We could fool ourselves saying that it's because there's no panettone in other periods of the year :P

Partial answer to my question:
osai-index.eu/the-index?ty...

In this paper, we investigate how well media frames generalize across different media landscapes. The 15 MFC frames remain broadly applicable, but requires revisions of the guidelines to adapt to the local context.

More on aclanthology.org/2025.starsem...

@mmitchell.bsky.social

Great, thanks a lot!

As I wasn't at the conference, I'd love to be able to watch the recording. Is it available online anywhere? :)

Great collaboration with Dmitry Nikolaev, @dominsta.bsky.social and @deboranozza.bsky.social ☺️

- Finally, and for me, most interestingly, our analysis suggests that political biases are already encoded during the pre-training stage.

Taken these evidences together, we highlight important implications these results play on data processing in the development of fairer LLMs.

- There's a strong correlation (Pearson r=0.90) between the predominant stances in the training data and the models’ behavior when probed for political bias on eight policy issues (e.g., environmental protection, migration, etc).

- Source domains of pre-training documents differ significantly, with right-leaning content containing twice as many blog posts and left-leaning content 3 times as many news outlets.

- The framing of political topics varies considerably: right-leaning labeled documents prioritize stability, sovereignty, and cautious reform via technology or deregulation, while left-leaning documents emphasize urgent, science-led mobilization for systemic transformation and equity.

- left-leaning documents consistently outnumber right-leaning ones by a factor of 3 to 12 across training datasets.
- pre-training corpora contains about 4 times more politically engaged content than post-training data.

We have the answers of these questions here : arxiv.org/pdf/2509.22367

We analyze the political content of the training data from OLMO2, the largest fully open-source model.
🕵️‍♀️ We run an analysis in all the datasets (2 pre- and 2 post-training) used to train the models. Here are our findings:

Thanks SoftwareCampus for supporting Multiview, the organizers of INRA, and Sourabh Dattawad and @agnesedaff.bsky.social for the great collaboration!

Our evaluation with normative metrics shows that this approach does not diversify only frames in user's history, but also sentiment and news categories. These findings demonstrate that framing acts as a control lever for enhancing normative diversity.

In this paper, we propose introduce media frames as a device for diversifying perspectives in news recommenders. Our results show an improvement in exposure to previously unclicked frames up to 50%.