Modelling Individual Behaviour Towards Influencers on Social Media Using Cell-DEVS
Bibliographic record
Abstract
With the exponentially rising popularity of social media, and the provided convenience of digital advertising across various platforms, individuals have found ways to sustain a lifestyle by providing companies a personalised platform for advertising their products.These individuals are popularly labelled as "influencers".Determining the impact an influencer has to a product's market is an important aspect in determining whether a company should invest in the influencer's platform.This thesis research applies and enhances previous approaches of modelling social interactions to simulate the effects of an influencer on its surrounding environment.The "influencer" model employs the Cell-DEVS formalism implemented through the Cadmium tool to simulate the reaction of individuals towards an "influencer".The "influencer" model provides means by which the effects of an influencer can be simulated under various scenarios while combining opinion and social interaction-based approaches to simulating human behaviour.This research presents a model to simulate the evolution of opinions and the resulting events regarding following or not following an influencer as a conclusion of the influenced human behaviour.The model, referred to as the 'influencer' model, is based on the methodologies presented by Behl et al [2], Wang et al [3] and White at al [4], which present simulation strategies for opinion evolution, sentiment propagation, and the spread of COVID-19, respectively.The 'influencer' model uses the Cell-DEVS formalism (an extension of the cellular automata formalism that can be used to build discrete-event cell spaces [43]), Agent-Based modelling, Network diffusion processes, and it is implemented
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".