How to Make the Information Dissemination Process More Effective: Studies based on Simulation Methods
Bibliographic record
Abstract
The article deals with issues related to the definition of parameters that affect the process of information dissemination in social media.Directed or bidirectional links are considered as parameters.The study of the propagation process is carried out using simulation methods.AnyLogic was chosen as a tool for conducting simulation experiments.Keywords-Social media, directed and not directed links, the degree of influence. I. INTRODUCTIONSocial media are web platforms and applications that allow to interact with the content of various types (text, photos, video and audio) and provide the user with the opportunity to interact with other users, rate and comment on content, and distribute it through likes, reposts and messages.In Russia, among the popular social media, the most popular is Vkontakte (VK).VK is a platform on which 9 out of 10 Russian users are registered.Another popular platform is the Odnoklassniki platform (7 out of 10 users).VK and Odnoklassniki allow users not only to communicate with each other, but also to comment on posts, organize events, etc., i.e. they are a channel for the dissemination of information, including the distribution of advertising products, services, materials for training, etc. [1].The fast and reliable distribution of advertising is, without a doubt, beneficial for businesses.There is another side of this process -the dissemination of unwanted information.One way or another, the study of the process of dissemination of information, the parameters that influenced this process, or, conversely, the containment of information is relevant and important [2].Simulation modeling methods were chosen for studying and conducting research, and the AnyLogic[3] simulation modeling system was used as a tool.Unlike the methods used in SNA (Social Network Analyzes), which mainly study the structural characteristics of social media, simulation methods allow us to consider the process in dynamics, to identify the causes that can cause certain events.There are various approaches to conducting simulation experiments.The object for these experiments can be either real or virtual social networks [4,5].This article deals with virtual social networks, i.e., models of real social networks, and more precisely, the Barabasi-Alberta model [6,7,8].Further, the article will be structured as follows: we will review similar works, consider a mathematical model of social media, information dissemination models, the main metrics that are used to assess the speed of information dissemination, and, finally, present the results of simulation experiments.II.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".