A Scalable Approach for Strengthening Social Media Ties Using Multi-Dimensional Analysis
Bibliographic record
Abstract
The assessment of relationship strength among interconnected users in online social networks remains a critical focal point in contemporary research.Despite a multitude of studies on strong tie identification, this constitutes an enduring challenge.In this work, a novel method is introduced that amalgamates factors such as user profile information, communication frequency, and network composition to ascertain the strength of ties among social media users.The proposed method encompasses computations involving three distinct variables: Analogy Profile (AP), Analogy Friendship (AF), and Analogy Reaction (AR).These variables collectively contribute to determining the overall quality of user relationships.Pearson's correlation, serving as AP, aids in identifying and quantifying user correlations' strength and orientation.Jaccard's coefficient offers a measure of user similarity, hence its use as AF.Lastly, the User Interconnection Potency Level, serving as AR, provides insights into user interaction dynamics and behaviour.For the purpose of experimental validation, ten different real-time social networks were considered.The performance of the proposed method was evaluated using Precision, Recall, and the Dice Similarity Coefficient (DSC) as evaluation matrices, on ten distinct real-world online social media datasets.Comparative analysis with two state-of-the-art methods, namely Trust Propagation-User Relationship Strength (TP-URS) and User Relationship Strength Fusing Multiple Factors (URSMF), demonstrated superior performance of our method.It achieved top scores of 92%, 98%, and 95% for Precision, Recall, and DSC, respectively.Overall, the proposed method outperforms TP-URS and URSMF in estimating relationship strengths among social media network users.These results underscore the utility of incorporating factors like profile information, communication patterns, and network composition when measuring tie strengths.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".