Journal articles on the topic 'BG. Information dissemination and diffusion'
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Shukla, Dhirendra Mani, and M. Akbar. "Diffusion of internationalization in business group networks: evidence from India." Management Decision 56, no. 2 (February 12, 2018): 406–20. http://dx.doi.org/10.1108/md-10-2016-0741.
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Purpose The purpose of this paper is to suggest that business group (BG) networks act as conduits for diffusion of information and resources pertaining to internationalization. It considers three types of BG networks arising from three different types of ties- director interlock, direct equity, and indirect equity. In particular, it examines the effects of cohesiveness of these BG networks on the diffusion of internationalization within a BG. Design/methodology/approach Drawing on social network perspective, it is hypothesized that, for each type of network, cohesiveness enhances within-BG similarity of the extent of internationalization. An empirical investigation is conducted on a sample of 55 Indian BGs for the period 2009-2013. Findings Results support all the three hypotheses, suggesting that higher level of cohesiveness leads to higher level of within-BG similarity of the extent of internationalization, for all three network types. Originality/value Findings of this study contribute to the BG literature by examining the effects of BG network cohesiveness on the diffusion of internationalization within a BG, for three types of BG networks.
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McEwan, Bree, Christopher J. Carpenter, and Jill E. Hopke. "Mediated Skewed Diffusion of Issues Information: A Theory." Social Media + Society 4, no. 3 (July 2018): 205630511880031. http://dx.doi.org/10.1177/2056305118800319.
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The modern media ecology has changed drastically over the last decade yet scholarly theoretical perspectives lag behind lay theories regarding news diffusion making it difficult to fully articulate and understand the processes driving dissemination of information and persuasion across networks and media contexts. The proposed theoretical framework takes into account extant research on the multiple mechanisms, specifically, cognitive ego involvement, the media environment, and interpersonal processes that operate in concert to influence the way information about societal issues is diffused through digital communication channels. The theoretical framework of mediated skewed diffusion of issues information provides 11 testable propositions. These are put forth to provide a foundation and encourage future research on information dissemination, online persuasion, and position polarization.
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Cheng, Shin-Ming, Vasileios Karyotis, Pin-Yu Chen, Kwang-Cheng Chen, and Symeon Papavassiliou. "Diffusion Models for Information Dissemination Dynamics in Wireless Complex Communication Networks." Journal of Complex Systems 2013 (September 4, 2013): 1–13. http://dx.doi.org/10.1155/2013/972352.
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Information dissemination has become one of the most important services of communication networks. Modeling the diffusion of information through such networks is crucial for our modern information societies. In this work, novel models, segregating between useful and malicious types of information, are introduced, in order to better study Information Dissemination Dynamics (IDD) in wireless complex communication networks, and eventually allow taking into account special network features in IDD. According to the proposed models, and inspired from epidemiology, we investigate the IDD in various complex network types through the use of the Susceptible-Infected (SI) paradigm for useful information dissemination and the Susceptible-Infected-Susceptible (SIS) paradigm for malicious information spreading. We provide analysis and simulation results for both types of diffused information, in order to identify performance and robustness potentials for each dissemination process with respect to the characteristics of the underlying complex networking infrastructures. We demonstrate that the proposed approach can generically characterize IDD in wireless complex networks and reveal salient features of dissemination dynamics in each network type, which could eventually aid in the design of more advanced, robust, and efficient networks and services.
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Guo, Rongchun. "News Hotspot Event Diffusion Mechanism Based on Complex Network." Mathematical Problems in Engineering 2022 (May 28, 2022): 1–9. http://dx.doi.org/10.1155/2022/1455324.
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The wide range of social hot news events on the Internet has made the Internet have a great impact on the public. However, there are few studies on Internet information. In order to improve the efficiency of user network information dissemination of Internet information based on complex network theory and model simulation, this paper makes a more in-depth study on information dissemination on the Internet, constructs a complex network of Internet information dissemination, and analyzes the static topology and dynamic evolution process of the network. Using the attention relationship between Internet users, the Internet information dissemination network, degree, and path were used to select multiple indicators. The static topology of the network is analyzed by using the complex network theory. The study found that the complex network of Internet information is different from other complex networks. The influencing factors of network dynamic evolution are studied from three aspects: overall structure, local structure, and time constraints. The evolution trend of different forms and overall network nodes in the evolution process was explored, and the network dynamic evolution process model was constructed. Practice shows that the model can better describe the evolution process of network information dissemination in complex networks. The degree values of the two networks are positively correlated with the corresponding average clustering coefficients, and the networks have a significant hierarchy. The correlation between news hot events and network nodes is not as good as users’ attention to the network.
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Vavryk, Petro. "One approach to formalizing the process of information dissemination based on diffusion-limited aggregation." Bulletin of Taras Shevchenko National University of Kyiv. Series: Physics and Mathematics, no. 1 (2022): 61–66. http://dx.doi.org/10.17721/1812-5409.2022/1.8.
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This article examines one of the approaches to the formalization of information dissemination processes based on the diffusion-limited aggregation model, using elements of cellular automata and their analogs. The model describes the dynamics of the information dissemination process without the influence of the mass media by taking into account the facts of information exchange that occurs during communication between participants of an arbitrary target audience. It is believed that the process is characterized by the property of self-similarity. An approach is proposed that makes it possible to study the dynamics of information dissemination processes, taking into account the attitude of the group members to each other and the attitude of the participants to the input information. As a result, an assessment of the effectiveness of the information dissemination process was obtained, which allows drawing conclusions regarding the success of information promotion measures. To demonstrate the processes of information dissemination modeled on the basis of the approach, the results of numerical experiments are presented, in which the implementation of the information exchange procedure for each person is limited to three members of the target group.
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Han, Shao Chun, Yun Liu, Hui Ling Chen, and Zhen Jiang Zhang. "Influence Model of User Behavior Characteristics on Information Dissemination." International Journal of Computers Communications & Control 11, no. 2 (January 26, 2016): 209. http://dx.doi.org/10.15837/ijccc.2016.2.2441.
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Quantitative analysis on human behavior, especially mining and modeling temporal and spatial regularities, is a common focus of statistical physics and complexity sciences. The in-depth understanding of human behavior helps in explaining many complex socioeconomic phenomena, and in finding applications in public opinion monitoring, disease control, transportation system design, calling center services, information recommendation. In this paper,we study the impact of human activity patterns on information diffusion. Using SIR propagation model and empirical data, conduct quantitative research on the impact of user behavior on information dissemination. It is found that when the exponent is small, user behavioral characteristics have features of many new dissemination nodes, fast information dissemination, but information continued propagation time is short, with limited influence; when the exponent is big, there are fewer new dissemination nodes, but will expand the scope of information dissemination and extend information dissemination duration; it is also found that for group behaviors, the power-law characteristic a greater impact on the speed of information dissemination than individual behaviors. This study provides a reference to better understand influence of social networking user behavior characteristics on information dissemination and kinetic effect.
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Ivokhin, Evgeniy V., and Yuriy A. Naumenko. "On Formalization of Information Dissemination Processes Based on Hybrid Diffusion Models." Journal of Automation and Information Sciences 50, no. 7 (2018): 79–86. http://dx.doi.org/10.1615/jautomatinfscien.v50.i7.70.
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Knight, David W., Arren Mendezona Allegretti, and Jerry J. Vaske. "Information dissemination-diffusion and marine protected area approval in the Philippines." Ocean & Coastal Management 113 (August 2015): 38–46. http://dx.doi.org/10.1016/j.ocecoaman.2015.05.016.
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Yu, Jianye, Junjie Lv, Yuanzhuo Wang, and Jingyuan Li. "Mechanism analysis of competitive information asynchronous dissemination on social networks." International Journal of Modern Physics C 30, no. 11 (November 2019): 1950094. http://dx.doi.org/10.1142/s0129183119500943.
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Information dissemination groups, especially those disseminating the same kind of information such as advertising, product promotion, etc., compete with each other when their information spread on social networks. Most of the existing methods analyze the dissemination mechanism mainly upon the information itself without considering human characteristics, e.g. relation networks, cooperation/defection, etc. In this paper, we introduce a framework of social evolutionary game (SEG) to investigate the influence of human behaviors in competitive information dissemination. Coordination game is applied to represent human behaviors in the competition of asynchronous information diffusion. We perform a series of simulations through a specific game model to analyze the mechanism and factors of information diffusion, and show that when the benefits of competitive information is around 1.2 times of the original one, it can compensate the loss of reputation caused by the change of strategy. Furthermore, through experiments on a dataset of two films on Sina Weibo, we described the mechanism of competition evolution over real data of social network, and validated the effectiveness of our model.
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Yan, Jiawen, Yuantao You, Yu Wang, and Dongfang Sheng. "Understanding the Complexity of Business Information Dissemination in Social Media: A Meta-Analysis of Empirical Evidence from China." Complexity 2021 (June 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/7647718.
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With the development of social networks, the complexity of the factors affecting the users’ information dissemination is increasing and the complexity of online social networks and influencing factors of individual behaviors and attitudes make the development of online public opinion present a dynamic, complex, and multifactor evolution. Analyzing the influencing factors of public opinion dissemination is conducive to optimize company management and information diffusion management. However, there has been no comprehensive analysis of the complex factors that influence the dissemination of information; this study focused on synthesizing 20 empirical studies on the influencing factors of China public opinion dissemination from the perspective of the user, and a meta-analysis was conducted. We establish the influencing factors of users’ information adoption model from three aspects of information source reliability, perceived information quality, and the heat of public opinion events based on elaboration likelihood model. The results indicated that the main influencing factors of public opinion communication are authority, reliability, quality of information form, quality of information editing, quality of information utility, and event attendance preference. Among the factors, authority and quality of information editing have more significant impacts on users’ information adoption behavior in the dissemination of public opinion. In addition, whether the type of event was a public emergency had a moderating effect. The results are helpful to explore the universality of the influencing factors so as to help related regulators better build a multiangle supervision mechanism and conduct early warning of information diffusion.
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Zhang, Zufan, Anqi Liu, Yinxue Yi, and Maobin Yang. "Exploring the Dynamical Behavior of Information Diffusion in D2D Communication Environment." Security and Communication Networks 2020 (September 22, 2020): 1–8. http://dx.doi.org/10.1155/2020/8848576.
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This paper is dedicated to exploring the dynamical behavior of information diffusion in the Device-to-Device (D2D) communication environment for information security, so as to study how to accelerate the dissemination of beneficial information and curb the spread of malicious information. A mathematical model of information diffusion considering the combined impact of user awareness and social tie between users is proposed. The equilibrium of the model and its stability are fully analyzed. Very importantly, there is a unique (viral) equilibrium that is globally asymptotically stable without any preconditions. This means that the spread of malicious information in the D2D communication environment cannot be completely eliminated whatever measures are taken, but its diffusion scale can be controlled by adjusting the value of the equilibrium, and then the goal of pursuing the best control effect at the minimum cost can be achieved. In the same way, the dissemination scale of beneficial information can be expanded. Finally, the obtained main theoretical results are illustrated by some examples, and some suggestions are also given.
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Xie, Dan, and Dongdong Lin. "IAPE Exploration under the International Communication Environment Based on Big Data Analysis of Social Network." Journal of Environmental and Public Health 2022 (September 29, 2022): 1–8. http://dx.doi.org/10.1155/2022/3798487.
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Social network (SN) plays an important role in issue focusing, public opinion filtering, and scheme policy making in the dissemination of ideological and political education (IAPE), which promotes it towards a more accurate and safe public health direction. After studying the information dissemination process and diffusion mode of IAPE, this paper puts forward the UECSR model of its information dissemination model of blockchain SN, adds the unique consensus node state of that, and redefines the information state transition process and dissemination probability calculation. Finally, the influence of the propagation probability of blockchain is discussed through experimental simulation classification, which can verify the model’s validity and rationality.
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Zhou, Nan, Xiu-Xiu Zhan, Song Lin, Shang-Hui Yang, Chuang Liu, Gui-Quan Sun, and Zi-Ke Zhang. "Information diffusion on communication networks based on Big Data analysis." Electronic Library 35, no. 4 (August 7, 2017): 745–57. http://dx.doi.org/10.1108/el-09-2016-0194.
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Purpose Information carriers (including mass media and We-Media) play important roles in information diffusion on social networks. The purpose of this paper is to investigate changes in the dissemination of information combing with data analysis. Design/methodology/approach This work analyzed nearly 200 years of coverage of different information carriers during different periods of human society, from the period of only mouth-to-mouth communication to the period of modern society. Information diffusion models are built to illustrate how the information dynamic changes with time and combined box office data of several movies to predict the process of information diffusion. In addition, a metric is defined to identify which information would become news in the future. Findings Results show that with the development of information carriers, information spreads faster and wider nowadays. The correctness of the metric proposed has been validated. Research limitations/implications The structure of social networks influences the dissemination of information. There are an enormous number of factors that influence the formation of hotspots. Practical implications The results and conclusion of this work will benefit by predicting the evolution of information carriers. The metric proposed will aid in searching hot news in the future. Originality/value This work may shed some light on a better understanding of information diffusion, spreading not only on social networks but also on the carriers used for the information spreading.
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Chen, Jing, Nana Wei, Chen Xin, Mingxin Liu, Zeren Yu, and Miaomiao Liu. "Anti-Rumor Dissemination Model Based on Heat Influence and Evolution Game." Mathematics 10, no. 21 (November 1, 2022): 4064. http://dx.doi.org/10.3390/math10214064.
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Aiming at the problem that the existing rumor dissemination models only focus on the characteristics of rumor dissemination and ignore anti-rumor dissemination, an evolution game model, SDIR, based on heat influence is proposed in this paper. Firstly, in order to solve the problem that rumor and anti-rumor information of emergency events disseminate simultaneously in social networks, the model extracts the factors that affect information dissemination: user behavior characteristics, user closeness and heat influence of participating topics. Secondly, anti-rumor information and an evolutionary game mechanism are introduced into the traditional SIR model, binary information is introduced to analyze the anti-rumor dissemination model SDIR, and the four state transitions and dissemination processes of SDIR are discussed. Finally, the SDIR model is experimentally validated in different datasets and dissemination models. The experimental results show that the SDIR model is in line with the actual dissemination law, and it can be proved that high self-identification ability plays a certain role in suppressing rumors; the anti-rumor information effectively inhibits the spread of rumor information to a certain extent. Compared with other models, the SDIR model is closer to the real diffusion range in the dataset.
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Zakhlebin, Igor, and Emoke-Ágnes Horvát. "Diffusion of Scientific Articles across Online Platforms." Proceedings of the International AAAI Conference on Web and Social Media 14 (May 26, 2020): 762–73. http://dx.doi.org/10.1609/icwsm.v14i1.7341.
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Online platforms have become the primary source of information about scientific advances for the wider public. As online dissemination of scientific findings increasingly influences personal decision-making and government action, there is a growing necessity and interest in studying how people disseminate research findings online beyond one individual platform. In this paper, we study the simultaneous diffusion of scientific articles across major online platforms based on 63 million mentions of about 7.2 million articles spanning a 7-year period. First, we find commonalities between people sharing science and other content such as news articles and memes. Specifically, we find recurring bursts in the coverage of individual articles with initial bursts co-occurring in time across platforms. This allows for a ranking of individual platforms based on the speed at which they pick up scientific information. Second, we explore specifics of sharing science. We reconstruct the likely underlying structure of information diffusion and investigate the transfer of information about scientific articles within and across different platforms. In particular, we (i) study the role of different users in the dissemination of information to better understand who are the prime sharers of knowledge, (ii) explore the propagation of articles between platforms, and (iii) analyze the structural virality of individual information cascades to place science sharing on the spectrum between pure broadcasting and peer-to-peer diffusion. Our work provides the broadest study to date about the sharing of science online and builds the basis for an informed model of the dynamics of research coverage across platforms.
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Tang, Mingsheng, Xinjun Mao, Shuqiang Yang, and Huiping Zhou. "A Dynamic Microblog Network and Information Dissemination in “@” Mode." Mathematical Problems in Engineering 2014 (2014): 1–15. http://dx.doi.org/10.1155/2014/492753.
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Social media, especially the microblogs, emerge as a part of our daily life and become a key way to information spread. Thus, information dissemination in the microblog became a research hotspot. Based on some principles that are summarized from the microblog users’ behaviors, this paper proposes a dynamic microblog network model. Through simulations this network has the features of periodicity of average degree, high clustering coefficient, high degree of modularity, and community. Besides, an information dissemination model through “@” in the microblog has been presented. With the microblog network model and the zombie-city model, this paper has modelled an artificial microblog and has simulated the information dissemination in the artificial microblog with different scenes. Therefore, some interesting findings have been presented. (1) Due to a better connectivity, information could spread widely in a random network; (2) information spreads more quickly in a stable microblog network; (3) the decay rate of the relationships will have an effect on information dissemination; that is, with a lower decay rate, information spreads more quickly and widely; (4) the higher active level of users in microblog could promote information spread widely and quickly; (5) the “@” mode of information dissemination makes a high modularity of the information diffusion network.
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APOLLONI, ANDREA, and FLORIANA GARGIULO. "DIFFUSION PROCESSES THROUGH SOCIAL GROUPS' DYNAMICS." Advances in Complex Systems 14, no. 02 (April 2011): 151–67. http://dx.doi.org/10.1142/s0219525911003037.
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Axelrod's model describes the dissemination of a set of cultural traits in a society constituted by individual agents. In a social context, nevertheless, individual choices toward a specific attitude are also at the basis of the formation of communities, groups and parties. The membership in a group changes completely the behavior of single agents who start acting according to a social identity. Groups act and interact among them as single entities, but still conserve an internal dynamics. We show that, under certain conditions of social dynamics, the introduction of group dynamics in a cultural dissemination process avoids the flattening of the culture into a single entity and preserves the multiplicity of cultural attitudes. We also consider diffusion processes on this dynamical background, showing the conditions under which information as well as innovation can spread through the population in a scenario where the groups' choices determine the social structure.
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Ghosh, Nirmalya, Barbara Holshouser, Udo Oyoyo, Stanley Barnes, Karen Tong, and Stephen Ashwal. "Combined Diffusion Tensor and Magnetic Resonance Spectroscopic Imaging Methodology for Automated Regional Brain Analysis: Application in a Normal Pediatric Population." Developmental Neuroscience 39, no. 5 (2017): 413–29. http://dx.doi.org/10.1159/000475545.
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During human brain development, anatomic regions mature at different rates. Quantitative anatomy-specific analysis of longitudinal diffusion tensor imaging (DTI) and magnetic resonance spectroscopic imaging (MRSI) data may improve our ability to quantify and categorize these maturational changes. Computational tools designed to quickly fuse and analyze imaging information from multiple, technically different datasets would facilitate research on changes during normal brain maturation and for comparison to disease states. In the current study, we developed a complete battery of computational tools to execute such data analyses that include data preprocessing, tract-based statistical analysis from DTI data, automated brain anatomy parsing from T1-weighted MR images, assignment of metabolite information from MRSI data, and co-alignment of these multimodality data streams for reporting of region-specific indices. We present statistical analyses of regional DTI and MRSI data in a cohort of normal pediatric subjects (n = 72; age range: 5-18 years; mean 12.7 ± 3.3 years) to establish normative data and evaluate maturational trends. Several regions showed significant maturational changes for several DTI parameters and MRSI ratios, but the percent change over the age range tended to be small. In the subcortical region (combined basal ganglia [BG], thalami [TH], and corpus callosum [CC]), the largest combined percent change was a 10% increase in fractional anisotropy (FA) primarily due to increases in the BG (12.7%) and TH (9%). The largest significant percent increase in N-acetylaspartate (NAA)/creatine (Cr) ratio was seen in the brain stem (BS) (18.8%) followed by the subcortical regions in the BG (11.9%), CC (8.9%), and TH (6.0%). We found consistent, significant (p < 0.01), but weakly positive correlations (r = 0.228-0.329) between NAA/Cr ratios and mean FA in the BS, BG, and CC regions. Age- and region-specific normative MR diffusion and spectroscopic metabolite ranges show brain maturation changes and are requisite for detecting abnormalities in an injured or diseased population.
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Ivohin, E. V., O. F. Voloshyn, and M. F. Makhno. "Modeling of Information Dissemination Processes Based on Diffusion Equations with Fuzzy Time Accounting." Cybernetics and Systems Analysis 57, no. 6 (November 2021): 896–905. http://dx.doi.org/10.1007/s10559-021-00416-z.
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andersen, Ross. "Colloquium – Communications and Public Information Colloquium - Diffusion and Dissemination Strategies for Translating Science." Medicine & Science in Sports & Exercise 38, Supplement (May 2006): 78. http://dx.doi.org/10.1249/00005768-200605001-00792.
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Ivokhin, Eugene, Larisa Adzhubey, Yuriy Naumenko, and Mykhailo Makhno. "On one approach to using of fractional analysis for hybrid modeling of information distribution processes." System research and information technologies, no. 4 (December 22, 2021): 128–37. http://dx.doi.org/10.20535/srit.2308-8893.2021.4.10.
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The article discusses a technique for constructing a model and a method for finding solutions in the problem of imitating the process of information dissemination based on the use of a boundary value problem for a fractional differential equation in partial derivatives. It is proposed to use the analogy technique for modeling information dissemination processes, which is based on the use of the features of a fractional analysis and the diffuse nature of information penetration processes. A method for constructing hybrid models is proposed, which makes it possible to take into account changes in the interval of values of the spatial variable over time. Homogeneous and inhomogeneous models of diffusion processes are considered, which make it possible to numerically obtain and analyze experimental data for solving problems of monitoring the levels of information dissemination in social groups.
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Sun, Qing, and Zhong Yao. "Evolutionary Game Analysis of Competitive Information Dissemination on Social Networks: An Agent-Based Computational Approach." Mathematical Problems in Engineering 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/679726.
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Social networks are formed by individuals, in which personalities, utility functions, and interaction rules are made as close to reality as possible. Taking the competitive product-related information as a case, we proposed a game-theoretic model for competitive information dissemination in social networks. The model is presented to explain how human factors impact competitive information dissemination which is described as the dynamic of a coordination game and players’ payoff is defined by a utility function. Then we design a computational system that integrates the agent, the evolutionary game, and the social network. The approach can help to visualize the evolution of % of competitive information adoption and diffusion, grasp the dynamic evolution features in information adoption game over time, and explore microlevel interactions among users in different network structure under various scenarios. We discuss several scenarios to analyze the influence of several factors on the dissemination of competitive information, ranging from personality of individuals to structure of networks.
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Granados, Alicia, Egon Jonsson, H. David Banta, Lisa Bero, Ann Bonair, Mme Camille Cochet, Nick Freemantle, et al. "EUR-ASSESS Project Subgroup Report on Dissemination and Impact." International Journal of Technology Assessment in Health Care 13, no. 2 (1997): 220–86. http://dx.doi.org/10.1017/s0266462300010370.
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The objective of health technology assessment (HTA) is to support decision making in health care. HTA does not claim to provide a definite solution to a health care problem, but to assist decision makers with evidence-based information about the clinical, ethical, social, and economic implications of the development, diffusion, and use of health care technology.
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Kwon, Joseph, Ingoo Han, and Byoungsoo Kim. "Effects of source influence and peer referrals on information diffusion in Twitter." Industrial Management & Data Systems 117, no. 5 (June 12, 2017): 896–909. http://dx.doi.org/10.1108/imds-07-2016-0290.
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Purpose Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover. Design/methodology/approach The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter. Findings Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination. Research limitations/implications The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL. Practical implications The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process. Originality/value Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.
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Utami, Sabrina Rahma, and Pitoyo Pitoyo. "Communication strategy of the Communication and Informatics Department of Payakumbuh in disseminating Covid-19 information in March – August 2021." Indonesian Journal of Communication Studies 14, no. 2 (January 24, 2022): 92. http://dx.doi.org/10.31315/ijcs.v14i2.5397.
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The Covid-19 pandemic is a situation that requires proper handling so that it does not spread further. Dissemination of Covid-19 information is one of the steps taken by the communication and informatics department of Payakumbuh (Diskominfo) to increase public participation by providing knowledge and changing people's attitudes to be able to take steps to prevent Covid-19. This study aims to determine the form of communication strategy of The Communication and Informatics Department of Payakumbuh in disseminating information on Covid-19 with supporting factors and inhibiting factors in its implementation. This research uses a qualitative approach with constructivism paradigm. The theory used in this study is the theory of diffusion of innovation. Data collection techniques were carried out through in-depth and semi-structured interviews, as well as observation. The results of the study show that the form of communication strategy is the stages of compiling communicants or communication targets, messages in the dissemination of Covid-19 information, media selection, determining the frequency of information dissemination, the role of communicators, and public response to the dissemination of information on Covid-19. One of the supporting factors in the dissemination of Covid-19 information is that The Communication And Informatics Department Of Payakumbuh’s public relations officer has understood the use of information and communication technology, thus facilitating the dissemination of Covid-19 information through new media, and one of the inhibiting factors is the lack of coordination at The Communication and Informatics Department Of Payakumbuh in managing the website, and the unopenness of hospital management in Payakumbuh in providing information updates on Covid-19 in Payakumbuh City.
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CUNHA, Francisco José Aragão Pedroza, Núbia Moura RIBEIRO, Roberto Luiz Souza MONTEIRO, and Hernane Borges de Barros PEREIRA. "Social network analysis as a strategy for monitoring the dissemination of information between hospitals." Transinformação 28, no. 3 (December 2016): 309–22. http://dx.doi.org/10.1590/2318-08892016000300006.
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Abstract This article explores the structure of connections between the hospitals that are members of a hospital management innovation and learning network. This study was based on the assumption that there are limitations to encourage the communication and diffusion of knowledge between health service organizations if they are not effectively connected through social networks. Social Network Analysis was used as a strategy for monitoring the dissemination of information between hospitals. Theoretical concepts of diffusion of knowledge allowed emphasizing the role of the phenomena and communication and learning processes as the driving forces for health service innovation. The results showed weak interactions between hospitals and a lack of cohesion within the network. Therefore, there is a need for policies to promote the flow of data and information, which requires network openness to foster the exchange of innovative processes. Interactions between these hospitals in horizontal and disseminated structures have yet to be stimulated, established, incorporated, and developed by individuals, institutions and health service organizations.
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Shi, Juan, Ping Hu, Kin Keung Lai, and Gang Chen. "Determinants of users’ information dissemination behavior on social networking sites." Internet Research 28, no. 2 (April 4, 2018): 393–418. http://dx.doi.org/10.1108/intr-01-2017-0038.
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Purpose As a new communication paradigm, social networking sites (SNS) have boosted information diffusion and viral marketing. Prior researchers have identified various factors affecting information dissemination on SNS. However, they often focus on limited factors and there is a lack of an integrated theoretical framework that explains aspects of relevant factors. Besides, the research on the impacts of relationships on individual retweeting behavior is still controversial. The purpose of this paper is to propose a theoretical framework to systematically investigate the determinants of individual dissemination behavior on SNS based on the elaboration likelihood model (ELM). Moreover, the authors also examine the relative importance of those relevant factors. Design/methodology/approach The authors randomly selected 1,250 members of Twitter and crawled posts published by each member since he/she created the Twitter account using Twitter API. The authors processed the data to create panel data and tested hypotheses with the panel logit model. Findings Factors both on the central route and on the peripheral route of ELM have positive impacts on individual dissemination behavior. Among them, information receiver-related factor and relationships-related factors are the most influential. Contrastingly, source-related factors are the least influential. Furthermore, the authors find that social tie strength mediates almost 50 percent of the effect of value homophily on individual dissemination behavior. Originality/value The authors are the first to directly apply ELM to examine individual dissemination behavior on SNS. By integrating factors into the two information processing routes, They incorporate relevant factors into the model and systematically analyze their impacts on individual retweeting behavior on SNS. The research offers at least one explanation for the contradictory findings about the effect of homophily on individual sharing behavior in previous research. The authors propose new variables that gauge topical relevance and interpersonal value homophily on SNS.
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Michaels, Sarah. "New Perspectives on Diffusion of Earthquake Knowledge." Earthquake Spectra 8, no. 1 (February 1992): 159–75. http://dx.doi.org/10.1193/1.1585675.
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This article contrasts different perspectives concerning the diffusion of earthquake knowledge among non-scientific audiences. The perspective that underlies much of current information dissemination efforts is a “producer-user” model. This model assumes a stable set of knowledge producers and an identifiable set of knowledge users. An alternative perspective is an “issue networks” model. This model assumes a more fluid set of actors and issues linked by common concerns about earthquake hazards. This latter model is suggested as a more accurate depiction of knowledge diffusion within the state and local earthquake policy arenas. This portrayal also leads to consideration of new strategies for disseminating seismological, earthquake engineering and related knowledge.
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Shi, Rui, Xiaoyong Hou, and Chang Liu. "Model of Negative Emotional Information Communication among Netizens under Corporate Negative Events." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/3527980.
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Corporate negative events have been frequently exposed in the information age. Research on the dissemination mechanism of negative emotional information for netizens contributes corporates to monitor public opinion trends and resolve public opinion crises. Combining emotional infection theory and the classic infectious disease model, we first divided emotional communication stage into individual and group propagation stages and constructed the SEI1I2R model of negative emotional information communication among netizens under corporate negative events. Then, we performed the model analysis and simulation. Results indicated that trust and communication conversion rates are in direct proportion to negative emotional diffusion speed; corporate response rate is inversely proportional to negative emotional diffusion speed; however, the effects of self-regulation are insignificant.
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Byamukama, Willbroad, Mbarara Rebecca Kalibwani, and Businge Phelix Mbabazi. "Information System (Is) Models: Technology as a Service for Agricultural Information Dissemination in Developing Countries (Uganda). A Systematic Literature Review." International Journal of Scientific and Management Research 05, no. 04 (2022): 42–54. http://dx.doi.org/10.37502/ijsmr.2022.5404.
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This article summarizes the current literature by reviewing the concepts, applications, and development of technology adoption models and theories that are supported by the literature review, with the novelty technology’s prospective application being the main focus. These included but were not limited to, the concepts of Diffusion of Innovations (DIT) (Rogers, 1995), Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1995), and Diffusion of Innovations (DIT) (Rogers, 1995). Theory of Planned Behavior (TPB) (Ajzen, 1985, 1991), Theory of Planned Behaviour, (Taylor and Todd, 1995), the Technology Acceptance Model (TAM) (Davis, Bogozzi and Warshaw, 1989, Technology Acceptance Model two (TAM2) Venkatesh and Davis (2000), Technology Acceptance Model three (TAM3) Venkatesh and Bala (2008), Unified Theory of Acceptance Model (UTAUT) Venkatesh et al; 2012 and the Extended Unified Theory of Acceptance Model (UTAUT2) Venkatesh et al; 2016. These assessments can give some information on technology adoption levels and potential applications for future researchers to consider, recognize and comprehend the underlying technology models and ideas that will have an impact on the preceding, current, and future applications of technology adoption and agricultural information dissemination by smallholder rural farmers.
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Akhter, Pervaiz, Tanveer Hussain, and Hafiz Bilal Ahsan. "Mass Media as a Source of Agricultural Information: An Overview of Literature." Global Regional Review VI, no. II (June 30, 2021): 58–63. http://dx.doi.org/10.31703/grr.2021(vi-ii).08.
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This research paper provides a systematic review of published research work by different research scholars regarding the role of communication channels in disseminating agricultural information and the diffusion of agricultural innovations among agriculturists. Findings of review are summarized with the help of reviewing methodology, major findings and implications of earlier published researches. The review depicts that there are significant variations in findings of the relevant researches some certain reasons like geographical, methodological and theoretical perspectives. It is hard to draw any specific conclusion about the role of a different communication channel in the agriculture sector. However, the review has revealed that the different channels of communication have a different role regarding the dissemination of agricultural information and diffusion of innovations amongst farmers.
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Liu, Xiaoyang, and Daobing He. "Information Propagation and Public Opinion Evolution Model Based on Artificial Neural Network in Online Social Network." Computer Journal 63, no. 11 (November 5, 2019): 1689–703. http://dx.doi.org/10.1093/comjnl/bxz104.
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Abstract This paper proposes a new information dissemination and opinion evolution IPNN (Information Propagation Neural Network) model based on artificial neural network. The feedforward network, feedback network and dynamic evolution algorithms are designed and implemented. Firstly, according to the ‘six degrees separation’ theory of information dissemination, a seven-layer neural network underlying framework with input layer, propagation layer and termination layer is constructed; secondly, the information sharing and information interaction evolution process between nodes are described by using the event information forward propagation algorithm, opinion difference reverse propagation algorithm; finally, the external factors of online social network information dissemination is considered, the impact of external behavior patterns is measured by media public opinion guidance and network structure dynamic update operations. Simulation results show that the proposed new mathematical model reveals the relationship between the state of micro-network nodes and the evolution of macro-network public opinion. It accurately depicts the internal information interaction mechanism and diffusion mechanism in online social network. Furthermore, it reveals the process of network public opinion formation and the nature of public opinion explosion in online social network. It provides a new scientific method and research approach for the study of social network public opinion evolution.
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Eljiz, Kathy, David Greenfield, Anne Hogden, Robyn Taylor, Nazlee Siddiqui, Maria Agaliotis, and Marianna Milosavljevic. "Improving knowledge translation for increased engagement and impact in healthcare." BMJ Open Quality 9, no. 3 (September 2020): e000983. http://dx.doi.org/10.1136/bmjoq-2020-000983.
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Ineffective knowledge dissemination contributes to clinical practice and service improvements not being realised. Meaningful knowledge translation can occur through the understanding and matching of appropriate communication mediums that are relevant for different stakeholders or audiences. To this end, we present a dissemination instrument, the ‘REAch and Diffusion of health iMprovement Evidence’ (README) checklist, for the communication of research findings, integrating both traditional and newer communication mediums. Additionally, we propose a ‘Strategic Translation and Engagement Planning’ (STEP) tool, for use when deciding which mediums to select. The STEP tool challenges the need for communicating complex and simple information against the desire for passive or active stakeholder interaction. Used collaboratively by academics and health professionals, README and STEP can promote co-production of research, subsequent diffusion of knowledge, and develop the capacity and skills of all stakeholders.
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Chen, Tingqiang, Lei Wang, Jining Wang, and Qi Yang. "A Network Diffusion Model of Food Safety Scare Behavior considering Information Transparency." Complexity 2017 (2017): 1–16. http://dx.doi.org/10.1155/2017/5724925.
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This study constructs the network diffusion model of food safety scare behavior under the effect of information transparency and examines the network topology and evolution characteristics of food safety scare behavior in a numerical simulation. The main conclusions of this study are as follows. (1) Under the effect of information transparency, the network degree distribution of food safety scare behavior diffusion demonstrates the decreasing characteristics of diminishing margins. (2) Food safety scare behavior diffusion increases with the information dissemination rate and consumer concern about food safety incidents and shows the characteristics of monotone increasing. And with the increasing of the government food safety supervision information transparency and media food safety supervision information transparency, the whole is declining characteristic of diminishing marginal. In addition, the extinction of food safety scare behavior cannot be achieved gradually given a single regulation of government food safety supervision information transparency and media food safety supervision information transparency. (3) The interaction effects between improving government food safety supervision information transparency or media food safety supervision information transparency and declining consumer concerns about food safety incidents or information transmission rate can engender the suppression of food safety scare behavior diffusion.
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Kanouse, David E., and Itzhak Jacoby. "When Does Information Change Practitioners' Behavior?" International Journal of Technology Assessment in Health Care 4, no. 1 (January 1988): 27–33. http://dx.doi.org/10.1017/s0266462300003214.
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AbstractPrograms that disseminate information to health care practitioners often do so partly to encourage appropriate changes in practice. However, merely providing information is seldom enough to accomplish such changes. If information transfer programs are to influence practice, they must be designed to maximize the conditions facilitating change. Reliance on a diffusion model for thinking about how information reaches practitioners has led researchers to over-emphasize the importance of exposure to information and ignore other factors that determine whether change will occur, such as practitioners' motivation to change, the context in which clinical decisions are made, and how information is presented. The fact that successful dissemination will not necessarily produce change also has implications for how information transfer programs should be monitored and evaluated.
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Liu, Xiaoyang, Daobing He, and Chao Liu. "Modeling information dissemination and evolution in time-varying online social network based on thermal diffusion motion." Physica A: Statistical Mechanics and its Applications 510 (November 2018): 456–76. http://dx.doi.org/10.1016/j.physa.2018.07.010.
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Gaviria-Marin, Magaly, and Claudio Cruz-Cázares. "Ranking web as indicator of knowledge diffusion: an application for SMEs." Academia Revista Latinoamericana de Administración 33, no. 2 (February 21, 2020): 219–40. http://dx.doi.org/10.1108/arla-02-2019-0056.
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PurposeThe purpose of this article is to analyze the influence of the diversity of information and the use of Web 2.0 applications on the web positioning of online business information providers.Design/methodology/approachA total of 203 online business information provider websites were selected using three search engines (Google, Yahoo and Bing). This information was encoded to develop two logistic regression models.FindingsThe results suggest that the knowledge offered and the resources used to transfer this knowledge play important roles in the web positioning of online business information providers. This study found that entrepreneurs mainly seek technological knowledge, and to acquire it, they use various Web 2.0 applications that do not necessarily include social networks.Practical implicationsThis article presents a novel proposal to analyze how knowledge diversity and Web 2.0 applications influence the web rankings of websites that offer information and knowledge for established or new, small and medium enterprises.Originality/valueThis article is novel in that it links the theory of knowledge dissemination with the technologies of the Internet (websites, Web 2.0 applications) and provides evidence of diverse sources of online information that are potentially useful for the entrepreneur of the twentieth century.
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Firdaniza, Firdaniza, Budi Nurani Ruchjana, Diah Chaerani, and Jaziar Radiant. "Information diffusion model with homogeneous continuous time Markov chain on Indonesian Twitter users." International Journal of Data and Network Science 6, no. 3 (2022): 659–68. http://dx.doi.org/10.5267/j.ijdns.2022.4.006.
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In this paper, a homogeneous continuous time Markov chain (CTMC) is used to model information diffusion or dissemination, also to determine influencers on Twitter dynamically. The tweeting process can be modeled with a homogeneous CTMC since the properties of Markov chains are fulfilled. In this case, the tweets that are received by followers only depend on the tweets from the previous followers. Knowledge Discovery in Database (KDD) in Data Mining is used to be research methodology including pre-processing, data mining process using homogeneous CTMC, and post-processing to get the influencers using visualization that predicts the number of affected users. We assume the number of affected users follows a logarithmic function. Our study examines the Indonesian Twitter data users with tweets about covid19 vaccination resulted in dynamic influencer rankings over time. From these results, it can also be seen that the users with the highest number of followers are not necessarily the top influencer.
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Gan, Chenquan, Xiaoke Li, Lisha Wang, and Zufan Zhang. "The Impact of User Behavior on Information Diffusion in D2D Communications: A Discrete Dynamical Model." Discrete Dynamics in Nature and Society 2018 (December 9, 2018): 1–9. http://dx.doi.org/10.1155/2018/3745769.
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This paper aims to explore the impact of user behavior on information diffusion in D2D (Device-to-Device) communications. A discrete dynamical model, which combines network metrics and user behaviors, including social relationship, user influence, and interest, is proposed and analyzed. Specifically, combined with social tie and user interest, the success rate of data dissemination between D2D users is described, and the interaction factor, user influence, and stability factor are also defined. Furthermore, the state transition process of user is depicted by a discrete-time Markov chain, and global stability analysis of the proposed model is also performed. Finally, some experiments are examined to illustrate the main results and effectiveness of the proposed model.
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Li, Yuejiang, H. Vicky Zhao, and Yan Chen. "An epidemic model for correlated information diffusion in crowd intelligence networks." International Journal of Crowd Science 3, no. 2 (August 30, 2019): 168–83. http://dx.doi.org/10.1108/ijcs-01-2019-0005.
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Purpose With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks. Design/methodology/approach This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them. Findings The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model. Originality/value The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.
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Zhang, Yaming, Fei Liu, Yaya H. Koura, and Yanyuan Su. "Dynamics of a Delayed Interactive Model Applied to Information Dissemination in Social Networks." Mathematical Problems in Engineering 2021 (March 8, 2021): 1–12. http://dx.doi.org/10.1155/2021/6611168.
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Reducing fake news and rumor propagation through social media may be challenging to achieve when dealing with sensible contents and communities with free access to online shared resources. Controlling rumor dissemination and promoting true news are the main techniques used to strangle false information that may result in dramatic effect on human wellbeing in an open or closed environment. In this article, we studied a predator-prey model with constant delay in both predator and prey equations and applied the proposed model to the underlying relationship between the existing rumor propagating through social media and the related authoritative information containing the truth broadcast to reduce the respective rumor negative effect on the targeted community. We showed that the proposed system was very responsive to small perturbations and exhibited complex dynamical behavior around the steady-state equilibrium when interaction occurs and delay is applied, considering the controlled situations. Numerical results suggested applying relatively small delay, which represents the ideal time to publish the related propagating rumor curative content to reduce its diffusion speed and promote the truth.
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Young, Kristen Lee. "Information Professionals’ Attitudes Influence the Diffusion of Information and Communication Technologies." Evidence Based Library and Information Practice 5, no. 1 (March 17, 2010): 147. http://dx.doi.org/10.18438/b8bg93.
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A Review of:
Rabina, D. L., & Walczyk, D. J. (2007). Information professionals’ attitude toward the adoption of innovations in everyday life. Information Research, 12(4), 1-15.
Objective – This study examined the general characteristics and patterns of librarians in connection with their willingness to adopt information and communication technologies.
Design – Online questionnaire.
Setting – General distribution to information professionals through online inquiry. More than 70% of responders worked in public or academic libraries.
Subjects – Librarians and library staff at mostly public and academic libraries.
Methods – The study was conducted during a two week period in April 2006 through an online questionnaire that was sent to library and librarian-related electronic mail lists. The questionnaire was divided into two parts and contained a total of 39 questions. Part one contained eight questions that asked for demographic data and the respondent’s daily attitude toward the adoption of information and communication technologies. Questions regarding age, number of years worked in a library, career, type of library environment worked in, and primary responsibilities within that environment were asked. For one question the respondents were asked to identify which of the categories they fall under when adopting a new technology. The results from part one were used to consider the innovativeness of librarians. The results from part two were used for a study of opinions on innovations and their relative advantage.
Main Results – A total of 1,417 responses were received. Of those, 1,128 were fully completed and considered valid and used for inquiry. The majority of respondents worked in public or academic libraries. Nine hundred and twenty-six respondents, or 88%, were from the U.S. and represented more than 300 distinct zip codes. Two hundred and two respondents, or 12%, were international respondents.
This study notes that the sociologist, Everett Rogers, identified and defined five adopter categories in 1958. Those categories are: innovators, early adapters, early majority, late majority, and laggards. The findings of this study indicate that regardless of the demographic variables considered, more than 60% of respondents, the majority of librarians surveyed, fall into two contrasting adapter categories: early adopters and early majority. The study suggests that the efficient and effective diffusion of new technologies in library settings may be difficult. Three problematic areas among librarians for the dissemination of innovation were identified: conflicting opinions among multiple opinion leaders, deceleration in the rate of adoption, and improper re-invention. The findings of the study also suggest that “contrary to common beliefs, librarians in academic or special libraries are no more innovative than public or school librarians” (Conclusion, ¶3).
Conclusion – The study concludes that librarians’ attitudes are unevenly distributed with most either accepting new innovations or being late adopters. The variables of age, role, tenure, and library type had little impact on the approach of the professional toward innovation. The identification of the three problem areas: opinion leadership, deceleration of adoption, and improper re-invention, represents where more time and effort may need to be spent to make the implementation of new technology a smoother process.
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Wang, Jia, Mengyao Guo, Li Zhang, Lujie Chen, and Xiaorong Hou. "Research on Dissemination Rule of Public Opinion from SNA Perspective: Taking the Vaccine Safety Event as an Example." Studies in Media and Communication 5, no. 1 (March 15, 2017): 42. http://dx.doi.org/10.11114/smc.v5i1.2039.
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With the rapid development of social media, the dissemination of health information has attracted more attention from people. To reveal the rule and mode of information diffusion path is the key to effective crisis prevention and control of information. In this paper, the team took the vaccine safety events as an example, selected and analyzed two hottest microblogs from each phase of one event. The team did visual analysis via Zhiwei which was one academic micro data analysis platform, and utilized social network analysis (SNA) to explore the propagating rules of public opinion.
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Jin, Xianlin. "Exploring Crisis Communication and Information Dissemination on Social Media: Social Network Analysis of Hurricane Irma Tweets." Journal of International Crisis and Risk Communication Research 3, no. 2 (October 1, 2020): 179–210. http://dx.doi.org/10.30658/jicrcr.3.2.3.
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This study utilized social network analysis to identify the top 10 Twitter influentials during the Hurricane Irma crisis period and examined the relationship between social media attributes and the bridge influence of controlling information flow. The number of a user’s followers and tweets significantly predicted one’s control of information. Crisis information tended to be shared in scattered subgroups. Social network boundaries impeded information diffusion, and the communication pattern was largely one-way. The findings partially supported the opinion leader argument while indicating that influentials can directly generate information, which is consistent with the social-mediated crisis communication model. Such findings will contribute to crisis literature and help emergency management professionals advance social media usage to disseminate crisis information, build effective communication, and provide immediate disaster relief responses
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Mozafari, Niloofar, Ali Hamzeh, and Sattar Hashemi. "Modelling information diffusion based on non-dominated friends in social networks." Journal of Information Science 43, no. 6 (September 1, 2016): 801–15. http://dx.doi.org/10.1177/0165551516667656.
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In recent years, social networks have played a strong role in diffusing information among people all around the globe. Therefore, the ability to analyse the diffusion pattern is essential. A diffusion model can identify the information dissemination pattern in a social network. One of the most important components of a diffusion model is information perception which determines the source each node receives its information from. Previous studies have assumed information perception to be just based on a single factor, that is, each individual receives information from their friend with the highest amount of information, whereas in reality, there exist other factors, such as trust, that affect the decision of people for selecting the friend who would supply information. These factors might be in conflict with each other, and modelling diffusion process with respect to a single factor can give rise to unacceptable results with respect to the other factors. In this article, we propose a novel information diffusion model based on non-dominated friends (IDNDF). Non-dominated friends are a set of friends of a node for whom there is no friend better than them in the set based on all considered factors, considering different factors simultaneously significantly enhance the proposed information diffusion model. Moreover, our model gives a chance to all non-dominated friends to be selected. Also, IDNDF allows having partial knowledge by each node of the social network. Finally, IDNDF is applicable to different types of data, including well-known real social networks like Epinions, WikiPedia, Advogato and so on. Extensive experiments are performed to assess the performance of the proposed model. The results show the efficiency of the IDNDF in diffusion of information in varieties of social networks.
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Mansoor, Ahmed Salmen, and Mokhtaruddin Ahmad. "The Role of Social Media Use in Social Coordination among Relief Local Organizations during Response to Humanitarian Crisis in Yemen." Jurnal Pengajian Media Malaysia 22, no. 1 (May 31, 2020): 51–68. http://dx.doi.org/10.22452/jpmm.vol22no1.4.
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The extreme levels of uncertainty and complexity surrounding disaster relief operations call for the rapid diffusion of information. Thus, to collect and share information, humanitarian organizations have become largely dependent on Internet-based social media platforms. This paper presented a field study examining the effective way information can be diffused for the coordination of relief local organizations during their response to the humanitarian crisis in Yemen. The study made use of a statistical method of quantitative to obtain the results through social media use in access decision-making information of response and promotion of social coordination among the local relief organizations and study of humanitarian needs and the equitable distribution of relief. This finding is indicating information posted on social media during a disaster was exhibited expediently compared to other means of dissemination that provides needed information at later disaster phases, however, the participation in the diffusion of information decreases with more communications.
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Totare, Prof R. Y., Aishwarya Ahergawli, Abhijeet Girase, Ishwari Tale, and Ayushi Khanbard. "A Review on Twitter Sentiment Analysis Using ML." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 1930–33. http://dx.doi.org/10.22214/ijraset.2022.48382.
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Abstract: Social Media sites like twitter have billions of people share their opinions day by day as tweets. As tweet is characteristic short and basic way of human emotions. So, in this paper we focused on sentiment analysis of Twitter data. Most of Twitter's existing sentiment analysis solutions basically consider only the textual information of Twitter messages and strives to work well in the face of short and ambiguous Twitter messages. Recent studies show that patterns of spreading feelings on Twitter have close relationships with the polarities of Twitter messages. In this paper focus on how to combine the textual information of Twitter messages and sentiment dissemination models to get a better performance of sentiment analysis in Twitter data. To this end, proposed system first analyses the diffusion of feelings by studying a phenomenon called inversion of feelings and find some interesting properties of the reversal of feelings. Therefore, we consider the interrelations between the textual information of Twitter messages and the patterns of diffusion of feelings, and propose random forest machine learning to predict the polarities of the feelings expressed in Twitter messages. As far as we know, this work is the first to use sentiment dissemination models to improve Twitter's sentiment analysis. Numerous experiments in the real-world dataset show that, compared to state-of-the-art text-based analysis algorithms.
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Perdana, Arif, Alastair Robb, and Fiona Rohde. "XBRL Diffusion in Social Media: Discourses and Community Learning." Journal of Information Systems 29, no. 2 (December 1, 2014): 71–106. http://dx.doi.org/10.2308/isys-50996.
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ABSTRACT Multiple discourses are critical in determining the success of information technology (IT) diffusion. Since its inception, such discourses also appear in the eXtensible Business Reporting Language (XBRL) diffusion sphere. To help explain XBRL diffusion, we explore the discourses relative to XBRL in social media. A case study with text mining and content analysis was conducted to address three research questions covering community discourses, polarity of viewpoint, and learning surrounding XBRL in social media. Our sample data consisted of members' posts and comments in LinkedIn XBRL groups over the period 2010 to 2013. Our analysis finds that XBRL discourses in social media have largely revolved around the dissemination of XBRL information to raise awareness among potential adopters (i.e., theorization) and to properly implement XBRL (i.e., translation). Our findings indicate that XBRL's theorization is not in doubt, while XBRL's translation remains challenging. Professionals generally view XBRL positively. Those who view XBRL less favorably are more likely to be skeptical rather than dismissive. We also observe that social media like LinkedIn is a relevant channel for communities to learn about XBRL. We discuss the findings and include several insights and implications that may be useful in augmenting the future of XBRL.
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Sharma, Karishma, Xinran He, Sungyong Seo, and Yan Liu. "Network Inference from a Mixture of Diffusion Models for Fake News Mitigation." Proceedings of the International AAAI Conference on Web and Social Media 15 (May 22, 2021): 668–79. http://dx.doi.org/10.1609/icwsm.v15i1.18093.
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The dissemination of fake news intended to deceive people, influence public opinion and manipulate social outcomes, has become a pressing problem on social media. Moreover, information sharing on social media facilitates diffusion of viral information cascades. In this work, we focus on understanding and leveraging diffusion dynamics of false and legitimate contents in order to facilitate network interventions for fake news mitigation. We analyze real-world Twitter datasets comprising fake and true news cascades, to understand differences in diffusion dynamics and user behaviours with regards to fake and true contents. Based on the analysis, we model the diffusion as a mixture of Independent Cascade models (MIC) with parameters \theta_T , \theta_F over the social network graph; and derive unsupervised inference techniques for parameter estimation of the diffusion mixture model from observed, unlabeled cascades. Users influential in the propagation of true and fake contents are identified using the inferred diffusion dynamics. Characteristics of the identified influential users reveal positive correlation between influential users identified for fake news and their relative appearance in fake news cascades. Identified influential users tend to be related to topics of more viral information cascades than less viral ones; and identified fake news influential users have relatively fewer counts of direct followers, compared to the true news influential users. Intervention analysis on nodes and edges demonstrates capacity of the inferred diffusion dynamics in supporting network interventions for mitigation.
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Arifin, Edvan Tazul, Jondri Jondri, and Indwiarti Indwiarti. "Prediction Retweet Using User-Based and Content-Based with ANN-GA Classification Method." Building of Informatics, Technology and Science (BITS) 4, no. 2 (September 21, 2022): 522–28. http://dx.doi.org/10.47065/bits.v4i2.1931.
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Current technological advances have caused rapid dissemination of information, especially on social media, one of which is Twitter. Retweeting or reposting messages is considered an easily available information diffusion mechanism provided by Twitter. By finding out why a user retweets a tweet from another person and by making this prediction we can understand how information diffuses on Twitter. In this study, Artificial Neural Network – Genetic Algorithm is used in the classification process and uses user-based and Content-Based features. Evaluation result obtained in this study are 90% accuracy, 72% precision, 83% recall, and 65% F1-Score value on the model by Oversampling.