Do Similar Brands ‘Like’ Each Other? An Investigation of Homophily Among Brands’ Social Networks on Facebook
Bibliographic record
Abstract
The advent of internet and communication technologies enabled marketers of brands to have more ways to communicate with their audience; one of which is connecting with other brands. One of the most popular outlets that allows brands to connect with other brands online is Facebook. Brands on Facebook can establish an official fan page where they can interact with their fans as well as network with other brands’ official Facebook pages through “liking” them. This paper seeks to investigate the “liking” behavior among local and global brands (brand to brand) on Facebook in Saudi Arabia and whether these brands’ “liking” network is based on homophilous relationships. The results showed that both status (e.g., geography and gender), and value (e.g., family ties and religion) homophilous relationships are in play. However, value homophily was a strong factor in brands’ network in Saudi Arabia for some brands in the absence of status homophily network. Although status homophily in general played a role, geographical proximity was not a strong factor compared to previous reports on social network analysis. The data for this study were obtained from 40 brands marketed in Saudi Arabia. Using Netvizz and Gephi, network structures were mapped to explore the relationships among the brand’s’ Facebook pages.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".