rnv.bsky.social
PhD student, University of Copenhagen
NLP, misinformation, media framing, hatespeech, cultural values, CSS, Pol Comm, AI ethics |
he/him.
https://scholar.google.com/citations?user=EQUUUUoAAAAJ&hl=en
25 posts
2,614 followers
1,054 following
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In "Investigating Human Values in Online Communities", we perform a high-scale study of the unique values expressed by online communities with different perspectives
arxiv.org/abs/2402.14177 #NAACL2025 #NLProc
@nadavb.bsky.social @rnv.bsky.social @frimelle.bsky.social @iaugenstein.bsky.social
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C3NLP Workshop #NAACL2025:
@iaugenstein.bsky.social will be presenting on tailoring LLM outputs to cultures, including where implicit cultural personalisation based on names leads to over-simplification arxiv.org/abs/2502.11995
@rnv.bsky.social @frimelle.bsky.social
bsky.app/profile/frim... #NLProc
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The high effort solution is to use an LLM to make a browser extension which tracks your academic reading and logs every paper you interact with to github, which builds and publishes a webapp to expose the data.
Which, clearly only a crazy weirdo would do.
dmarx.github.io/papers-feed/
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Using simple, small models with the goal of usability and scalability of the task, we hope social scientists, journalists and researchers use this as a first step in studying multimodal framing and its intended/unintended effects.
More here:
bsky.app/profile/mari...
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Find other interesting results in our paper: arxiv.org/abs/2503.20960 or play with the dataset yourself: huggingface.co/datasets/cop...
Work done with my amazing colleagues @aicentre.dk
@srishtiy.bsky.social @mariaa.bsky.social @serge.belongie.com @iaugenstein.bsky.social
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Using our method, you can also get issue-specific frames inductively from the article texts. When publishers are compared across the political spectrum, some clear patterns of how the left frames Immigration vs the right.
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Across topics, we find substantial differences in framing across the article and the image. These hold across political leanings as well.
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We collect a dataset of 500k articles and images from various publishers in the US, across the political spectrum and systematically analyse differences in framing across them.
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Editors choose to convey more subtle messaging through images that can evoke a more emotional response. But can this be measured? We demonstrate a methodology using large language and vision models to do such multi-modal analysis reliably & at scale. We use both generic and issue-specific frames!
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Congratulations Christina!
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Congratulations, Amartya!
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Great position!
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Have you seen this?
freeourfeeds.com
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Indeed! I might look into it next week, will report back if I do!
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You're probably familiar with this one, but if not, there's also TopicGPT (github.com/chtmp223/top...)
Would be nice to compare the two
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I'd been meaning to try it when it came out but it completely slipped my mind, thanks for the pointer!