Networked Information Sources Research Articles

Mapping the Mpox discourse: A network and sentiment analysis

Mpox, a zoonotic disease re-emerging from animals to humans, poses a risk of evolving into a global pandemic due to its high infectivity and potential asymptomatic transmission. This study maps the structure and configuration of mpox-related discussions on Twitter/X, identifies key influencers and top hashtags, and analyzes public sentiment. Data were collected using NodeXL Pro from May 7, 2022, to January 15, 2023, with the keyword “Monkeypox” and visualized using Gephi. Social network analysis ranked nodes by betweenness centrality scores to highlight key communicators, and the YifanHu layout algorithm visualized the network. Influential users, source topics, and hashtags were identified, and sentiment analysis was conducted using Azure Machine Learning tools. The analysis identified 11,397 mpox-related tweets. The network structure resembled a community with diverse participants. Influential users included health and science journalists, writers, academics, medical doctors, and public figures. News media and organizational websites were the top information sources, emphasizing the need for accessible scientific information. “Monkeypox” and “Mpox” were the most prevalent hashtags. Negative sentiments dominated the discussion. This analysis provides insights into network structure, key influencers, information sources, and public sentiment, aiding tailored health initiatives to address misinformation and advocate valid health information and emergency responses.

Centrality of Information Sources in Agricultural Input Networks: Insights from Social Network Analysis

Input dealers being credible localite sources of information for farmers have huge potential of aiding faster diffusion of innovations. Under this context, understanding how the input dealers’ access and exchange information is essential for enriching them with updated information and improving the agricultural value chain. This study employs Social Network Analysis (SNA) to explore the centrality measures—degree centrality, closeness centrality, and betweenness centrality—of various information sources utilized by input dealers in Nagar Kurnool and Wanaparthy districts of Telangana during 2020. The analysis reveals that traditional sources, such as extension agents and other input dealers, are highly central to the network, with both scoring the highest in degree and closeness centrality (0.733 and 0.698, respectively). These sources are not only frequently accessed but also serve as key bridges within the network, facilitating the flow of information. Digital platforms like smartphones show growing importance, with significant degree centrality (0.667), yet they play a less critical bridging role. In contrast, social media and print media exhibit lower centrality scores, indicating their peripheral influence. The findings suggest that while traditional information sources remain dominant, digital platforms are emerging as valuable tools for information dissemination highlighting the need for strategies that strengthen traditional channels while integrating digital tools to enhance the efficiency and reach of agricultural information networks. This research contributes to a deeper understanding of the complex information ecosystem in agriculture, offering insights for policymakers and stakeholders aiming to optimize information flows within the sector.

Are vulnerable farmers more easily influenced? Heterogeneous effects of Internet use on the adoption of integrated pest management

The Internet is believed to bring more technological dividends to vulnerable farmers during the green agriculture transformation. However, this is different from the theory of skill-biased technological change, which emphasizes that individuals with higher levels of human capital and more technological endowments benefit more. This study investigates the effects of Internet use on farmers’ adoption of integrated pest management (IPM), theoretically and empirically, based on a dataset containing 1 015 farmers in China's Shandong Province. By exploring the perspective of rational inattention, the reasons for the heterogeneity of the effects across farmers with different endowments, i.e., education and land size, are analyzed. The potential endogeneity issues are addressed using the endogenous switching probit model. The results reveal that: (1) although Internet use significantly positively affects farmers’ adoption of IPM, vulnerable farmers do not benefit more from it. Considerable selection bias leads to an overestimation of technological dividends for vulnerable farmers; (2) different sources of technology information lead to the difference in the degree of farmers’ rational inattention toward Internet information, which plays a crucial role in the heterogeneous effect of Internet use; and (3) excessive dependence on strong-tie social network information sources entraps vulnerable farmers in information cocoons, hindering their ability to reap the benefits of Internet use fully. Therefore, it is essential to promote services geared towards elderly-oriented Internet agricultural technology information and encourage farmers with strong Internet utilization skills to share technology information with other farmers actively.

Rumor source localization in social networks based on infection potential energy

The location technology of information sources in social networks is a major factor in exploring the means of information propagation. In this context, most existing methods ignore the direction of infected nodes and fail to make full use of the diffusion information, resulting in poor identification of source localization. To address such problems, a novel source location method based on infection potential energy is proposed. Its methodology consists of several steps: Firstly, a network reconstruction method is suggested, based on infection potential energy, to make the constructed information diffusion path more accurate. Afterwards, a network pruning method is identified to reduce the search space of candidate sources. Finally, considering the minor differences in the Gaussian density values of multiple nodes and the tendency of the rumor to spread, a distance centrality method is proposed. Experiments in real networks show that the suggested method achieves a better location performance.

Tools, Tests, and Checklists: The Evolution and Future of Source Evaluation Frameworks

In this article, we connect the scholarly conversations around established and emerging source evaluation frameworks such as the ABCs of website evaluation, CRAAP Test, DIG Method, RADAR approach, SIFT Method, the Proactive Evaluation approach proposed by the Project Information Literacy Team. We then explain each evaluation framework, delineating which framework is appropriate for a particular format, such as scholarly sources, online sources, image-based sources, and/or networked information sources. Lastly, we showcase the ways we have incorporated these source evaluation frameworks into our own classroom instruction and provide readers with ideas on how to do the same within their own institutional contexts.

Ginger Product Price Information Based on Semantic Heterogeneity of Multisource Network Information

In order to improve prices of ginger products information service, promote the transmission and interaction between prices of ginger products information data, agricultural products are roughly divided into nine categories. Through network research, the price information of agricultural products on the Internet is analyzed. According to the hierarchical theory of information processing, three types of semantic heterogeneity of schema, contextual data, and individual anomaly data with hierarchical progressive relationship are proposed. The results show that almost 100% of the main attributes exist in the network information sources, in addition, more than 80% of the websites have type differentiation of agricultural products, and less is provided for attributes such as specification and origin, which also have an impact on the price of agricultural products. Through the network information source, the attribute characteristics of multisource network prices of ginger products information are grasped, and the classification characteristics of existing semantic heterogeneity are combined, and it provides a strong support for constructing the classification system of semantic heterogeneity of prices of ginger products information.

Development of a COVID-19 Web Information Transmission Structure Based on a Quadruple Helix Model: Webometric Network Approach Using Bing.

BackgroundDeveloping an understanding of the social structure and phenomenon of pandemic information sources worldwide is immensely significant.ObjectiveBased on the quadruple helix model, the aim of this study was to construct and analyze the structure and content of the internet information sources regarding the COVID-19 pandemic, considering time and space. The broader goal was to determine the status and limitations of web information transmission and online communication structure during public health emergencies.MethodsBy sorting the second top-level domain, we divided the structure of network information sources into four levels: government, educational organizations, companies, and nonprofit organizations. We analyzed the structure of information sources and the evolution of information content at each stage using quadruple helix and network analysis methods.ResultsThe results of the structural analysis indicated that the online sources of information in Asia were more diverse than those in other regions in February 2020. As the pandemic spread in April, the information sources in non-Asian regions began to diversify, and the information source structure diversified further in July. With the spread of the pandemic, for an increasing number of countries, not only the government authorities of high concern but also commercial and educational organizations began to produce and provide significant amounts of information and advice. Nonprofit organizations also produced information, but to a lesser extent. The impact of the virus spread from the initial public level of the government to many levels within society. After April, the government’s role in the COVID-19 network information was central. The results of the content analysis showed that there was an increased focus on discussion regarding public health–related campaign materials at all stages. The information content changed with the changing stages. In the early stages, the basic situation regarding the virus and its impact on health attracted most of the attention. Later, the content was more focused on prevention. The business and policy environment also changed from the beginning of the pandemic, and the social changes caused by the pandemic became a popular discussion topic.ConclusionsFor public health emergencies, some online and offline information sources may not be sufficient. Diversified institutions must pay attention to public health emergencies and actively respond to multihelical information sources. In terms of published messages, the educational sector plays an important role in public health events. However, educational institutions release less information than governments and businesses. This study proposes that the quadruple helix not only has research significance in the field of scientific cooperation but could also be used to perform effective research regarding web information during crises. This is significant for further development of the quadruple helix model in the medical internet research area.

Impact of Confirmation Bias on Competitive Information Spread in Social Networks

This article investigates the impact of confirmation bias on competitive information spread in the social network that comprises individuals in a social network and competitive information sources at a cyber layer. We formulate the problem of information spread as a zero-sum game, which admits a unique Nash equilibrium in pure strategies. We characterize the dependence of pure Nash equilibrium on the public's innate opinions, the social network topology, as well as the parameters of confirmation bias. We uncover that confirmation bias moves the equilibrium toward the center only when the innate opinions are not neutral, and this move does not occur for the competitive information sources simultaneously. Numerical examples in the context of well-known Krackhardt's advice network are provided to demonstrate the correctness of theoretical results.

Information Sources Estimation in Time-Varying Networks

Identifying information sources plays a significant role in network science and engineering. However, existing source identification approaches generally focus on static networks without considering the temporal features of networks. To this end, we comprehensively study the problem of identifying single and multiple information sources in time-varying networks. Specifically, we first represent the time-varying networks by time aggregated graph (TAG), and employ a microcosmic susceptible-infected-recovered (SIR) model to characterize the diffusion dynamics of each node. Second, in the case of single-source, we exploit a TAG-based reverse infection (RI-TAG) algorithm to specify a set of suspect nodes, which not only reduces the scope of seeking the source but also ensures the feasibility of path calculation. Then, a novel computationally efficient algorithm is proposed to estimate the information source and diffusion time simultaneously. Subsequently, in the case of multi-source, we design a multi-source estimation algorithm, which divides the set of infected nodes into various partitions, and then runs our single-source estimation algorithm in each partition. Moreover, we present an effective algorithm to estimate the number of sources. Finally, experimental results on various synthetic and empirical time-varying networks demonstrate the effectiveness of the proposed algorithms.

Locating multiple information sources in social networks based on the naming game

Identifying the source of information in a network plays a key role in controlling the impact of information. Herein, we study the problem of multiple source localization in the context of information propagation in social networks. We use the theory of the naming game to conduct observations. Moreover, we divide the observations into different sets based on the information provided by them and then estimate the source of each set. Finally, we combine the source of each observation set to obtain all the estimated information sources. The proposed method can locate sources without knowing the number of information sources. Simulations on four real data sets are provided to verify the performance of our method.

A Hybrid Intrusion Detection System for IoT Applications with Constrained Resources

Network security and network forensics technologies for the Internet of Things (IoT) need special consideration due to resource-constraints. Cybercrimes conducted in IoT focus on network information and energy sources. Graph theory is adopted to analyze the IoT network and a hybrid Intrusion Detection System (IDS) is proposed. The hybrid IDS consists of Centralized and Active Malicious Node Detection (CAMD) and Distributed and Passive EEA (Energy Exhaustion Attack) Resistance (DPER). CAMD is integrated in the genetic algorithm-based data gathering scheme. CAMD detects malicious nodes manipulated by cyber criminals and provides digital evidence for forensics. DPER is implemented in a set of communication protocols to alleviate the impact of EEA attacks. Simulation experiments conducted on NS-3 platform showed the hybrid IDS proposed detected and traced malicious nodes precisely without compromising energy efficiency. Besides, the impact of EEA attacks conducted by cyber criminals was effectively alleviated.

The Formation of Students’ Self-Organization Skills in a Technical University

The main components of self-organization and its implementation stages are identified against the background of determining the importance of self-organization skills mastery by technical university graduates for the successful implementation of educational and future professional activities. Based on the social demands generated by the need to form general cultural and professional competencies in the learning process, the necessity to organize students’ independent work in such a way that it motivates self-organization and, as a result, self-education is confirmed. It is proved that the transformation of students’ independent work should be carried out regularly, taking into account the changes taking place in society, using modern information technologies as a kind of organizational resource. In order to reduce time expenditures in the process of updating the educational material for a particular discipline, it is proposed to establish the relationship between the components of the educational process by identifying intersubject communications, which are considered as a means of achieving the applied orientation of the discipline, at the stage of selecting the necessary information. The formation of students’ self-organization skills has been carried out on the basis of data from a frequency analysis of the network information sources. The results of frequency analysis and statistical processing of experimental data show the ability to make a forecast about the weight of specific topics for future professional activities, which will allow to distribute study time correctly. The use of established intersubject communications of disciplines and the fulfillment of the selected conditions contribute to the development of students’ educational activities self-organization.

Connected, digitalized welding production—Industrie 4.0 in gas metal arc welding

The digitalization and thus the presence of far-reaching, networked information technology have led to fundamental and accelerated changes in society and technology in recent decades. Industrial production technology cannot be viewed isolated in this context and is thus confronted with new expectations on the one hand but also with great potential on the other. Initiatives such as “Industrie 4.0” from Germany, “Made in China 2015” from China, or “Industrial Internet Consortium” from the USA address the need for deliberate promotion of digitalized production systems but are often still at the conceptual stage. This paper describes the basic framework and core elements of Industrie 4.0 and places them in the context of gas metal arc welding (GMAW). Based on this, concepts for autonomous welding production and for utilizing networked information sources for the improvement of product quality are described. The described concepts show the potential added value of networked production systems for gas metal arc welding in terms of process and product optimization and the necessary methodological requirements.

A neural computing approach to the construction of information credibility assessments for online social networks

Information credibility assessment has traditionally been understood in the online information processing context. This study extends this literature by exploring information credibility assessment mechanisms in the online social network context. In doing so, it responds to the emerging call for an understanding of the online social network context under the operational mechanism of information credibility assessment. Specifically, this study proposes a neural computing approach based on the stimulate–organism–response framework to determine the process, strategy and clue of online social network information assessment. Drawing on the theory of the Technology Acceptance Model and the theory of reasoned action, and based on a survey of 399 returned online questionnaires, we find that the involvement of online social network information, information sources and information transfer channels has a significant positive correlation with information usefulness and credibility, while information scenarios have no significant impact on usefulness and credibility, and the credibility of information is significant to the user’s information adoption behavior. Different from the predictions made in the literature, the information scenario does not affect information credibility and information usefulness. Conclusions and academic and practical implications are discussed.

The Analysis of Influencing Factors of Information Dissemination on Cascade Size Distribution in Social Networks

The cognitive behavior of online user groups on information promotes the dissemination of information. The trajectories of information dissemination in social networks can be described by treelike cascades, and the distribution of the sizes of these cascades can capture the distribution of popularity of a social network. Numerous studies have shown that the cascade size distribution follows fat-tail distributions, including power-law distribution and bimodal distribution; however, the underlying characteristic of this highly skewed distribution lacks quantitative experimental analysis. Based on the stochastic epidemic-like information dissemination model, namely, the susceptible view forward removed model, this paper explores the impact of the content attractiveness and influence, and information source on the cascade size distribution through computational experiments. On the one hand, we find that when the mean value of the information influence and attractiveness is small, the cascade sizes follow a power-law distribution, and the larger the variance, the heavier the tail. On the other hand, the more random the distribution of information sources in social networks, the smaller the slope of the power-law cascade size distribution. Our findings quantitatively reveal the causality of power-law cascade size distribution from computational experiments, clarify the role of information attractiveness, influence, and sources on the distribution of popularity in social networks.

Detecting Multiple Information Sources in Networks under the SIR Model

In this paper, we study the problem of detecting multiple information sources in networks under the Susceptible-Infected-Recovered (SIR) model. First, assuming the number of information sources is known, we develop a sample-path-based algorithm, named clustering and localization, for trees. For $g$ -regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. We further present a heuristic algorithm for general networks and an algorithm for estimating the number of sources when the number of real sources is unknown.

Network convergence: theory, architectures, and applications

With ever increasing demand for highly correlated video services in wireless networks, network information sources often carry vast redundancy in information contents, as exemplified by popular multimedia contents requested online by a large number of mobile users. The consequence is the waste of two precious resources - spectrum and energy. A good strategy is network convergence whereby commonly requested contents are transmitted via broadcast channels while personal contents are unicast by cellular networks. This article studies crucial issues arising from network convergence including its information-theoretic foundation, architectures, and feasible schemes for various application scenarios.

Big Data and Predictive Reasonable Suspicion

The Fourth Amendment requires “reasonable suspicion” to seize a suspect. As a general matter, the suspicion derives from information a police officer observes or knows. It is individualized to a particular person at a particular place. Most reasonable suspicion cases involve police confronting unknown suspects engaged in observable suspicious activities. Essentially, the reasonable suspicion doctrine is based on “small data” – discrete facts involving limited information and little knowledge about the suspect. But what if this small data is replaced by “big data”? What if police can “know” about the suspect through new networked information sources? Or, what if predictive analytics can forecast who will be the likely troublemakers in a community? The rise of big data technology offers a challenge to the traditional paradigm of Fourth Amendment law. Now, with little effort, most unknown suspects can be “known,” as a web of information can identify and provide extensive personal data about a suspect independent of the officer’s observations. New data sources including law enforcement databases, third party information sources (phone records, rental records, GPS data, video surveillance data, etc.), and predictive analytics, combined with biometric or facial recognition software, means that information about that suspect can be known in a few data searches. At some point, the data (independent of the observation) may become sufficiently individualized and predictive to justify the seizure of a suspect. The question this article poses is can a Fourth Amendment stop be predicated on the aggregation of specific, individualized, but otherwise non-criminal factors?This article traces the consequences in the shift from a “small data” reasonable suspicion doctrine, focused on specific, observable actions of unknown suspects, to the “big data” reality of an interconnected information rich world of known suspects. With more targeted information, police officers on the streets will have a stronger predictive sense about the likelihood that they are observing criminal activity. This evolution, however, only hints at the promise of big data policing. The next phase will be using existing predictive analytics to target suspects without any actual observation of criminal activity, merely relying on the accumulation of various data points. Unknown suspects will become known, not because of who they are but because of the data they left behind. Using pattern matching techniques through networked databases, individuals will be targeted out of the vast flow of informational data. This new reality subverts reasonable suspicion from being a source of protection against unreasonable stops, to a means of justifying those same stops.

From Personal Attitudes to Public Opinion

A model of a social network–based attitude diffusion system in the context of activity and travel choice behavior is presented. The principal mechanisms contributing to attitude formation were identified, and mathematical models to capture these processes were developed. The primary contributions of this research are (a) the modeling of attitude diffusion according to social and learning mechanisms and (b) the evolution of these attitudes over time in a lattice neighborhood social network. The agent-based framework presented is sufficiently general and flexible to allow the building of a more complete representation of information diffusion and attitude formation within activity and travel behavior choice dimensions, for example, mode choice or departure time choice. The framework allows the extension of the presented approach with additional social network structures, information sources, and social interaction mechanisms in the physical and virtual realms or the extension and modification of the presented approach to simulate the impact of information-based management strategies.

Topic Detection and Tracking Method for Tibetan Network

Internet has become an important tool to gain information; how to effectively detect hot topics from a lot of network information sources, e.g., Tibetan network, seems to be an urgent issue. Traditional hot topic is mainly based on the number of comments to get and the topic contents are usually not considered. In this letter, we study the approach of content-based relevance, i.e., based on user browsing behavior and the topic attention degree to discover the hot topic. And use the complex network theory to analyze the tracking model.