Optimal structure of groups under exposure to fake news

Abstract Humans predominantly form their beliefs based on communication with other humans rather than direct observations, even on matters of facts, such as the shape of the globe or the effects of child vaccinations. Despite the fact that this is a well-known (not to say: trivial) observation, literature on opinion dynamics and opinion formation largely overlooks this circumstance. In the present paper we study the effects of limited access to information on the level of knowledge of members of groups embedded into an environment that can be observed. We also study the consequences of false information circulating within the group. We find that exposure to fake news makes intense communication counterproductive, but, at the same time, calls forth diversification of agents with respect to their information spreading abilities.

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Opinion Dynamics Optimization by Varying Susceptibility to Persuasion via Non-Convex Local Search

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3466617 ◽

2021 ◽

Vol 16 (2) ◽

pp. 1-34

Author(s):

Rediet Abebe ◽

T.-H. HUBERT Chan ◽

Jon Kleinberg ◽

Zhibin Liang ◽

David Parkes ◽

...

Keyword(s):

Local Search ◽

Objective Function ◽

Iterative Process ◽

Large Scale ◽

Opinion Dynamics ◽

Theoretical Models ◽

Global Optimum ◽

Local Optimum ◽

Opinion Formation ◽

Long Line

A long line of work in social psychology has studied variations in people’s susceptibility to persuasion—the extent to which they are willing to modify their opinions on a topic. This body of literature suggests an interesting perspective on theoretical models of opinion formation by interacting parties in a network: in addition to considering interventions that directly modify people’s intrinsic opinions, it is also natural to consider interventions that modify people’s susceptibility to persuasion. In this work, motivated by this fact, we propose an influence optimization problem. Specifically, we adopt a popular model for social opinion dynamics, where each agent has some fixed innate opinion, and a resistance that measures the importance it places on its innate opinion; agents influence one another’s opinions through an iterative process. Under certain conditions, this iterative process converges to some equilibrium opinion vector. For the unbudgeted variant of the problem, the goal is to modify the resistance of any number of agents (within some given range) such that the sum of the equilibrium opinions is minimized; for the budgeted variant, in addition the algorithm is given upfront a restriction on the number of agents whose resistance may be modified. We prove that the objective function is in general non-convex. Hence, formulating the problem as a convex program as in an early version of this work (Abebe et al., KDD’18) might have potential correctness issues. We instead analyze the structure of the objective function, and show that any local optimum is also a global optimum, which is somehow surprising as the objective function might not be convex. Furthermore, we combine the iterative process and the local search paradigm to design very efficient algorithms that can solve the unbudgeted variant of the problem optimally on large-scale graphs containing millions of nodes. Finally, we propose and evaluate experimentally a family of heuristics for the budgeted variant of the problem.

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Intelligent Detection of False Information in Arabic Tweets Utilizing Hybrid Harris Hawks Based Feature Selection and Machine Learning Models

Symmetry ◽

10.3390/sym13040556 ◽

2021 ◽

Vol 13 (4) ◽

pp. 556

Author(s):

Thaer Thaher ◽

Mahmoud Saheb ◽

Hamza Turabieh ◽

Hamouda Chantar

Keyword(s):

Machine Learning ◽

Social Media ◽

Feature Selection ◽

Language Processing ◽

User Profile ◽

Vital Role ◽

Classification Model ◽

Fake News ◽

False Information ◽

Social Media Platforms

Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

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PHYSICS OF THE MIND: OPINION DYNAMICS AND DECISION MAKING PROCESSES BASED ON A BINARY NETWORK MODEL

International Journal of Modern Physics B ◽

10.1142/s0217979208050231 ◽

2008 ◽

Vol 22 (25n26) ◽

pp. 4482-4494 ◽

Cited By ~ 7

Author(s):

F. V. KUSMARTSEV ◽

KARL E. KÜRTEN

Keyword(s):

Decision Making ◽

Social Networks ◽

Chaotic Behavior ◽

Opinion Dynamics ◽

Human Mind ◽

Critical Parameters ◽

Opinion Formation ◽

Natural Catastrophes ◽

Risk Condition ◽

Binary Network

We propose a new theory of the human mind. The formation of human mind is considered as a collective process of the mutual interaction of people via exchange of opinions and formation of collective decisions. We investigate the associated dynamical processes of the decision making when people are put in different conditions including risk situations in natural catastrophes when the decision must be made very fast or at national elections. We also investigate conditions at which the fast formation of opinion is arising as a result of open discussions or public vote. Under a risk condition the system is very close to chaos and therefore the opinion formation is related to the order disorder transition. We study dramatic changes which may happen with societies which in physical terms may be considered as phase transitions from ordered to chaotic behavior. Our results are applicable to changes which are arising in various social networks as well as in opinion formation arising as a result of open discussions. One focus of this study is the determination of critical parameters, which influence a formation of stable mind, public opinion and where the society is placed “at the edge of chaos”. We show that social networks have both, the necessary stability and the potential for evolutionary improvements or self-destruction. We also show that the time needed for a discussion to take a proper decision depends crucially on the nature of the interactions between the entities as well as on the topology of the social networks.

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The Combination of Pairwise and Group Interactions Promotes Consensus in Opinion Dynamics

Complexity ◽

10.1155/2021/4382836 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Xiaoxuan Liu ◽

Changwei Huang ◽

Haihong Li ◽

Qionglin Dai ◽

Junzhong Yang

Keyword(s):

Complex Systems ◽

Numerical Simulations ◽

Complete Graph ◽

Group Interaction ◽

Opinion Dynamics ◽

Pairwise Interaction ◽

Network Structures ◽

Opinion Formation ◽

Group Interactions ◽

Bounded Confidence

In complex systems, agents often interact with others in two distinct types of interactions, pairwise interaction and group interaction. The Deffuant–Weisbuch model adopting pairwise interaction and the Hegselmann–Krause model adopting group interaction are the two most widely studied opinion dynamics. In this study, we propose a novel opinion dynamics by combining pairwise and group interactions for agents and study the effects of the combination on consensus in the population. In the model, we introduce a parameter α to control the weights of the two interactions in the dynamics. Through numerical simulations, we find that there exists an optimal α , which can lead to a highest probability of complete consensus and minimum critical bounded confidence for the formation of consensus. Furthermore, we show the effects of α on opinion formation by presenting the observations for opinion clusters. Moreover, we check the robustness of the results on different network structures and find the promotion of opinion consensus by α not limited to a complete graph.

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Fake and Real News detection Using Python

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst207376 ◽

2020 ◽

pp. 423-428

Author(s):

Fakhra Akhtar ◽

Faizan Ahmed Khan

Keyword(s):

Social Media ◽

Fake News ◽

False Information ◽

False Negatives ◽

Mainstream Media ◽

Social Media Platforms ◽

The Masses ◽

Negative Attributes ◽

Entire Dataset ◽

F Measure

<p>In the digital age, fake news has become a well-known phenomenon. The spread of false evidence is often used to confuse mainstream media and political opponents, and can lead to social media wars, hatred arguments and debates.Fake news is blurring the distinction between real and false information, and is often spread on social media resulting in negative views and opinions. Earlier Research describe the fact that false propaganda is used to create false stories on mainstream media in order to cause a revolt and tension among the masses The digital rights foundation DRF report, which builds on the experiences of 152 journalists and activists in Pakistan, presents that more than 88 % of the participants find social media platforms as the worst source for information, with Facebook being the absolute worst. The dataset used in this paper relates to Real and fake news detection. The objective of this paper is to determine the Accuracy , precision , of the entire dataset .The results are visualized in the form of graphs and the analysis was done using python. The results showed the fact that the dataset holds 95% of the accuracy. The number of actual predicted cases were 296. Results of this paper reveals that The accuracy of the model dataset is 95.26 % the precision results 95.79 % whereas recall and F-Measure shows 94.56% and 95.17% accuracy respectively.Whereas in predicted models there are 296 positive attributes , 308 negative attributes 17 false positives and 13 false negatives. This research recommends that authenticity of news should be analysed first instead of drafting an opinion, sharing fake news or false information is considered unethical journalists and news consumers both should act responsibly while sharing any news.</p>

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A family of falsehoods: Deception, media hoaxes and fake news

Newspaper Research Journal ◽

10.1177/0739532918796228 ◽

2018 ◽

Vol 39 (3) ◽

pp. 350-361 ◽

Cited By ~ 5

Author(s):

Teri Finneman ◽

Ryan J. Thomas

Keyword(s):

Social Media ◽

Fake News ◽

Public Officials ◽

False Information ◽

Media Environment ◽

News Organizations ◽

Information Spread ◽

History Of

“Fake news” became a concern for journalists in 2017 as news organizations sought to differentiate themselves from false information spread via social media, websites and public officials. This essay examines the history of media hoaxing and fake news to help provide context for the current U.S. media environment. In addition, definitions of the concepts are proposed to provide clarity for researchers and journalists trying to explain these phenomena.

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The impact of the information factor on modern international relations

SCIENTIFIC WORK ◽

10.36719/2663-4619/62/141-146 ◽

2021 ◽

Vol 62 (01) ◽

pp. 141-146

Author(s):

Gulnaz Tahir Hasanova ◽

Keyword(s):

International Relations ◽

Outer Space ◽

Cyber Attacks ◽

The Internet ◽

Information Warfare ◽

Fake News ◽

False Information ◽

Fifth Dimension ◽

Private Actors ◽

The Impact

This study aims to highlight the growing strategic importance that cyberspace is gaining in the dynamics of international politics. After land, sea, air, and outer space, cyberspace is the fifth dimension of conflict. The type of non-military weapons used to fight, as well as the subjects targeted, make civilian systems new centers of gravity to defend against an enemy that most often "operates in the shadows." The international scenario rmation revolution (which contributed to the "democratization of information"), is radically evolving from a unipolar (American-led) to an almost multipolar architecture. The Internet today is an indispensable communication and information network for various legal and illegal subjects of international relations. Social networks (Facebook, Twitter, Telegram) play a very important role in this process. The Internet can also allow manipulation or even destabilization of the international community with the spread of false information (fake news). It is also a field for intelligence activities. Finally, the Internet is becoming the field of a new form of confrontation. Thus, both states and private actors protect themselves from possible cyber attacks by developing cybersecurity. In anticipation of this, states are developing cyberspace strategies and military-digital capabilities. Key words: international relations, information, cyberspace, cybersecurity, territorial integrity, state, subjects of international relations, information warfare

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Social Space in Simulation Models

Interdisciplinary Applications of Agent-Based Social Simulation and Modeling - Advances in Human and Social Aspects of Technology ◽

10.4018/978-1-4666-5954-4.ch012 ◽

2014 ◽

pp. 200-219

Author(s):

Davide Nunes ◽

Luis Antunes

Keyword(s):

Social Relations ◽

Social Space ◽

Opinion Dynamics ◽

Simulation Models ◽

Opinion Formation ◽

Consensus Formation ◽

Space Design ◽

Network Topologies ◽

Multiple Interaction ◽

Set Up

In real world scenarios, the formation of consensus is a self-organisation process by which actors have to make a joint assessment about a target subject, be it a decision making problem or the formation of a collective opinion. In social simulation, models of opinion dynamics tackle the opinion formation phenomena. These models try to make an assessment, for instance, of the ideal conditions that lead an interacting group of agents to opinion consensus, polarisation or fragmentation. This chapter investigates the role of social relation structure in opinion dynamics and consensus formation. The authors present an agent-based model that defines social relations as multiple concomitant social networks and explore multiple interaction games in this structural set-up. They discuss the influence of complex social network topologies where actors interact in multiple distinct networks. The chapter builds on previous work about social space design with multiple social relations to determine the influence of such complex social structures in a process such as opinion formation.

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Fake News and Information Warfare

Advances in Media, Entertainment, and the Arts - Handbook of Research on Deception, Fake News, and Misinformation Online ◽

10.4018/978-1-5225-8535-0.ch011 ◽

2019 ◽

pp. 167-191 ◽

Cited By ~ 1

Author(s):

Rosanna E. Guadagno ◽

Karen Guttieri

Keyword(s):

Presidential Election ◽

Information Age ◽

Psychological Research ◽

Individual Factors ◽

Cyber Warfare ◽

Information Warfare ◽

Fake News ◽

False Information ◽

Russian Government ◽

Key Terms

Fake news—false information passed off as factual—is an effective weapon in the information age. For instance, the Russian government perfected techniques used in its 2007 Estonian and 2008 Georgian cyber campaigns to support Donald Trump's successful candidacy in the 2016 United States presidential election. In this chapter, the authors examine fake news and Russia's cyberwarfare efforts across time as case studies of information warfare. The chapter identifies key terms and reviews extant political science and psychological research related to obtaining an understanding of psychological cyber warfare (“psywar”) through the proliferation of fake news. Specifically, the authors suggest that there are social, contextual, and individual factors that contribute to the spread and influence of fake news and review these factors in this chapter.

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