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Lisette Espín-Noboa

Lisette Espín-Noboa Add us as a preferred source on Google

Mastodon @lespin@datasci.social
⚔ Compare ☆ Follow Own this channel?
🔄 Data last refreshed 6 days ago
Followers
20
Account age
3 yrs
🎁 Free analysisfor Lisette Espín-Noboa
🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

📊 Activity & format

Posting cadence
0.23 / week
A lower-frequency account — each post lands with more weight.
Content mix
Mostly images
Recent: 0 text · 12 image · 0 video.
Follower / following
0.7×
Follows 28 back. A more reciprocal / networked account.

Recent posts

View on Mastodon ↗
A lot of opportunities come through the people we know. But when connections & network position shape who gets rewarded, what counts as fair and when? In our new preprint on network fairness, we identify 10 network effects that can shape … A trilogy mapping how LLMs surface or overlook scholars: - I (6 LLMs): https://arxiv.org/abs/2506.00074 - II (22 LLMs) extended: https://arxiv.org/abs/2602.08873 - III (44 LLMs): https://arxiv.org/abs/2605.28187 - Tool and leaderboard: ht… The "Beyond Scaling" panel raised a lot of questions: limits of scaling, structure vs. more data, rethinking evaluation, and trust in high-stakes settings. 5/6 Wei Wang received the SIGKDD Innovation Award with a keynote spanning two decades from knowledge discovery to agentic scientific discovery. 4/6 #XGBoost won the SIGKDD Test of Time Award, 10 years after the original KDD 2016 paper. Tianqi Chen's talk was a nice reflection on how iterative error correction and crossa-platform design turned it into the standard for tabular ML. Well … We introduce #LLMScholarBench: an evaluation benchmark auditing 22 LLMs as people recommenders. Each end-user intervention improves some result characteristics but never all at once. Accuracy vs. fair representation is still an open trade-… It's been a few weeks since #KDD2026 in Jeju and it was a lot to take in. First KDD, first time in South Korea, ~4K attendees. I presented our work on auditing LLMs as scholar recommender systems. 🧵 1/6 This week I presented three pieces of our work at @icssi.org: 1. #LLMScholar a trilogy of LLM audits https://bit.ly/ICSSI26_LLMScholar_poster 2. Parenthood in Academia in Europe https://bit.ly/ParenthoodEuropeICSSI26 3. Department size gro… 📢 Last call for abstracts! Submit your work to the DACH-CSS Conference 2026, May 21–22 in Vienna, hosted by CSH & CEU. Accepted abstracts: 3-minute talk + poster session. 🗓 Deadline: TODAY (Feb 20) 📝 Registration open Details: https://comp… ⏰ Reviewers, today’s the day! Please upload your reviews by the end of day (Oct 24) AoE 🙏 Thanks for all your work! Authors: we plan to send out notifications by Oct 31. #NetSciX #NetSciX2026 @MLuczak @ProfMSmall @droneale @danielapaolotti… Huge thanks to @LiuhuayingYang & @CSHVienna for creating such a creative space every year. 👏👏👏 If you want to explore how science & data can be translated into playful, pedagogical, and artistic forms, apply for the next edition in 2026! … or our work on ranking & recsys audits (http://doi.org/10.1038/s41598-022-05434-1): - SHROOM BOOM: a board game [http://visualizing-complexity-science-group4.netlify.app] - Hidden Network: interactive tool + exhibit - Network Sandbox: play…

🐘 Community & instance

Home server
datasci.social
Their home server on the fediverse — the instance a creator picks signals the community they belong to.
On Mastodon since
Jan 2023
Joined in the Twitter-exodus wave of late 2022 — part of the migration that made Mastodon a real destination.

💡 Facts

🗓️Joined Mastodon in 2023 — 3 years ago.
📤Posts about 0.2× per week.

🕵️ Fake follower check

Estimated
55/100
Fair Credibility score
~80%
Real Real audience
Low Fake-follower risk
Low Data confidence
  • Est. 80% real, active audience · Low fake-follower risk.
  • Limited engagement data — score uses the signals available.
  • Established account (3+ years old).

Low confidence — few signals are public for this listing, so treat it as a rough screen. Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

Check my bibliometrics: https://bit.ly/lespin-dashboard Visiting Professor at Graz University of Technology (TUG) PostDoc at Complexity Science Hub Vienna (CSH) PostDoc at Central European University (CEU) @tugraz @CSHVienna @NetInCSH @ceu @dnds_ceu #NetworkFairness #NetworkInequality #NetworkScience #DataScience #ComputationalSocialScience Previously at @GESIS_org @unikold @mpi_sws @Saar_Uni @espol_fiec @neoBOXec

📸 Gallery

A lot of opportunities come through the people we know. But when connections & network position shape who gets rewarded, what counts as fair and when? In our new preprint on network fairness, we identify 10 network effects that can shape human and algorithmic decisions: https://arxiv.org/abs/2609.13867 A trilogy mapping how LLMs surface or overlook scholars: - I (6 LLMs): https://arxiv.org/abs/2506.00074 - II (22 LLMs) extended: https://arxiv.org/abs/2602.08873 - III (44 LLMs): https://arxiv.org/abs/2605.28187 - Tool and leaderboard: https://vis.csh.ac.at/whosenamecomesup 6/6 The "Beyond Scaling" panel raised a lot of questions: limits of scaling, structure vs. more data, rethinking evaluation, and trust in high-stakes settings. 5/6 Wei Wang received the SIGKDD Innovation Award with a keynote spanning two decades from knowledge discovery to agentic scientific discovery. 4/6 #XGBoost won the SIGKDD Test of Time Award, 10 years after the original KDD 2016 paper. Tianqi Chen's talk was a nice reflection on how iterative error correction and crossa-platform design turned it into the standard for tabular ML. Well deserved. 3/6 We introduce #LLMScholarBench: an evaluation benchmark auditing 22 LLMs as people recommenders. Each end-user intervention improves some result characteristics but never all at once. Accuracy vs. fair representation is still an open trade-off. https://dl.acm.org/doi/10.1145/3770855.3817543 2/6 It's been a few weeks since #KDD2026 in Jeju and it was a lot to take in. First KDD, first time in South Korea, ~4K attendees. I presented our work on auditing LLMs as scholar recommender systems. 🧵 1/6 This week I presented three pieces of our work at @icssi.org: 1. #LLMScholar a trilogy of LLM audits https://bit.ly/ICSSI26_LLMScholar_poster 2. Parenthood in Academia in Europe https://bit.ly/ParenthoodEuropeICSSI26 3. Department size growth dynamics https://bit.ly/DepSizeICSSI26 If you want to know more about them please reach out. #ComplexityScienceHub @NetInCSH @ScienceOfScience #SciSci

🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

Philosophical Fish Philosophical Fish 16% Jason (Spooky Version) Jason (Spooky Version) 15% Detroit Lions Podcast Detroit Lions Podcast 13% Observer1 Observer1 12% ZombieHotflash ZombieHotflash 12% Tim Rutherford Tim Rutherford 10%

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ZombieHotflash
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Jason (Spooky Version)
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Tim Rutherford
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Lisette Espín-Noboa has 20 followers on Mastodon.

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