Paper Citation Record · LEDGER

Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:1708.00524.

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

pith.paper-citation-record.v1
1708.00524 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T12:55:53.855306Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-25T09:05:36.014359Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 829c85ae-d4b8-4fe1-ab96-f535ef5338e6 · inbound

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study cites this paper.

Transfer Learning for Risk Classification of Social Media Posts: Model Evaluation Study Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-25T09:05:36.017693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5629356e-5f2e-453e-b5ff-4aad51e8bc6d · inbound

Towards Understanding Emotional Intelligence for Behavior Change Chatbots cites this paper.

Towards Understanding Emotional Intelligence for Behavior Change Chatbots Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-24T17:44:45.815842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation a9c48006-dfa2-4ea5-a498-1dbd496a9051 · inbound

EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation cites this paper.

EmotionX-IDEA: Emotion BERT -- an Affectional Model for Conversation Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 1987

Resolution: unresolved; no resolver link, observed 2026-08-14T12:55:53.855306Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2479367b-e63b-4210-832e-cb4da4d54792 · inbound

Evaluating LLMs Capabilities Towards Understanding Social Dynamics cites this paper.

Evaluating LLMs Capabilities Towards Understanding Social Dynamics Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T16:59:41.550449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb1c835a-2ae3-4b57-a26b-0562a01e79c5 · inbound

RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media cites this paper.

RedNote-Vibe: A Dataset for Capturing Temporal Dynamics of AI-Generated Text in Lifestyle Social Media Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-18T13:51:26.104777Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 090a64ed-bc2d-4cbf-ac22-c45a645b04e9 · inbound

A Hormone-inspired Emotion Layer for Transformer language models (HELT) cites this paper.

A Hormone-inspired Emotion Layer for Transformer language models (HELT) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:35:14.615000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.