Towards Storytelling by Extracting Social Information from OSN Photo's Metadata
Bibliographic record
Abstract
The popularity of online social networks (OSNs) is growing rapidly over time. People share their experiences with their friends and relatives with the help of multimedia such as image, video, text, etc. The amount of such shared multimedia is also growing likewise. The large amount of multimedia data on OSNs contains in it a snapshot of user's life. This social network data can be crawled to build stories about individuals. However, the information needed for a story, such as events and pictures, is not fully available on user's own profile. While part of this information can be retrieved from user's own timeline, a large amount of event and multimedia information is only available on friend's profiles. As the number of friends can be very large, in this work we focus on identifying subset of friends for enriching the story data. In this paper we explore social relationships from multimedia perspective and propose a framework to build stories using information from multiple-profiles. To the best of our knowledge, this is the first work on building stories using multiple OSN profiles. The experimental results show that with the proposed method we get more information (events, locations, and photos) about the individuals in comparison to the traditional methods that rely on user's own profile alone.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".