Minimizing Social Data Overload through Interest-Based Stream Filtering in a P2P Social Network
Bibliographic record
Abstract
In Online Social Networks (OSNs) users are overwhelmed with the huge amount of social data, most of which are irrelevant to their interest. Filtering of the social data stream is the way to deal with this problem, and it has already been applied by centralized OSNs, such as Facebook. However, it is much harder to filter the social data stream in decentralized OSNs. Decentralized OSNs, mostly based on P2P architectures, such as Diaspora or Friendica, have been proposed as an alternative to the currently dominant centralized OSNs, where people are forced to share their data with the site, and thus lose their control and rights over it. This paper presents an implementation of an interest based stream filtering mechanism using Mad mica - a decentralized OSN, based on the Friendica P2P protocol. The mechanism uses the interaction between users to construct a model of user interests overlaid on the relationships of users with their friends, which acts as a filter later while propagating social data. So Madmica provides a solution to two problems simultaneously - the problem of user privacy and control over their data (through its decentralized architecture) and the problem of social data overload (through its filtering mechanism). We present the results of a pilot study to evaluate the user experience with Madmica.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".