WCN25-4269 SOCIAL MEDIA ACTIVITIES OF INTERNATIONAL SOCIETY OF NEPHROLOGY -CAPTIVATING NEPHROLOGY NETIZENS
Bibliographic record
Abstract
this hashtag we analyzed that there were around 80 engagements on the tweets with a reach of 13186 and 16.184 impressions.Pre congress workshop on genetics and kidney disease had 108 views, day 1 conference had 346 views, day 2 conference had 727 views on YouTube.#WINICON 2023was official hashtag for second annual conference and welcome video was a huge hit, bragging 240 views which absolutely showed a huge positive impact on the delegates attending the conference.Preconference interviews with esteemed international faculty was another crowd puller as reflected in around 715 views and 22 likes on you tube.Day 1 of the conference had 379 views, day 2 had 194 views on you tube .On twitter #WINICON 2023had 119 engagements with 46,215 reach and 87,037 impressions.#WINICON 2024social media coverage showed the total number of posts on X were 627 with approximately 100 users.All these cumulated to 12,90,927 impressions and reach of 1,81,009.A total of 79 posts were shared on Facebook.This included posts from the official Win icon account, WIN-India account and personal accounts of the official #SoMe members.The posts garnered 1718 likes, 61 shares, 142 comments and 8006 video plays.In contrast, engagements this time on YouTube were relatively lower.A total of 12 videos were posted which included 6 pre-conference videos, 4 welcome videos, 1 video of the press meet and 1 video of 'welcome to WCN 25'.The total views were 607 with 21 likes.Five posts were shared on LinkedIn, which garnered 59 likes, 1 comment, 3 reposts and 2338 impressions.Conclusions: We saw a steep rise in the social media coverage from 2022 to 2024 and its impact on attendance to the conference Twitter and YouTube showed the highest impact on the conference attendance by facilitating the interaction with previously unknown people, opportunity for robust dialogue, and the ease on visual graphics and photos.The formation of a dedicated social media committee is an useful strategy that can provide substantial benefits.I have no potential conflict of interest to disclose.I did not use generative AI and AI-assisted technologies in the writing process.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.124 | 0.045 |
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".