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This study explores linguistic differences between human and LLM-generated dialogues, using 19.5K dialogues generated by ChatGPT-3.5 as a companion to the EmpathicDialogues dataset.
Can people feel happy and sad at the same time?
Larsen, J. T., McGraw, A. P., and Cacioppo, J. T · 2001
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Lying words: Predicting deception from linguistic styles
Newman, M. L., Pennebaker, J. W., Berry, D. S., and Richards, J. M · 2003
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Linguistic markers of psychological change surrounding September 11, 2001
Cohn, M. A., Mehl, M. R., and Pennebaker, J. W · 2004
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Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language
Chung, C. K., and Pennebaker, J. W · 2008
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The psychological meaning of words: LIWC and computerized text analysis methods
Tausczik, Y. R., and Pennebaker, J. W · 2010
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The psychological functions of function words
Chung, C., and Pennebaker, J · 2011
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Pronoun use reflects standings in social hierarchies
Kacewicz, E., Pennebaker, J. W., Davis, M., Jeon, M., and Graesser, A. C · 2014
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Trait and state authenticity across cultures
Slabu, L., Lenton, A. P., Sedikides, C., and Bruder, M · 2014
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The development and psychometric properties of LIWC2015
Pennebaker, J. W., Boyd, R. L., Jordan, K., and Blackburn, K · 2015
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Mind mapping: Using everyday language to explore social & psychological processes
Pennebaker, J. W · 2017
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Umap: Uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N., and Großberger, L · 2018
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Examining long-term trends in politics and culture through language of political leaders and cultural institutions
Jordan, K. N., Sterling, J., Pennebaker, J. W., and Boyd, R. L · 2019
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Towards empathetic open-domain conversation models: A new benchmark and dataset
Rashkin, H., Smith, E. M., Li, M., and Boureau, Y.-L · 2019
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An overview of chatbot technology
Adamopoulou, E., and Moussiades, L · 2020
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Emotional tone, analytical thinking, and somatosensory processes of a sample of italian tweets during the first phases of the covid-19 pandemic: Observational study
Monzani, D., Vergani, L., Pizzoli, S. F. M., Marton, G., and Pravettoni, G · 2021
Cited alongside, same era.
The development and psychometric properties of LIWC-22
Boyd, R. L., Ashokkumar, A., Seraj, S., and Pennebaker, J. W · 2022
Cited alongside, same era.
Dual-feature-embeddings-based semi-supervised learning for cognitive engagement classification in online course discussions
Liu, Z., Kong, W., Peng, X., Yang, Z., Liu, S., Liu, S., and Wen, C · 2023
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Self-presentation in medicine: How language patterns reflect physician impression management goals and affect perceptions
Markowitz, D. M · 2023
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Authentic first impressions relate to interpersonal, social, and entrepreneurial success
Markowitz, D. M., Kouchaki, M., Gino, F., Hancock, J. T., and Boyd, R. L · 2023
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Visualizing linguistic diversity of text datasets synthesized by large language models
Reif, E., Kahng, M., and Petridis, S · 2023
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Can ChatGPT defend its belief in truth? evaluating LLM reasoning via debate
Wang, B., Yue, X., and Sun, H · 2023
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Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al · 2023
Cited alongside, same era.
Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives
Bartal, A., Jagodnik, K. M., Chan, S. J., Babu, M. S., and Dekel, S · 2023
Cited alongside, same era.
Characteristics of online user-generated text predict the emotional intelligence of individuals
Dover, Y., and Amichai-Hamburger, Y · 2023
Cited alongside, same era.
Towards possibilities & impossibilities of AI-generated text detection: A survey
Ghosal, S. S., Chakraborty, S., Geiping, J., Huang, F., Manocha, D., and Bedi, A. S · 2023
Cited alongside, same era.
Human heuristics for AI-generated language are flawed
Jakesch, M., Hancock, J. T., and Naaman, M · 2023
Cited alongside, same era.
Contrasting linguistic patterns in human and LLM-generated news text
Muñoz-Ortiz, A., Gómez-Rodríguez, C., and Vilares, D
Cited in the paper.
Characterizing empathy and compassion using computational linguistic analysis
Yaden, D. B., Giorgi, S., Jordan, M., Buffone, A., Eichstaedt, J. C., Schwartz, H. A., Ungar, L., and Bloom, P · 2023
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Representing affect information in word embeddings
Zhang, Y., Chen, W., Zhang, R., and Zhang, X · 2023
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LLM-GEm: Large language model-guided prediction of people’s empathy levels towards newspaper article
Hasan, M. R., Hossain, M. Z., Gedeon, T., and Rahman, S · 2024
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Linguistic markers of inherently false AI communication and intentionally false human communication: Evidence from hotel reviews
Markowitz, D. M., Hancock, J. T., and Bailenson, J. N · 2024
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The science of detecting LLM-generated text
Tang, R., Chuang, Y.-N., and Hu, X · 2024
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