Melo
Keyword(s):  
Social Networks ◽  
Brazilian Portuguese ◽  
News Article ◽  
Bag Of Words ◽  
Fake News ◽  
Feature Vectors ◽  
Textual Content

While many works investigate spread patterns of fake news in social networks, we focus on the textual content. Instead of relying on syntactic representations of documents (aka Bag of Words) as many works do, we seek more robust representations that may better differentiate fake from legitimate news. We propose to consider the subjectivity of news under the assumption that the subjectivity levels of legitimate and fake news are significantly different. For computing the subjectivity level of news, we rely on a set subjectivity lexicons for both Brazilian Portuguese and English languages. We then build subjectivity feature vectors for each news article by calculating the Word Mover's Distance (WMD) between the news and these lexicons considering the embedding the news words lie in, in order to analyze and classify the documents. The results demonstrate that our method is robust, especially in scenarios where training and test domains are different.

scholarly journals A Study of Rumor Detection based on Social Network Topic Models Relationship

2020 ◽  
Author(s):  
Diogo Nolasco ◽  
Jonice Oliveira
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Local Community ◽  
Topic Model ◽  
Fake News ◽  
Detection Problem ◽  
Model Method ◽  
Show Evidence ◽  
Rumor Detection ◽  
Scientific Topic

The rumor detection problem on social networks has attracted considerable attention in recent years with the rise of concerns about fake news and disinformation. Most previous works focused on detecting rumors by individual messages, classifying whether a post or blog entry is considered a rumor or not. This paper proposes a method for rumor detection on topic-level that identifies whether a social topic related to a scientific topic is a rumor. We propose the use of a topic model method on social and scientific domains and correlate the topics found to detect the most prone to be rumors. Results applied in the Zika epidemic scenario show evidence that the least correlated topics contain a mix of rumors and local community discussions.

scholarly journals Coronavirus in Spain: Fear of ‘Official’ Fake News Boosts WhatsApp and Alternative Sources

2020 ◽  
Vol 8 (2) ◽  
pp. 462-466 ◽  
Author(s):  
Carlos Elías ◽  
Daniel Catalan-Matamoros
Keyword(s):  
Social Networks ◽  
The State ◽  
Fake News ◽  
Public Media ◽  
History Of ◽  
The Media ◽  
Alternative Sources ◽  
Official Sources

The communication of the Coronavirus crisis in Spain has two unexpected components: the rise of the information on social networks, especially WhatsApp, and the consolidation of TV programs on mystery and esotericism. Both have emerged to “tell the truth” in opposition to official sources and public media. For a country with a long history of treating science and the media as properties of the state, this very radical development has surprised communication scholars.

scholarly journals Mapeo del consumo de medios en los jóvenes: redes sociales, 'fake news' y confianza en tiempos de pandemia

2021 ◽  
Vol 11 (2) ◽  
pp. 187-208
Author(s):  
Ana Pérez-Escoda ◽  
◽  
Gema Barón-Dulce ◽  
Juana Rubio-Romero ◽  
◽  
...  
Keyword(s):  
Social Networks ◽  
Three Dimensions ◽  
Media Consumption ◽  
Quantitative Methodology ◽  
Fake News ◽  
Redes Sociales ◽  
Generation Z ◽  
The Media ◽  
Major Transformation ◽  
Source Of Information

The explosion of the Covid-19 pandemic has led to a major transformation in media consumption and the use of social networks. New habits and extensive exposure to connected devices coupled with unmanageable amounts of information warn of a worrying reality, especially among the younger population. The aim of this research is to discover the degree of trustworthiness of Generation Z towards the media, their media consumption preferences and the association they make between media consumption and fake news. Using a descriptive and exploratory quantitative methodology, a study is presented with a sample of 225 young people belonging to this population niche. The study addresses three dimensions: media consumption, social networks and perception of fake news. The results show that generation Z is an intensive consumer of the media they trust the least and perceive traditional media as the most trustworthy. The findings indicate that social networks are the main source of information consumption for this ge­neration, among other content, despite also being the least trustworthy and the most likely to distribute fake news according to their perceptions. There is a lack of media literacy from a critical rather than a formative perspective.

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.

scholarly journals Fake News in Social Networks

2017 ◽  
Author(s):  
Christoph Aymanns ◽  
Jakob Foerster ◽  
Co-Pierre Georg
Keyword(s):  
Social Networks ◽  
Fake News
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