Pith. sign in

Paper Citation Record · LEDGER

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection

As of 10 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2512.20670.

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

pith.paper-citation-record.v1
2512.20670 v2

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-16T21:00:09.342056Z

measured 20 of 20 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

  • verified exact7
  • verified fuzzy13
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d15a0b53-13f0-49d6-a37e-b59d3e1dd881 · outbound

This paper cites Bootstrapping multi-view representations for fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Bootstrapping multi-view representations for fake news detection

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.952149Z

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=2026-05-16T21:00:09.342056Z digest=sha256:96276855c026c9e3806d36d73903deb1a9d7cf4d351bd7017f68b84953a6133b

Observation 920ed284-12dd-4171-9f40-af0b52e5f8c3 · outbound

This paper cites SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection SEER: Semantic Enhancement and Emotional Reasoning Network for Multimodal Fake News Detection

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.161079Z

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=2026-05-16T21:00:09.342056Z digest=sha256:6be700a8b3bf3c580f7aab0b5bbfd78881a85d7d01e29545500f3002d561ce2d

Observation 679f3341-49cc-4f07-b4d0-db30a37052aa · outbound

This paper cites Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Bridging Thoughts and Words: Graph-Based Intent-Semantic Joint Learning for Fake News Detection

Reference 3

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.165763Z

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=2026-05-16T21:00:09.342056Z digest=sha256:5a0b97f0a8598b0b91feba51abee4af9522cb9c7316c7bd231b126ae0e74033c

Observation 905cb656-960f-4fe5-9b2a-5f77ab59604f · outbound

This paper cites Prompt- induced linguistic fingerprints for llm-generated fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Prompt- induced linguistic fingerprints for llm-generated fake news detection

Reference 4

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.155945Z

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=2026-05-16T21:00:09.342056Z digest=sha256:923383681cbdb33da2bb3a14bff422e35c6b2205b190ca8c1f07bb11cab0ea00

Observation f8ede4b1-30e2-4d34-bc04-3086c91a93f6 · outbound

This paper cites Tension-field theory.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Tension-field theory

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.949243Z

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=2026-05-16T21:00:09.342056Z digest=sha256:1d2fea70167e16004c8d84fc9583a24a9c3df480274a40cb5a9e5889a68fdabc

Observation 2edfb621-3bfa-448b-940a-12451d56753b · outbound

This paper cites You only look once: Unified, real-time object detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection You only look once: Unified, real-time object detection

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.925223Z

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=2026-05-16T21:00:09.342056Z digest=sha256:9146e95c24fdf6eb1ddc2593af936acff48b18cff09b2f3d1c8352acbd5688f4

Observation bcfdb5e3-6ca7-452f-bca6-31a7ba8fd3f0 · outbound

This paper cites Senticnet 7: A commonsense-based neurosymbolic ai frame- work for explainable sentiment analysis.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Senticnet 7: A commonsense-based neurosymbolic ai frame- work for explainable sentiment analysis

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.927734Z

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=2026-05-16T21:00:09.342056Z digest=sha256:2e46eeee51b268a31b147e347c0ee69b2f46e5663296055cb3b12eb913ea492f

Observation 7c52b07d-4593-4ee9-9d4b-02340d653d75 · outbound

This paper cites Spotfake: A multi-modal framework for fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Spotfake: A multi-modal framework for fake news detection

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.930225Z

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=2026-05-16T21:00:09.342056Z digest=sha256:7ea93c3abb6c48927934072b0aa7bdb3183327849baa40ca10c09335607af1e6

Observation 78c41225-1812-4dec-b508-57b5c531f70e · outbound

This paper cites Cross- modal ambiguity learning for multimodal fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Cross- modal ambiguity learning for multimodal fake news detection

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.932850Z

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=2026-05-16T21:00:09.342056Z digest=sha256:62dcad8c3e4dc7b1ee8ce7c8b4b9c6e3fac941bf19e4830291c5ff358e10ae56

Observation efe4e9e0-a8d6-4d31-b106-8632bd7b5939 · outbound

This paper cites Mvan: Multi-view attention networks for fake news detection on social media.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Mvan: Multi-view attention networks for fake news detection on social media

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.938047Z

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=2026-05-16T21:00:09.342056Z digest=sha256:eb9ca449855b8dde928d3242e13ab657f9ac1772c8de643180fe5a01f3ed0cf8

Observation 43d6bb99-c385-4046-927e-971f0abc24b4 · outbound

This paper cites Eann: Event adversarial neural networks for multi-modal fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Eann: Event adversarial neural networks for multi-modal fake news detection

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.940439Z

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=2026-05-16T21:00:09.342056Z digest=sha256:d56fcdeb0ef4378c47c57466a2da5975f1eef25b53bd5a7df5aa5a3b4e20f67b

Observation 60cbb533-a99f-46d2-8655-7c475b22ca9a · outbound

This paper cites Multimodal fake news detection via clip-guided learning.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Multimodal fake news detection via clip-guided learning

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.943096Z

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=2026-05-16T21:00:09.342056Z digest=sha256:83e0176bc090cbbc25d9c4a7244ee4d3a6b43fcd829ca787c42b6debde29c182

Observation 6581da78-c3e9-40af-aac5-6da8b06c8002 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.918023Z

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=2026-05-16T21:00:09.342056Z digest=sha256:3c4d167323b90b453e8c68017f4e5d4c1804906354a3d9eef3a02ab05e734ab6

Observation 58682c92-f32f-4c90-9313-ae5410bf214f · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection An image is worth 16x16 words: Transformers for image recognition at scale

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.920343Z

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=2026-05-16T21:00:09.342056Z digest=sha256:6ffd4ee4e2901edea9b279b19c81fd59a98cddc72e07459a7dfe1d01615059a8

Observation 32e9bdab-dab8-423e-affd-77670e0c540b · outbound

This paper cites SAFE: Similarity-Aware Multi-Modal Fake News Detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection SAFE: Similarity-Aware Multi-Modal Fake News Detection

Reference 15

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.136171Z

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=2026-05-16T21:00:09.342056Z digest=sha256:0af4f3a0d97ac4707cc877636f60d7a663b0ec3d1a4d5cb05f6140a75c39cebf

Observation 65bb5995-37a7-4d68-aec9-b0b97a1b3c0b · outbound

This paper cites Modality interactive mixture-of-experts for fake news detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Modality interactive mixture-of-experts for fake news detection

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.922827Z

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=2026-05-16T21:00:09.342056Z digest=sha256:2dd605a7520868af19bd04d9127a08b8ebfbe763170c9b7c7b9300539fa43e3d

Observation 0552a05f-2620-445c-8763-80a0559b5ad5 · outbound

This paper cites KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection KEN: Knowledge Augmentation and Emotion Guidance Network for Multimodal Fake News Detection

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.145456Z

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=2026-05-16T21:00:09.342056Z digest=sha256:dedabfa680f5a3f5ee98c7fb45eb7bc3aff691a05bc433d0ff59dbdda2614057

Observation 029adf76-8f91-4d50-95f8-e71a763a43f0 · outbound

This paper cites Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Synergizing LLMs with Global Label Propagation for Multimodal Fake News Detection

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.150989Z

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=2026-05-16T21:00:09.342056Z digest=sha256:9acee4256c5a82f35bcd9522214ab9e53cbac92dad38c281e5de572d8356a125

Observation 7ea73048-3b1a-4d7a-89ec-82e06826a594 · outbound

This paper cites Masked autoencoders are scalable vision learners.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Masked autoencoders are scalable vision learners

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-05-16T21:01:16.935375Z

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=2026-05-16T21:00:09.342056Z digest=sha256:a44719c20ba61e40782107ea74d7017f38edfe297c46cd1db77b31e045d4bcd0

Observation aa561e56-aabf-4b11-825c-c256c14093d0 · outbound

This paper cites Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese.

Disentangling Fact from Sentiment: A Dynamic Conflict-Consensus Framework for Multimodal Fake News Detection Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-16T21:01:16.140672Z

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=2026-05-16T21:00:09.342056Z digest=sha256:7fb03266e619874c310dcf8b573c0e7b9102d16afba9e945e05ace4e4645a001

Pith citing papers

No inbound Pith citation observations are available.