Spread Of Information Research Articles

Prebunking earthquake predictions on social media

Earthquake misinformation has not only a significant impact on crisis management, but also on trust in scientific information and institutions. As a global center for seismic information, the Euro-Mediterranean Seismological Center (EMSC) has developed a strategy to combat false information related to earthquakes, particularly on Twitter (now known as X). This strategy includes the automation of prebunking tweets to prevent the spread of false information and, particularly unreliable claims of earthquake predictions. In the field of false information, predictions of earthquakes have a particular characteristic: their appearance follows a specific dynamic and the attention paid to them is predictable, which makes the automation of countermeasures (prebunking and debunking) both possible and relevant. Therefore, the objectives of the EMSC’s automatic tweets are to warn people in advance, particularly those who may be vulnerable to earthquake misinformation, while filling the information void with verified, scientifically-based information in the meantime. This paper seeks to examine the usefulness of such a tool. It does so by analyzing users’ engagement with these tweets. In addition, it focuses on two case studies, the seismic sequence in Turkey following the M7.8 earthquake on February 6, 2023 and the M6.8 Morocco earthquake on September 8, 2023. The results show engagement with the automatically generated tweets but an even greater interaction and engagement with manual tweets, highlighting the importance of a holistic approach. Recommendations include optimizing the visibility of the tweets, extending efforts beyond social media, and exploring alternative platforms. The paper advocates for a comprehensive strategy, combining automated tools with manual engagement and leveraging diverse communication channels to take into account cultural differences.

Advancing orthopaedic trauma care through WhatsApp: An analysis of clinical and non-clinical applications, challenges, and future directions.

The integration of WhatsApp, a widely-used instant messaging application (IMA), into the realm of orthopaedics and trauma surgery has emerged as a significant development in recent years. This paper explores the multifaceted role of WhatsApp in orthopaedics, focusing on its clinical and non-clinical applications, advantages, disadvantages, and future prospects. The study synthesizes findings from various research papers, emphasizing the growing reliance on mobile technology in healthcare. WhatsApp's role in orthopaedics is notable for its ease of use, real-time communication, and accessibility. Clinically, it facilitates triage, teleconsultation, diagnosis, treatment, patient advice, and post-operative monitoring. Non-clinically, it supports telemedicine, teleradiology, virtual fracture clinics, research, and education in orthopaedic surgery. The application has proven beneficial in enhancing communication among healthcare teams, providing quick responses, and motivating junior physicians. Its use in educational settings has been shown to improve learner's understanding and patient care. However, the use of WhatsApp in orthopaedics is not without challenges. Risks include the potential spread of misleading information, privacy concerns, and issues with image quality affecting diagnosis and treatment decisions. The paper acknowledges the importance of maintaining professional boundaries and the need for oversight measures to ensure content accuracy. Looking forward, the potential of WhatsApp and similar IMAs in orthopaedics lies in their ability to streamline data collection and retrieval, improve doctor-patient communication, and address challenges like bureaucratic red tape and limited resources. The paper suggests that future orthopaedic practice, particularly in emergency departments, will increasingly rely on such technologies for efficient patient management. This shift, however, must be approached with an understanding of the ethical, legal, and practical implications of integrating social media and mobile technology in healthcare.

Influence of Social Media on Court Decisions in Blasphemy Cases

This research aims to conduct an in-depth analysis of the influence of social media on judges' decisions in religious blasphemy cases in Indonesia. In the context of the digital era, social media has become the main arena where public opinion is expressed, discussions develop, and information spreads quickly. Cases of religious blasphemy often create waves of controversy and debate on social media, widening their impact in society. This research uses a qualitative approach to analyze how social media influences the judicial process, from its role in shaping public opinion to its possible influence on arguments presented in court. Through analysis of social media content and a study of significant religious blasphemy cases, this research aims to understand the extent to which opinions and pressure generated by social media can influence judges' decisions. It is hoped that the results of the analysis will provide a deeper understanding of the challenges faced by the justice system in facing the pressures and expectations of an increasingly dominant digital space, as well as provide a basis for improving justice policies and practices that are more adaptive to social and digital dynamics.

A novel spreading dynamic based on adoption against the trend

In the spreading dynamics of previous fashion trends, adoption researchers have neglected to consider that some individuals may behave differently from popular tendencies, which is called opposite-trend adoption behavior. To explore the dissemination mechanisms of the behavior, we first establish the adoption-against-trend model. Additionally, an edge division theory based on the adoption of opposite trends was proposed to quantitatively analyze this unique dissemination mechanism. This study presents three different degrees of opposite trends, each highlighting unique spreading scenarios. In the case of a strong opposite trend, no spreading occurs. In the case of a weak opposite trend, limited contact will accelerate information spreading, but it will not alter the mode of spreading. Nevertheless, in the case of a moderately opposite trend, the degree of the opposite trend alters the mode of spreading. Meanwhile, a cross-phase transition occurs. The findings of this paper can be applied to various areas, including social media and commercial trades.

Smoothed Analysis of Information Spreading in Dynamic Networks

The best known solutions for k -message broadcast in dynamic networks of size n require Ω ( nk ) rounds. In this article, we see if these bounds can be improved by smoothed analysis. To do so, we study perhaps the most natural randomized algorithm for disseminating tokens in this setting: at every timestep, choose a token to broadcast randomly from the set of tokens you know. We show that with even a small amount of smoothing (i.e., one random edge added per round), this natural strategy solves k -message broadcast in \(\tilde{O}(n+k^3)\) rounds, with high probability, beating the best known bounds for \(k=o(\sqrt {n})\) and matching the Ω ( n + k ) lower bound for static networks for k = O ( n 1/3 ) (ignoring logarithmic factors). In fact, the main result we show is even stronger and more general: Given ℓ-smoothing (i.e., ℓ random edges added per round), this simple strategy terminates in \(O(kn^{2/3}\log ^{1/3}(n)\ell ^{-1/3})\) rounds. We then prove this analysis close to tight with an almost-matching lower bound. To better understand the impact of smoothing on information spreading, we next turn our attention to static networks, proving a tight bound of \(\tilde{O}(k\sqrt {n})\) rounds to solve k -message broadcast, which is better than what our strategy can achieve in the dynamic setting. This confirms the intuition that although smoothed analysis reduces the difficulties induced by changing graph structures, it does not eliminate them altogether. Finally, we apply tools developed to support our smoothed analysis to prove an optimal result for k -message broadcast in so-called well-mixed networks in the absence of smoothing. By comparing this result to an existing lower bound for well-mixed networks, we establish a formal separation between oblivious and strongly adaptive adversaries with respect to well-mixed token spreading, partially resolving an open question on the impact of adversary strength on the k -message broadcast problem.

Dynamics of a new e-rumor model considering irresolute individuals

Nowadays, social networks are well known in our societies, as well as their advantages and disadvantages. The propagation of fake news can quickly become harmful in a political or economical context for example. Consequently, it is important to understand the dynamics of information spread to better control it. In this paper, we propose a new model of e-rumor where the novelty is to consider irresolute individuals. From this last one, we define a threshold R0 completely determining the dynamics of the model. If R0 is less than one, the rumor will die whereas if R0 is greater than one, the rumor will persist.

Infection patterns in simple and complex contagion processes on networks.

Contagion processes, representing the spread of infectious diseases, information, or social behaviors, are often schematized as taking place on networks, which encode for instance the interactions between individuals. The impact of the network structure on spreading process has been widely investigated, but not the reverse question: do different processes unfolding on a given network lead to different infection patterns? How do the infection patterns depend on a model's parameters or on the nature of the contagion processes? Here we address this issue by investigating the infection patterns for a variety of models. In simple contagion processes, where contagion events involve one connection at a time, we find that the infection patterns are extremely robust across models and parameters. In complex contagion models instead, in which multiple interactions are needed for a contagion event, non-trivial dependencies on models parameters emerge, as the infection pattern depends on the interplay between pairwise and group contagions. In models involving threshold mechanisms moreover, slight parameter changes can significantly impact the spreading paths. Our results show that it is possible to study crucial features of a spread from schematized models, and inform us on the variations between spreading patterns in processes of different nature.

Competitive Influence Maximization in Trust-Based Social Networks With Deep Q-Learning

Social network analysis is a rapidly evolving research area having several real-life application areas, e.g. digital marketing, epidemiology, spread of misinformation. Influence maximization aims to select a subset of nodes in such manner that the information propagated over the network is maximized. Competitive influence maximization, which describes the phenomena of multiple actors competing for resources within the same infrastructure, can be solved with a greedy approach selecting the seed nodes utilizing the influence strength between nodes. Recently, deep reinforcement learning methods were applied for estimating the influence strength. We train a controller with reinforcement learning for selecting a node list of given length as the initial seed set for the information spread. Our experiments show that deep Q-learning methods are suitable to analyze the competitive influence maximization on trust and distrust based social networks. Keywords: influence maximization, reinforcement learning, Q-learning.

Affective polarization and dynamics of information spread in online networks

Members of different political groups not only disagree about issues but also dislike and distrust each other. While social media can amplify this emotional divide—called affective polarization by political scientists—there is a lack of agreement on its strength and prevalence. We measure affective polarization on social media by quantifying the emotions and toxicity of reply interactions. We demonstrate that, as predicted by affective polarization, interactions between users with same ideology (in-group replies) tend to be positive, while interactions between opposite-ideology users (out-group replies) are characterized by negativity and toxicity. Second, we show that affective polarization generalizes beyond the in-group/out-group dichotomy and can be considered a structural property of social networks. Specifically, we show that emotions vary with network distance between users, with closer interactions eliciting positive emotions and more distant interactions leading to anger, disgust, and toxicity. Finally, we show that similar information exhibits different dynamics when spreading in emotionally polarized groups. These findings are consistent across diverse datasets spanning discussions on topics such as the COVID-19 pandemic and abortion in the US. Our research provides insights into the complex social dynamics of affective polarization in the digital age and its implications for political discourse.

Peranan Komunikasi Dalam Sistem Sosial : Analisis Teori Niklas Lukman

The theory of communication as autopoiesis proposed by Niklas Luhmann offers a revolutionary perspective in understanding communication in social systems. Luhmann argues that communication is not just a tool for exchanging information, but is an independent process and has autonomous mechanisms to maintain and reproduce itself. This theory combines the concept of autopoiesis which originally came from biology, where a system can produce and maintain its own components through internal interactions. In Luhmann's theory, communication is seen as a basic element of a social system that has the ability to create and reproduce social structures through processes communication itself. Luhmann emphasized that communication occurs through three main elements: information, message, and understanding. This process not only transmits information, but also shapes and constructs social reality. Communication in Luhmann's view is a selective process that involves selecting the information to be communicated, the way the information is conveyed, and how the information is understood by the recipient. Apart from that, the theory of communication as autopoiesis is relevant in the context of modern communication which is increasingly complex and fragmented. In the digital era, where information spreads rapidly and in large quantities, understanding communication as an autopoietic process helps us understand how information is processed, selected, and internalized in social systems. It also helps in understanding the challenges and opportunities that have arisen from developments in communication technologies, such as social media and the internet, which have changed the way we communicate and interact with each other. Overall, Niklas Luhmann's theory of communication as autopoiesis provides a powerful theoretical framework for understanding communication as an independent and dynamic process in social systems. This theory not only provides insight into how communication functions in a social context, but also offers analytical tools for exploring the complexity and dynamics of communication in modern society. By understanding communication through the lens of autopoiesis, we can better capture the essence of the communication process and how it shapes our social reality.

Research on Risk Prevention and Resolution Strategies of College Network Ideology in the Context of the Internet

With the rapid development of the Internet, the network ideology of colleges and universities is facing severe challenges. This paper deeply analyzes the root of the risk of network ideology and makes a specific investigation of the status quo of network public opinion in colleges and universities. On this basis, the study explores and puts forward a series of targeted risk prevention and resolution strategies, aiming at providing a systematic solution for the network ideology security of colleges and universities. In this paper, with the combination of theory and practice as the path, we verify the effectiveness and applicability of the proposed strategy through the analysis of the implementation effect of the strategy. This study also provides theoretical support and practical guidance for the prevention and control of ideological risks and public opinion guidance in universities under the network environment, which has important practical significance. With the continuous progress of network technology, the threats to the network ideology of colleges and universities are increasing. For example, the spread of false information has become a serious problem affecting the security of college network ideology.

Analysis of Information Security Under the Conditions of Hybrid War in Ukraine: Social Aspects

Abstract The development of the post-industrial society requires the acceleration of the integration of the national economy into the globalized economic space. This stage is characterized by active informatization of all spheres of life in society, which requires information security and cyber protection for high-quality information provision of the country’s population, intellectualization of economic processes and prevention of destructive informational influence on the social status of the individual. It is also necessary to consider that for Ukraine the specified stage is complicated by the hybrid war with Russia, which requires strengthening the protection of information from cyber-attacks and the formation of new approaches in preserving the quality of information on social networks. Therefore, the purpose of this article is the development of scientific and methodological approaches in information security management, strengthening its social significance. This requires solving a certain range of tasks of identifying and preventing socially dangerous information based on the use of economic and mathematical methods and models. The article highlights the main directions of socialization of modern technological development and theoretically substantiates its significant impact on human consciousness and behavior, puts a person in front of serious challenges and, under conditions of hybrid warfare, requires strengthening of information security in social networks. In the work, the components of information security are supplemented, its impact on social aspects of society’s life is highlighted. As a result of the research, the authors proposed a scientific and methodical approach to the construction of a system for countering the spread of socially dangerous information in social networks. Besides, its functional elements are highlighted. A method of combating the spread of harmful information in social networks has been developed, which solves the problem of information support of the decision-making process and includes providing the chosen and alternative options to the person making the decision, with the justification of the choice. This creates prerequisites for information support for making a well-founded management decision.

Impact of AI on Communication Relationship and Social Dynamics: A qualitative Approach

The integration of artificial intelligence (AI) into social media platforms has ushered in a new era of digital communication, offering unprecedented opportunities for content curation, relationship-building, and information exchange. Through this qualitative study, we have explored the multifaceted impact of AI-driven algorithms and natural language processing (NLP) technologies on user experiences and societal dynamics, addressing key research objectives and questions. Our findings underscore the transformative potential of AI in enhancing content curation and user interaction on social media platforms. Participants highlighted the benefits of personalized content recommendations and AI-enabled features such as chatbots, which streamline user interactions and provide instant support. Furthermore, AI algorithms play a crucial role in facilitating relationship-building through friend suggestions and group recommendations, fostering community engagement and social connections among users. However, alongside these benefits, our study also revealed significant challenges and ethical concerns associated with AI integration in social media. Participants expressed concerns about the proliferation of echo chambers and misinformation, fueled by algorithmic biases and the spread of false information through social bots. Privacy considerations emerged as a prominent issue, with participants emphasizing the need for transparency and accountability in AI implementation to safeguard user data and mitigate risks of algorithmic surveillance. In light of these findings, it is evident that the responsible deployment of AI technologies is paramount in ensuring positive user experiences and preserving the integrity of digital information ecosystems. Ethical considerations must guide the design and implementation of AI-driven algorithms, prioritizing transparency, fairness, and user empowerment. Platform operators, policymakers, and civil society stakeholders must collaborate to develop robust regulatory frameworks and governance mechanisms that uphold ethical standards and protect user rights in the digital age.

A study of collaborative governance of online users tourism information (Revision 2)

ABSTRACT With the rapid development of the Internet, the spread of tourism information is more rapid, and tourism information governance is increasingly important. This research adopts a system dynamics approach to construct a system dynamics model of collaborative tourism information governance from government, netizens, tourism enterprises, and the information itself, using the Huangshan information as a case study. The results show that the impact of ‘information popularity’ is the largest, ‘government credibility’ in the second place; ‘emergency management capability of tourism enterprises’, ‘the intensity of information disposal efforts’ and ‘role of opinion leaders’ have the weak influence to the tourism information governance. It is proposed to improve the public’s judgment, strengthen its judgment, improve the efficiency of the use of official media, promote the openness of information, and encourage the government to take the initiative and increase the level of interaction, strengthen the emergency management training of tourism enterprises, improve the ability to respond to the leading role of opinion leaders, correct guidance and other strategies.

Investigating Innovation Diffusion in Gender-Specific Medicine: Insights from Social Network Analysis

The field of healthcare is characterized by constant innovation, with gender-specific medicine emerging as a new subfield that addresses sex and gender disparities in clinical manifestations, outcomes, treatment, and prevention of disease. Despite its importance, the adoption of gender-specific medicine remains understudied, posing potential risks to patient outcomes due to a lack of awareness of the topic. Building on the Innovation Decision Process Theory, this study examines the spread of information about gender-specific medicine in online networks. The study applies social network analysis to a Twitter dataset reflecting online discussions about the topic to gain insights into its adoption by health professionals and patients online. Results show that the network has a community structure with limited information exchange between sub-communities and that mainly medical experts dominate the discussion. The findings suggest that the adoption of gender-specific medicine might be in its early stages, focused on knowledge exchange. Understanding the diffusion of gender-specific medicine among medical professionals and patients may facilitate its adoption and ultimately improve health outcomes.

Two-Stage Information Spreading Evolution on the Control Role of Announcements

Two-Stage Information Spreading Evolution on the Control Role of Announcements

Who is Going to Help? Detecting Social Media Influencers to Spread Information About Missing Persons

Who is Going to Help? Detecting Social Media Influencers to Spread Information About Missing Persons

Enhancement of random forest algorithm applied in fake news detection

The widespread increase of fake news presents a serious obstacle in today's information-sharing situation, making it more difficult to distinguish fact from fabricated information. The study aims to improve machine learning algorithms' capacity to identify fake news to address this significant issue. To improve detection accuracy, Researchers applied a method that combines the Random Forest algorithm with Adaptive Boosting, or AdaBoost. By utilizing ensemble learning techniques, our method successfully leverages the collective intelligence of numerous decision trees, outperforming traditional approaches. By carefully experimenting with hyperparameters using the Random Search, The proposed method improved the algorithm's capacity to identify significant patterns suggestive of false information. The integration of AdaBoost to Random Forest produces a significant improvement, from 92.56% to 99.79% accuracy average difference, with an accuracy gain of 7% over the baseline Random Forest model. This improvement emphasizes how effective ensemble methods are at negotiating the complexities in terms of fake news detection. This study adds to the ongoing efforts to protect the integrity of information in the digital era by showcasing the efficacy of sophisticated machine-learning approaches in addressing the complex problem of fake news. The study underscores the critical importance of continuous innovation and adaptability in combating the increase of misinformation. Through interdisciplinary collaboration and technological advancements, the aim is to fortify defenses against the detrimental spread of false information, fostering an information ecosystem that prioritizes truth and resilience.

Fake News Detection

The spread of false information on the internet has become a major social issue, casting doubt on the veracity of information shared on these platforms. This study uses cutting-edge methods from machine learning (ML) and natural language processing (NLP) to present a complete framework for the detection of fake news. The purpose of this paper is to develop a model for detecting bogus news. A model is selected by using supervised learning techniques. In addition, we categorize news stories as real or fraudulent using the Naïve Bayes, Logistic Regression, and Random Forest algorithms. Our methodology offers an approach to false news identification that is more robust by taking into account the credibility of the news sources in addition to the content of the news. Using labeled datasets of fictitious and authentic news stories, we train our algorithms. A few methodologies were compared to achieve varying degrees of accuracy. When compared to the other two models, Random Forest is thought to have produced the best results in terms of accuracy. We assess our framework's effectiveness using real-world news articles and benchmark datasets, showcasing its versatility in correctly recognizing false information in a variety of settings and domains. We demonstrate the advantages of our method in terms of detection accuracy, scalability, and computational efficiency by comprehensive experimentation and comparative analysis. All things considered, our suggested framework is a major step forward in the fight against the dissemination of false information on the internet and provides a workable way to lessen the negative effects of fake news on people, communities, and society at large.

Misinformation and Disinformation: Unravelling the Web of Deceptive Information

In the current digital era, misinformation and deception spread quickly, posing serious problems for civilizations everywhere. Online platforms and social media networks have become a haven for misinformation, which is defined as the unintended distribution of erroneous or inaccurate information, and disinformation, which is the deliberate spread of misleading information with the intent to deceive. This essay examines the subtle distinctions between misinformation and disinformation, highlighting their crucial roles in influencing public opinion and public debate globally. Examining the effects of misinformation and disinformation on people, groups, and democratic processes, the difficulties brought about by the quick dissemination of erroneous information and the decline in public confidence in traditional media and institutions are highlighted. Furthermore, an analysis is conducted on the legal and regulatory frameworks in India and throughout the world that deal with misinformation and disinformation. The analysis identifies important policies and laws that are meant to stop the spread of lies. The paper concludes with suggestions and solutions for dealing with misinformation and disinformation. It emphasises the value of fact-checking programmes, media literacy education, and cooperative efforts between governments, media outlets, and civil society to protect democratic values and advance information integrity in the digital age.