Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification | Paper Citation Record

Observation 7b5e16ea-1c79-4375-bc14-3039da35537f · outbound

This paper cites Autores Cuellar, Jaime E.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Autores Cuellar, Jaime E

Reference 1

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This paper cites En este sentido, se buscó poner a prueba la capacidad del modelo para interpretar correctamente el mensaje subyacente, con formas lingüísticas desestructuradas.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification En este sentido, se buscó poner a prueba la capacidad del modelo para interpretar correctamente el mensaje subyacente, con formas lingüísticas desestructuradas

Reference 2

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This paper cites (2024) emplearon el análisis de sentimientos como base para desarrollar una métrica del impacto de los rumores en redes sociales en términos de daño.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification (2024) emplearon el análisis de sentimientos como base para desarrollar una métrica del impacto de los rumores en redes sociales en términos de daño

Reference 4

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This paper cites (País) + (nombre del Presidente) + ‘presidente’.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification (País) + (nombre del Presidente) + ‘presidente’

Reference 6

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This paper cites El código utilizado fue construido a partir de la documentación de OpenAI para realizar análisis de sentimientos (Guzman 2024; OpenAI 2023a).

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification El código utilizado fue construido a partir de la documentación de OpenAI para realizar análisis de sentimientos (Guzman 2024; OpenAI 2023a)

Reference 8

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This paper cites Sin embargo, su contraposición en el grupo B, los prompts 2 y 8, que son el otro prompt de referencia y su variación modal respectivamente, no tienen tal cercanía.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Sin embargo, su contraposición en el grupo B, los prompts 2 y 8, que son el otro prompt de referencia y su variación modal respectivamente, no tienen tal cercanía

Reference 10

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This paper cites Lo anterior proporciona un marco inicial para conocer, contrastar y analizar los resultados de la prueba de Chi-cuadrado.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Lo anterior proporciona un marco inicial para conocer, contrastar y analizar los resultados de la prueba de Chi-cuadrado

Reference 11

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This paper cites También, se observa que los prompts desestructurados, sin palabras gramaticales ni puntuación, no fueron los que mayor difirieron con el resto, como pasó en el análisis anterior.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification También, se observa que los prompts desestructurados, sin palabras gramaticales ni puntuación, no fueron los que mayor difirieron con el resto, como pasó en el análisis anterior

Reference 13

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This paper cites Otro hallazgo está en la robustez del LLM.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Otro hallazgo está en la robustez del LLM

Reference 14

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This paper cites Nunca vi a el karma de actuar de manera tan instantánea.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Nunca vi a el karma de actuar de manera tan instantánea

Reference 16

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This paper cites ChatGPT and the Future of Medical Writing.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification ChatGPT and the Future of Medical Writing

Reference 17

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This paper cites RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification RAmBLA: A Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain

Reference 18

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This paper cites Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Machine Learning Techniques for Sentiment Analysis of COVID-19-Related Twitter Data

Reference 19

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This paper cites Language models are few-shot learners.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Language models are few-shot learners

Reference 20

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This paper cites The Nexus between Information Disorder and Terrorism: A Mix of Machine Learning Approach and Content Analysis on 39 Terror Attacks.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification The Nexus between Information Disorder and Terrorism: A Mix of Machine Learning Approach and Content Analysis on 39 Terror Attacks

Reference 21

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This paper cites New York: Routledge.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification New York: Routledge

Reference 24

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This paper cites Commercial Sentiment Analysis Solutions: A Comparative Study.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Commercial Sentiment Analysis Solutions: A Comparative Study

Reference 25

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This paper cites ACOSO, SOLEDAD Y DESPRESTIGIO: Un estudio sobre las formas, las rutas de atención y el impacto de las violencias digitales contra las candidatas al Congreso colombiano en 2022.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification ACOSO, SOLEDAD Y DESPRESTIGIO: Un estudio sobre las formas, las rutas de atención y el impacto de las violencias digitales contra las candidatas al Congreso colombiano en 2022

Reference 26

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This paper cites Prompt Engineering with ChatGPT: A Guide for Academic Writers.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Prompt Engineering with ChatGPT: A Guide for Academic Writers

Reference 27

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This paper cites TrustLLM: Trustworthiness in Large Language Models.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification TrustLLM: Trustworthiness in Large Language Models

Reference 31

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification A brief history of APIs

Reference 32

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This paper cites Analyzing European Migrant-Related Twitter Deliberations.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Analyzing European Migrant-Related Twitter Deliberations

Reference 33

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This paper cites Exploring ChatGPT Capabilities and Limitations: A Survey.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Exploring ChatGPT Capabilities and Limitations: A Survey

Reference 34

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification The language of prompting: What linguistic properties make a prompt successful?

Reference 36

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Vasarhelyi

Reference 37

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This paper cites Extracting financial data from unstructured sources: leveraging large language models.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Extracting financial data from unstructured sources: leveraging large language models

Reference 38

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Generated Knowledge Prompting for Commonsense Reasoning

Reference 39

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Cutting Down on Prompts and Parameters: Simple Few-Shot Learning with Language Models

Reference 40

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Large Language Model Guided Tree-of-Thought

Reference 41

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This paper cites Sentiment Analysis Using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Sentiment Analysis Using Dictionary-Based Lexicon Approach: Analysis on the Opinion of Indian Community for the Topic of Cryptocurrency

Reference 42

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Unresolved cited work

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Reliability Issues of LLMs: ChatGPT a Case Study

Reference 44

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This paper cites Where Did the News Come From? Detection of News Agency Releases in Historical Newspapers.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Where Did the News Come From? Detection of News Agency Releases in Historical Newspapers

Reference 45

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Toxic Bias: Perspective API Misreads German as More Toxic

Reference 46

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This paper cites From Twitter to Aso-Rock: A Sentiment Analysis Framework for Understanding Nigeria 2023 Presidential Election.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification From Twitter to Aso-Rock: A Sentiment Analysis Framework for Understanding Nigeria 2023 Presidential Election

Reference 47

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Negligent Algorithmic Discrimination

Reference 48

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification A Computational Look at Oral History Archives

Reference 49

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Prompt Programming for Large Language Models: Beyond the Few-Shot Paradigm

Reference 50

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This paper cites 33 Rolin, Kristina.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification 33 Rolin, Kristina

Reference 51

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Trust in artificial agents

Reference 52

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This paper cites A perfect X-ray beam splitter and its applications to time-domain interferometry and quantum optics exploiting free-electron lasers.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification A perfect X-ray beam splitter and its applications to time-domain interferometry and quantum optics exploiting free-electron lasers

Reference 53

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This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 55

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Finetuned Language Models Are Zero-Shot Learners

Reference 56

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Sentiment Analysis Using Deep Learning Architectures: A Review

Reference 57

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Why Johnny Can’t Prompt: How Non-AI Experts Try (and Fail) to Design LLM Prompts

Reference 58

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This paper cites https://doi.org/10.1145/3544548.3581388 Zanotti, Giacomo, Mattia Petrolo, Daniele Chiffi, and Viola Schiaffonati.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification https://doi.org/10.1145/3544548.3581388 Zanotti, Giacomo, Mattia Petrolo, Daniele Chiffi, and Viola Schiaffonati

Reference 59

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This paper cites Keep Trusting! A Plea for the Notion of Trustworthy AI.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Keep Trusting! A Plea for the Notion of Trustworthy AI

Reference 60

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This paper cites Sentiment Analysis in the Era of Large Language Models: A Reality Check.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Sentiment Analysis in the Era of Large Language Models: A Reality Check

Reference 61

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Meta Prompting for AI Systems

Reference 62

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Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Unresolved cited work

Reference 68

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

Observation c3b9e73f-2ee4-4d4c-88d4-f4b6a5f9b97b · outbound

This paper cites Defining Trust and E-trust: From Old Theories to New Problems.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Defining Trust and E-trust: From Old Theories to New Problems

Reference 2009

Resolution: verified exact; doi, observed 2026-08-16T12:40:03.586880Z

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 990e2a18-3511-485c-baf0-45ecacd75153 · outbound

This paper cites Toward a Model of Trust and E-trust Processes Using Object-oriented Methodologies.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Toward a Model of Trust and E-trust Processes Using Object-oriented Methodologies

Reference 2010

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:04.322413Z

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 2ef81270-5087-47a4-aaa7-3e72b2480f47 · outbound

This paper cites Can we trust robots?.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Can we trust robots?

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:03.367625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 53bfe4f4-a184-4b78-92ea-f950acd2706a · outbound

This paper cites Prediction of Sentiment Analysis on Educational Data Based on Deep Learning Approach.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Prediction of Sentiment Analysis on Educational Data Based on Deep Learning Approach

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:03.477550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63f3ea1e-54d0-4420-b7a9-c350c2920a6f · outbound

This paper cites Su impacto en diferentes áreas del conocimiento ha sido inmediato.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Su impacto en diferentes áreas del conocimiento ha sido inmediato

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:04.501476Z

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 65074678-6b14-462f-9f05-3017af97e37f · outbound

This paper cites Biden vs Trump: Modelling US General Elections Using BERT Language Model.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Biden vs Trump: Modelling US General Elections Using BERT Language Model

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-16T12:40:03.360503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 794c5862-b3a0-446e-8039-aa32a9b16527 · outbound

This paper cites A pesar del reciente auge de los LLM como ChatGPT, ya se han documentado varias limitaciones del modelo.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification A pesar del reciente auge de los LLM como ChatGPT, ya se han documentado varias limitaciones del modelo

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:04.490514Z

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 f3a8b2a0-df2e-44e3-ac30-84908eb3698b · outbound

This paper cites verdad científica.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification verdad científica

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:04.469906Z

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 69514ae1-9e7b-4e62-829b-914c5e817ac2 · outbound

This paper cites Trust as reliance in computer artifacts means that we expect an object to do something to help us attain our goals.

Trusting CHATGPT: how minor tweaks in the prompts lead to major differences in sentiment classification Trust as reliance in computer artifacts means that we expect an object to do something to help us attain our goals

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T12:40:04.359625Z

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.

Coverage vector

Reference resolution

Outbound references

Pith citing papers