Spread Of Information Research Articles

Beyond network centrality: individual-level behavioral traits for predicting information superspreaders in social media.

Understanding the heterogeneous role of individuals in large-scale information spreading is essential to manage online behavior as well as its potential offline consequences. To this end, most existing studies from diverse research domains focus on the disproportionate role played by highly connected 'hub' individuals. However, we demonstrate here that information superspreaders in online social media are best understood and predicted by simultaneously considering two individual-level behavioral traits: influence and susceptibility. Specifically, we derive a nonlinear network-based algorithm to quantify individuals' influence and susceptibility from multiple spreading event data. By applying the algorithm to large-scale data from Twitter and Weibo, we demonstrate that individuals' estimated influence and susceptibility scores enable predictions of future superspreaders above and beyond network centrality, and reveal new insights into the network positions of the superspreaders.

The Influence of Facebook Social Media on The Spread of Hoax Information on Generation X in Setu Sari Village Cileungsi Subdistrict

This study aims to determine whether or not the influence of Facebook social media on the dissemination of hoax information to Generation X in one of the villages that the researchers chose as a sample. This research uses a quantitative approach and uses a quantitative descriptive method. This research was carried out in Setu Sari Village, Cileungsi District with 150 respondents as a sample. The data collection technique in this study was through observation, and distributing questionnaires by print out or via the Google Forms link. This study obtained the final results of the t-test, f-test, and simple regression test with a significance value of 0.790. Significance value 0.05, meaning that there is no influence of Facebook social media on the dissemination of hoax information to generation X in Setu Sari Village. The conclusion is that the majority of respondents actively use the Facebook platform as an intermediary for information and entertainment for respondents. This means that from the results of the respondents' answers, the majority of Facebook social media users with the X generation category have advanced in literacy, or it can be interpreted that the X generation in Setu Sari Village currently has good literacy values in receiving information read or from other people so that it can reduce the spread of information. hoaxes that are widely shared on the platform.Keywords: Social Media, Facebook.

Innovation and Networks in the Bioeconomy: A Case Study from the German Coffee Value Chain

The transition to a circular bioeconomy requires innovation across many sectors, but social dynamics within a sector’s network may affect innovation potential. We investigate how network dynamics relate to the perceptions and adoption of bioeconomy innovation using a case study from the food processing sector. Our case study of the German coffee value chain represents a technologically advanced sector with a strong sustainability focus and potential for residue valorization, which is an important dimension of a sustainable circular bioeconomy. We identify three distinct views (pioneers, traditional and limited users) related to residue valorization, map linkages between actors using social network analysis, and highlight barriers to innovation. We collected data through an online survey and semi-structured interviews with key actors in the coffee roasting sector. Within the social network analysis, we find that public waste managers are closely linked to the most influential actors, state actors such as the customs and tax offices can quickly interact with others in the network and promote the spread of information (highest closeness centrality) and specific roasters play an important role as intermediaries for efficient communication (highest betweenness centrality). Finally, we identify four main barriers including the structure of the coffee network, inconsistencies in federal waste regulations, economies of scale, and visions of sustainability. To support a sustainable bioeconomy, we recommend that policy makers revise the primary regulatory frameworks for waste (e.g., German Recycling Act) to clarify how to classify food residues, their disposal structures and broaden their use streams.

Increasing student engagement through digital branding in higher education marketing

As a result of the employment of social media, interpersonal contact and user involvement are made possible, which results in the modification of traditional hierarchical communication techniques and the introduction of an unexpected component into the decision-making process of customers. Even though it is considered to be a component of internet marketing, social media marketing is a crucial component for individuals and businesses that need to retain their competitiveness. On the other hand, the success of social media marketing is dependent on the presence of innovative items, the responsiveness of organizations to the market, and the construction of powerful online platforms. Twitter, Instagram, and Facebook are just some of the social networking sites that have evolved into indispensable tools for marketers. Since they enable interactive contact, these platforms are of tremendous value when it comes to the marketing of companies and services. Websites that facilitate social networking play an important part in the spread of information about educational institutions of higher learning. These websites also serve as effective means for recruiting prospective students. It has not yet been determined whether or not the content that is broadcast on these internet platforms has an effect on the cognitive processes that prospective students use when making choices about their education. The acknowledgment of user interaction on social media platforms is the most important non-financial benefit that organizations may get from their use of these platforms. When we have a thorough awareness of user interaction across a variety of platforms, we may have a better grasp of the influence that it has on how people perceive businesses. Instagram has become a significant medium for educational institutions of higher learning due to its visual-centric qualities, as well as its heightened engagement rates among a varied, youthful, and multinational community. This article places a strong emphasis on the relevance of brand equity within the context of the marketing strategy used by educational institutions of higher learning. When it comes to the idea of brand equity, there are several elements that may have an impact on it. Some of these characteristics include, but are not limited to, the personality of the brand, social credibility, and market position. It is quite necessary for educational institutions to make certain that they provide excellent services and facilities to their students. Nevertheless, the construction of a communication system that is both competent and effective is equally important for the purpose of boosting the positioning of the university's brand and effectively managing the value of its brand. The concept of "brand soul" serves as the basis for this, and it refers to the way the teachers and staff of the educational institution become the embodiment of the brand.

‘Are you vaccinated? Yeah, I’m immunized': a risk orders theory analysis of celebrity COVID-19 misinformation

ABSTRACT Background On 11 March 2020, COVID-19 was declared a global health pandemic by the World Health Organization (WHO). Vaccinating populations is paramount in changing the course of a pandemic. The rapid spread of (mis) and disinformation online from celebrities, politicians, and media influencers creates a corrosion of trust in public health interventions. Methods Given the importance of the spread of information during a public health crisis, the current study uses risk orders theory with a constructivist grounded theory approach to analyze an episode of a popular podcast available on YouTube, titled, ‘Aaron Rodgers Tells Pat McAfee His Side of Vaccine Situation.’ Results Findings illuminated three themes concerning COVID-19 medical interventions from celebrity discourse: (1) misinterpreting medical terminology; (2) conflating bodily autonomy and altruism; and (3) political ideology as an impetus for misinformation. Conclusions The discussion offers implications for healthcare practitioners in debunking mis- and disinformation. Foremost, lack of transparency concerning autonomy, liberty, freedom, and choice from public health experts who design messages during a public health crisis creates a space for non-medical influencers to promote pseudoscience, misinformation, and disinformation. This leads to public distrust of medical experts and confuses the public’s understanding of best practices based upon standard of medical evidence and care.

Suicide-Related Groups and School Shooting Fan Communities on Social Media: A Network Analysis

As network structure of virtual communities related to suicide and school shooting still remains unaddressed in scientific literature, we employed basic demographics analysis and social network analysis (SNA) to show common features, as well as distinct facets in the communities’ structure and their followers’ network. Open and publicly accessible data of over 16,000 user accounts were collected with a social media monitoring system. Results showed that adolescents and young adults were the main audience of suicide-related and school shooting fan communities. List of blocked virtual groups related to school shooting was more extensive than that of suicide, which indicates a high radicalization degree of school shooting virtual groups. The homogeneity of followers’ interests was more typical for subscribers of suicide-related communities. A social network analysis showed that followers of school shooting virtual groups were closely interconnected with their peers, and their network was monolithic, while followers of suicide-related virtual groups were fragmented into numerous communities, so presence of a giant connected component in their network can be questioned. We consider our results highly relevant for better understanding the network aspects of virtual information existence, harmful information spreading, and its potential impact on society.

Fake News Detection On Social Media:Review of Literature

In today's world, social media has become one of the most accessible sources of news for people worldwide due to its low cost, easy accessibility, and rapid dissemination. However, the harmful effects of fake news on individuals and society have been evident for a long time. Despite the challenges in detecting false information, numerous scholars are working to understand the issue and its characteristics. The purpose of this paper is to analyze contemporary frameworks that make use of different machine learning techniques in order to further our understanding of false news identification. The efficiency of these frameworks in recognizing and halting the spread of false information may be compared by examining various methods. A particular technique was used in the review process to provide a thorough and instructive assessment. Even though a lot of research has been done to identify false news, more can be done in the future. These studies have many limitations such as biased data, adaptability, credibility analysis and the news is only categorized as legitimate or unauthentic by their solutions. Nevertheless, a system of ratings or scores to assess the reliability of news is necessary for a workable approach.

Unscrambling Quantum Information with Clifford Decoders.

Quantum information scrambling is a unitary process that destroys local correlations and spreads information throughout the system, effectively hiding it in nonlocal degrees of freedom. In principle, unscrambling this information is possible with perfect knowledge of the unitary dynamics [B. Yoshida and A. Kitaev, arXiv:1710.03363.]. However, this Letter demonstrates that even without previous knowledge of the internal dynamics, information can be efficiently decoded from an unknown scrambler by monitoring the outgoing information of a local subsystem. We show that rapidly mixing but not fully chaotic scramblers can be decoded using Clifford decoders. The essential properties of a scrambling unitary can be efficiently recovered, even if the process is exponentially complex. Specifically, we establish that a unitary operator composed of t non-Clifford gates admits a Clifford decoder up to t≤n.

People with Disabilities, the Age of Information and Communication Technology and the Prevailing Digital Divide—A Descriptive Analysis

Technology provides a way forward to bringing improvements in every sphere of life. The issue of disabilities is common throughout the world and the practical implications of information and communication technology (ICT) help people with such disabilities to acquire basic physical and learning support. The purpose of this review is to identify the development of information and technologies for disabled people and to bring into account a broad-spectrum descriptive analysis of the prevailing technologies from a socio-economic perspective. This paper also covers the various shades of ICT practices for disabilities, including future direction related to such practices. The methodology adopted for this purpose was the gathering of the latest and updated data from the scientific platforms to bring to the readers only the relevant and up-to-date information. The results generated illuminate the potential benefits of digital technologies for disabled persons. The article discusses the way these technologies overcome the barriers among digital beneficiaries that may suffer from certain disabilities. It also illustrates how technologies are being improved from time to time as per emerging needs. Moreover, the paper is also a directive for the increasing need for inclusive information and technology spread beyond the barrier of disabilities to truly bring about the digital revolution of the world.

Dynamic identification of important nodes in complex networks by considering local and global characteristics

Abstract By combining centrality measures and community detection, a better insight into the nature of the evolution of important nodes in complex networks is obtained. Meanwhile, the dynamic identification of important nodes in complex networks can be enhanced by considering both local and global characteristics. Local characteristics focus on the immediate connections and interactions of a node within its neighbourhood, while global characteristics take into account the overall structure and dynamics of the entire network. Nodes with high local centrality in dynamic networks may play crucial roles in local information spreading or influence. On the global level, community detection algorithms have a significant impact on the overall network structure and connectivity between important nodes. Hence, integrating both local and global characteristics offers a more comprehensive understanding of how nodes dynamically contribute to the functioning of complex networks. For more comprehensive analysis of complex networks, this article identifies important nodes by considering local and global characteristics (INLGC). For local characteristic, INLGC develops a centrality measure based on network constraint coefficient, which can provide a better understanding of the relationship between neighbouring nodes. For global characteristic, INLGC develops a community detection method to improve the resolution of ranking important nodes. Extensive experiments have been conducted on several real-world datasets and various performance metrics have been evaluated based on the susceptible–infected–recovered model. The simulation results show that INLGC provides more competitive advantages in precision and resolution.

Factors Affecting the Formation of False Health Information and the Role of Social Media Literacy in Reducing Its Effects

The COVID-19 pandemic heightened concerns about health and safety, leading people to seek information to protect themselves from infection. Even before the pandemic, false health information was spreading on social media. We conducted a review of recent literature in health and social sciences and proposed a theoretical model to understand the factors influencing the spread of false health information. Our focus was on how false health information circulated before and during the pandemic, impacting people’s perceptions of believing information on social media. We identified four possible strategies to counteract the negative effects of false health information: prebunking, refuting, legislation, and media literacy. We argue that improving people’s social media literacy skills is among the most effective ways to address this issue. Our findings provide a basis for future research and the development of policies to minimize the impact of false health information on society.

The Educational Affordances and Challenges of ChatGPT: State of the Field

ChatGPT was released to the public in November 30, 2022. This study examines how ChatGPT can be used by educators and students to promote learning and what are the challenges and limitations. This study is unique in providing one of the first systematic reviews using peer review studies to provide an early examination of the field. Using PRISMA principles, 44 articles were selected for review. Grounded coding was then used to reveal trends in the data. The findings show that educators can use ChatGPT for teaching support, task automation, and professional development. These were further delineated further by axial sub codes. Eight student uses were 24/7 support, explain difficult concepts, conversational partner, personalized feedback and materials, provide writing support, offer self-assessment, facilitate engagement, and self-determination. In addition to be affordances of the AI, the data from the articles also showed limitations to ChatGPT and misuses, specifically, inaccuracies and hallucinations, potential bias, and tool limitations. Misuses are plagiarism and cheating, privacy issues and spread of false information. This study is a springboard for researchers, practitioners, policy makers and funders in understanding the emerging state of the field of ChatGPT.

An explainable deepfake of speech detection method with spectrograms and waveforms

Research on deepfake techniques for speech is crucial for combatting the spread of fake information, safeguarding public privacy, and advancing forensic techniques. However, the lack of transparency and explainability of spoofed speech detection models raises concerns about their reliability. In this paper, we suggest using raw waveform signals and spectrograms as fused features of the spoofed speech detection model. We use the SHAP method to analyze the feature distribution of spoofed speech detection and explain the likelihood of fake speech. Our experimental results demonstrate that our approach achieves better classification results with lighter model parameters than other feature fusion methods. Finally, the feature contribution values are calculated under the SHAP method to visualize them as heat maps. It helps researchers to analyze the feature distribution of spoofed speech to identify the most critical features that distinguish between spoofed and bona fide and to ensure transparency in their use.

A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks.

In the realm of online social networks, the spreading of information is influenced by a complex interplay of factors. To explore the dynamics of one-time retweet information spreading, we propose a Susceptible-Infected-Completed (SIC) multi-information spreading model. This model captures how multiple pieces of information interact in online social networks by introducing inhibiting and enhancement factors. The SIC model considers the completed state, where nodes cease to spread a particular piece of information after transmitting it. It also takes into account the impact of past and present information received from neighboring nodes, dynamically calculating the probability of nodes spreading each piece of information at any given moment. To analyze the dynamics of multiple information pieces in various scenarios, such as mutual enhancement, partial competition, complete competition, and coexistence of competition and enhancement, we conduct experiments on BA scale-free networks and the Twitter network. Our findings reveal that competing information decreases the likelihood of its spread while cooperating information amplifies the spreading of mutually beneficial content. Furthermore, the strength of the enhancement factor between different information pieces determines their spread when competition and cooperation coexist. These insights offer a fresh perspective for understanding the patterns of information propagation in multiple contexts.

Culture and regional development: information and communication technologies from a territorial perspective

This theoretical essay aims to analyze the theoretical aspects of culture and information and communication technologies from the perspective of territoriality. To this end, the methodological path followed has a theoretical wedge based on reference authors on the subject. The first part examines the bibliography on regional development and territoriality, highlighting economic and social aspects. It then explores the main concepts and definitions of culture and development. The last part analyzes the conjuncture of information and communication technologies, culture, and territorial development, narrowing down the theoretical gaps that underpin the theme. At the end of the paper, the theoretical framework shows that territorial development stems, among other things, from cultural actions, since there is the economic contribution of the arts, heritage, and especially the cultural and creative industries, added to cultural rights, with a focus on inclusion, access, empowerment, and integral development for the well-being of citizens. Finally, it should be noted that the spread of information and computer technologies is complementing and impacting the composition and operations of the cultural sector.

Detection of fake news from social media using support vector machine learning algorithms

Never happened before in human history the spreading of fake news; now, the development of the Worldwide Web and the adoption of social media have given a pathway for people to spread misinformation to the world. Everyone is using the Internet, creating and sharing content on social media, but not all the information is valid, and no one is verifying the originality of the content. Identifying the content's essence is sometimes complicated for researchers and intelligent researchers. For example, during Covid-19, misinformation spread worldwide about the outbreak, and much false information spread faster than the virus. This misinformation will create a problem for the public and mislead people into taking the proper medicine. This work will help us to improve the prediction rate. Here we investigate the ability of machine learning classifiers and deep learning models: Naive Bayes, Logistic Regression, Support Vector Machine, Decision Tree, Random Forest and K-Nearest Neighbor. Deep learning models include Convolutional Neural Networks and Long Short-Term Memory (LSTM). The various types of machine learning and deep learning models will be trained and tested using the Covid-19 dataset (1,375,592 tweets).

Online Spreading of Topic Tags and Social Behavior

This article explores information spreading in modern online social networks. According to the law of information spread in real social networks, information retweeting is divided into two types: topic retweeting and relationship retweeting. Social behavior is considered as a higher order interaction, and the nodal influence effect in the traditional approach is abstracted as part of it for analysis. The process of topic communities being subjected to social behavior is simulated by the social behavior model, and the dynamic retweeting rate is established. A network evolution model is constructed based on the centrality and noncontinuity characteristics of topic communities in the spread process. The social reinforcement effect in information spreading is described in two dimensions by defining topic expansion rate and topic diffusion rate. This work conducts multiple views of analysis and visualization, which provide more results of quantitative aspect. The validity of the model is verified by comparing the model simulation results with real cases and the generalization ability experiments.

The effect of technology on Arabic language learning in higher education

Technology is advancing and developing, and we cannot let go of our lives. One of them is in the world of education, the use of technology is often found. teachers combine technological devices in the learning process so that the learning atmosphere becomes more interesting, not only with increasingly open technological developments and the spread of information and knowledge throughout the world across the boundaries of space and time. Thus, technological developments have a positive impact on educators and students. In this study, researchers measured the extent to which the application of this technology in the learning process was highly valued. For this reason, this research aims to find out the impact of technology on Arabic language learning at the Batusangkar Institute State Islamic University. The method used in this research is a survey research method. Data collection uses observation, interviews and questionnaires. Furthermore, the expected result of this research is to find out the role or influence of technology on Arabic language learning activities in higher education. Based on the research results, it can be seen that there is an influence or influence of technology on Arabic language learning at the Batusangkar State Islamic Institute.

MNCs' corporate social irresponsibility and foreign subsidiary performance

AbstractResearch SummaryBuilding on the cognitive view of stakeholder evaluation, we propose that multinational corporations' (MNCs') socially irresponsible acts transcend geographic boundaries and negatively affect foreign subsidiary performance. Moreover, we propose that foreign subsidiaries' product innovation and marketing campaigns create strategic noise in the information space that can mitigate the negative effect of MNCs' corporate social irresponsibility (CSI) incidents occurring elsewhere on the performance of their foreign subsidiaries. We test our arguments on 335 subsidiaries of 42 multinational grocery retailers from 18 different home countries. Our analyses, based on a sample of 2185 subsidiary‐year observations over the period of 9 years (2012–2020), largely support our core argument that CSI incidents negatively influence the sales growth of foreign subsidiaries.Managerial SummaryThis research underscores the importance for MNC managers to be cognizant of the potential fallout from CSI incidents. With the global spread of information, MNCs' misconduct that occurs elsewhere can quickly impact the sales growth of foreign subsidiaries. The study found that product or service innovations are more effective than marketing campaigns in managing reputational damage, emphasizing the value of genuine, capability‐enhancing strategies. Furthermore, the findings highlight the interconnectedness between socially responsible practices and innovation, suggesting that MNCs and their subsidiaries should focus on maintaining strong ethical standards while simultaneously fostering an environment conducive to innovation. This approach not only addresses the adverse effects of CSI but also strengthens the MNC's overall standing with its stakeholders.

Uncovering COVID-19 conversations: Twitter insights and trends

In this paper, we delve into the public discourse surrounding COVID-19 on Twitter to unearth the collective sentiments, concerns, and spread of information during the pandemic. By leveraging a dataset of relevant tweets and corresponding ISO country codes, our analysis will map out the geographical and digital landscape of these conversations. The significance of this work lies in its potential to inform public health strategies, shape policymaking, and contribute to social research on crisis communication. Stakeholders ranging from health officials to the public have a vested interest in understanding the contours of this dialogue. Our objective is to craft a data-driven narrative through visualizations that reveal how the world engages with the pandemic on the digital front, providing actionable insights into global and local responses to COVID-19 using Machine Learning techniques.