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Paper Citation Record · LEDGER

Stance Detection on Social Media with Fine-Tuned Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.12171.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2404.12171 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:29:20.946328Z

measured 1 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-10T05:30:23.456663Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

12
pith, observed 2026-08-10T05:30:23.456663Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 2e2d19da-839a-4ec9-9b95-6a8427bbe8bf · inbound

Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models cites this paper.

Rethinking stance detection: A theoretically-informed research agenda for user-level inference using language models Stance Detection on Social Media with Fine-Tuned Large Language Models

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-09T13:29:20.946328Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:29:20.946328Z digest=sha256:ef0a5b1cc4e9a88f6bdba5a0a5b9287ed4ca8317e4996af897669a2d5f274068

Observation 9a0e7d44-c61a-47db-bd7c-2a4da818e148 · inbound

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences cites this paper.

Finding Common Ground: Using Large Language Models to Detect Agreement in Multi-Agent Decision Conferences Stance Detection on Social Media with Fine-Tuned Large Language Models

Reference 34

Resolution
metadata mismatch
local_arxiv, observed 2026-08-06T18:24:55.240708Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:24:53.967301Z digest=sha256:634188d46073182f03ffa9ece5ae26392efbf6ceea578bb5ca2525074dfcfc90