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Collaborative knowledge building achieves better results than individual knowledge building essentially due to the triggering phenomenon taking place among the users in a collaborative setting. Although the literature points to a few theories supporting the existence of this phenomenon, yet these theories have never been validated in real collaborative environments, thus questioning their general prevalence. In this work, we provide a mechanized way to observe the presence of triggering in knowledge building environments. We implement the method on the most-edited articles of Wikipedia and show that it may help in discerning how the existing knowledge leads to the inclusion of more knowledge in these articles. The proposed technique may further be used in other collaborative knowledge building settings as well. The insights obtained from the study will help the portal designers in building portals enabling optimal triggering.
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Stored only when both papers are in the corpus — 12 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.