9/ The non-typeset version of the article is available here (no paywall): emilbakke.github.io/files/moment...

8/ We hope our quali-quant based framework, using computational methods combined with actor-network theory, can inspire future research on how momentary symbol construction processes occur in digital collectives.

7/ There is a remarkable visual orientation in momentary symbols to grab attention in the digital realm 🎇

For example, the mask ban imposed in Hong Kong turned masks into a visual symbol that protesters and audiences shared with the international audience on Twitter to gain sympathy.

6/ Momentary symbols are volatile and can both spur and vanish in a matter of days as new events unfold💥

For example, lasers were practical tools to counter surveillance from authorities, until they became categorized as "weapons" after which they were used to visualize collective presence.

5/ Symbol construction is temporally contingent on concurrent evens (e.g., bans and arrests) ⏳

The figure shows spikes in mentions of the three selected objects over the span of the protest period - which often coincide with on-the-ground events in Hong Kong.

4/ We find that the Anti-ELAB 2019 protests highlight the empirical relevance of momentary symbols: objects with practical relevance experienced sudden spikes in symbolic-visual translations, but quickly returned to become practical tools once new symbols surfaced. Thus, we find that:

3/ Using a quali-quant inspired design, we construct actor-network inspired “translation snapshots” based on 1.6M tweets and object detection in ~670k images from the 2019 Anti-ELAB protests in Hong Kong to trace how umbrellas, lasers, and masks oscillate between symbolic and practical roles.

2/ We introduce the notion of "momentary symbols" to capture how objects briefly become dominant symbols for a collective, but just as quickly fade again. This stands in contrast to how the literature usually perceives symbols in collectives: as enduring visual elements over time.

Thank you! I am also very much looking forward to the journey we have ahead of us after tomorrow ✌️

Thanks Fabian!

Thanks Frithjof!

Thanks Curd!

For more analysis and nuance, read our preprint here: arxiv.org/abs/2503.16021

Any comments and feedback are most welcome! Please reach out💬

We also find the AI-generated articles are distinct from the human-written ones – yet are rather similar to each other.

We therefore envision LLMs as “gravity wells" that can, dependening on the initial structure of information, both compress and disperse articles in similarity space.

Furthermore, the effect of AI-driven imitation on news diversity is influenced by both the imitation strategy the agents use and their prevalence in the world.