iiinova — where your digital self keeps evolving

Eun Cheol Choi @euncheolchoi.bsky.social · 26/06/2025

Our code is open-source. With just a few lines of Python, anyone can measure AI exposure for any job description using our repository. (5/5) Paper: workshop-proceedings.icwsm.org/abstract.php... Repository: github.com/EunCheolChoi...

Our Python snippet for measuring wAII for a job posting, which is open-sourced.

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Eun Cheol Choi @euncheolchoi.bsky.social · 26/06/2025

𝐖𝐡𝐚𝐭 𝐰𝐞 𝐟𝐨𝐮𝐧𝐝 1. Jobs in tech, manufacturing, and engineering are more exposed to AI; HR, public service, and legal sectors are less exposed 2. Exposure to AI shows distinct association patterns regarding offered salary depending on the industry sectors (4/5)

In the information industry sector, our wAII is “positively” associated with offered salary.

On the other hand, in the wholesale trade industry sector, wAII is “negatively” associated with offered salary.

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Eun Cheol Choi @euncheolchoi.bsky.social · 26/06/2025

To track this, we developed the Weighted AI Index (wAII), a scalable method to measure how closely a job’s tasks align with recent AI innovations. We extract key job tasks from postings; compare them to AI-related patents; compute an “AI exposure” score. (3/5)

A diagram explaining how we extracted ‘tasks’ from each job post. A task is a role or a skillset that is essential for a job position.

We query based on extracted tasks and retrieve the most similar AI-related patents in terms of semantic similarities.

Weighted AI Index is calculated as the sum of the product between the task weight and the similarity of the task and the retrieved patent.

Full LLM prompt for extracting tasks from a job post.

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Eun Cheol Choi @euncheolchoi.bsky.social · 26/06/2025

𝐊𝐞𝐲 𝐢𝐝𝐞𝐚 AI doesn't affect all jobs equally. Some industries and roles are far more exposed to disruption from technological innovation than others. (2/5)

A bar chart comparing ‘weighted AI index (wAII)’ of different industry sectors. wAII is our proposed method of measuring how much a job position is exposed to AI technologies.

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Eun Cheol Choi @euncheolchoi.bsky.social · 26/06/2025

𝑾𝒊𝒍𝒍 𝑨𝑰 𝒕𝒂𝒌𝒆 𝒎𝒚 𝒋𝒐𝒃? It’s a question that keeps many of us up at night—and for good reason. Our new research maps labor market vulnerability in the age of AI with 100K job postings and 50K AI-related patents. (1/5)

Title page for our new paper, “Mapping Labor Market Vulnerability in the Age of AI”

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Reposted by Eun Cheol Choi

Luca Luceri @luceriluc.bsky.social · 01/05/2025

"Limited effectiveness of LLM-based data augmentation for COVID-19 misinformation stance detection" by @euncheolchoi.bsky.social @emilioferrara.bsky.social et al, presented by the awesome Chur at The Web Conference 2025 arxiv.org/abs/2503.02328

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Reposted by Eun Cheol Choi

Manoel Horta Ribeiro @manoelhortaribeiro.bsky.social · 28/11/2024

Hot takes: - The benefits of easily accessible social media data usually outweigh the potential harms. - Some uses of (public) social media data are unethical/should be illegal, and we should target that. - We're better off having clear boundaries between public and private online spaces.

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Reposted by Eun Cheol Choi

Jason Koebler @jasonkoebler.bsky.social · 27/11/2024

An employee of Huggingface, a site of AI training datasets, made a dataset of a million Bluesky posts scraped simply because they could. It’s currently trending: www.404media.co/someone-made...

404media.co

Someone Made a Dataset of One Million Bluesky Posts for 'Machine Learning Research'

A Hugging Face employee made a huge dataset of Bluesky posts, and it’s already very popular.

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Reposted by Eun Cheol Choi

Jay 🦋 @jay.bsky.team · 23/11/2024

Bluesky's firehose is a treasure trove of public data for researchers and developers, and it's completely free. Check out our developer docs: docs.bsky.app

docs.bsky.app

Bluesky Documentation | Bluesky

Explore guides and tutorials to the Bluesky API.

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Reposted by Eun Cheol Choi

Christoph Molnar @christophmolnar.bsky.social · 22/11/2024

Just realized BlueSky allows sharing valuable stuff cause it doesn't punish links. 🤩 Let's start with "What are embeddings" by @vickiboykis.com The book is a great summary of embeddings, from history to modern approaches. The best part: it's free. Link: vickiboykis.com/what_are_emb...

Over the past decade, embeddings — numerical representations of
machine learning features used as input to deep learning models — have
become a foundational data structure in industrial machine learning
systems. TF-IDF, PCA, and one-hot encoding have always been key tools
in machine learning systems as ways to compress and make sense of
large amounts of textual data. However, traditional approaches were
limited in the amount of context they could reason about with increasing
amounts of data. As the volume, velocity, and variety of data captured
by modern applications has exploded, creating approaches specifically
tailored to scale has become increasingly important.
Google’s Word2Vec paper made an important step in moving from
simple statistical representations to semantic meaning of words. The
subsequent rise of the Transformer architecture and transfer learning, as
well as the latest surge in generative methods has enabled the growth
of embeddings as a foundational machine learning data structure. This
survey paper aims to provide a deep dive into what embeddings are,
their history, and usage patterns in industry.

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Reposted by Eun Cheol Choi

David Rothschild @davmicrot.bsky.social · 22/11/2024

New Paper on Opportunities and risks of LLMs in survey research papers.ssrn.com/sol3/papers.... " Backed by both practical examples & academic literature, we identify areas for research and development, distinguishing between challenges related to survey methods & the tools used to deploy surveys"

papers.ssrn.com

Opportunities and risks of LLMs in survey research

Recent advances in the development of large language models (LLMs) bring both disruptive opportunities and underlying risks to survey research. LLMs' capabiliti

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Reposted by Eun Cheol Choi

Daniel Vila @dvilasuero.hf.co · 21/11/2024

Interested in RLHF, DPO, LLM alignment? I've just created this list featuring awesome people like @natolambert.bsky.social . The list is the opposite of exhaustive; I've just joined some days ago 😅 go.bsky.app/MqRGAf2

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Reposted by Eun Cheol Choi

Shayan Sardarizadeh @shayan86.bsky.social · 14/11/2024

If you're keen to learn content verification for fact-checking and open source investigations, this is a step-by-step guide on how to verify images and videos that I posted a while ago. Once you familiarise yourself with reverse search, you'll become much better at spotting online misinformation.

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Reposted by Eun Cheol Choi

Emilio Ferrara @emilioferrara.bsky.social · 13/11/2024

Sharing my first Computational Social Science starter pack! Will grow with time, feel free to nominate and self nominate! go.bsky.app/CYmRvcK

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Reposted by Eun Cheol Choi

Brian C. Keegan, Ph.D. @brianckeegan.com · 14/11/2024

Here is a @github.com repo where I will share tutorial notebooks on how to retrieve and analyze data from @bsky.app @atproto.com Feedback, suggestions, and contributions are welcome! github.com/brianckeegan...

github.com

GitHub - brianckeegan/bluesky-datascience: Exploratory notebooks for data science using Bluesky data

Exploratory notebooks for data science using Bluesky data - brianckeegan/bluesky-datascience

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Reposted by Eun Cheol Choi

Emilio Ferrara @emilioferrara.bsky.social · 14/11/2024

Ready for another Computational Social Science Starter Pack? Here is number 2! More amazing folks to follow! Many students and the next gen represented! go.bsky.app/GoEyD7d

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