Exploring the Relationship Between Personality Traits and User Feedback

• URL: http://arxiv.org/abs/2307.12036v1
• Date: Sat, 22 Jul 2023 10:10:27 GMT
• Title: Exploring the Relationship Between Personality Traits and User Feedback
• Authors: Volodymyr Biryuk, Walid Maalej
• Abstract summary: We present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. Results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users.
• Score: 9.289846887298852
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Previous research has studied the impact of developer personality in different software engineering scenarios, such as team dynamics and programming education. However, little is known about how user personality affect software engineering, particularly user-developer collaboration. Along this line, we present a preliminary study about the effect of personality traits on user feedback. 56 university students provided feedback on different software features of an e-learning tool used in the course. They also filled out a questionnaire for the Five Factor Model (FFM) personality test. We observed some isolated effects of neuroticism on user feedback: most notably a significant correlation between neuroticism and feedback elaborateness; and between neuroticism and the rating of certain features. The results suggest that sensitivity to frustration and lower stress tolerance may negatively impact the feedback of users. This and possibly other personality characteristics should be considered when leveraging feedback analytics for software requirements engineering.

Related papers

• Beyond Text: Leveraging Multi-Task Learning and Cognitive Appraisal Theory for Post-Purchase Intention Analysis [10.014248704653]
  This study evaluates multi-task learning frameworks grounded in Cognitive Appraisal Theory to predict user behavior. Our experiments show that users' language and traits improve predictions above and beyond models predicting only from text.
  arXiv  Detail & Related papers  (2024-07-11T04:57:52Z)
• Modulating Language Model Experiences through Frictions [56.17593192325438]
  Over-consumption of language model outputs risks propagating unchecked errors in the short-term and damaging human capabilities in the long-term. We propose selective frictions for language model experiences, inspired by behavioral science interventions, to dampen misuse.
  arXiv  Detail & Related papers  (2024-06-24T16:31:11Z)
• EERPD: Leveraging Emotion and Emotion Regulation for Improving Personality Detection [19.98674724777821]
  We propose a new personality detection method called EERPD. This method introduces the use of emotion regulation, a psychological concept highly correlated with personality, for personality prediction. Experimental results demonstrate that EERPD significantly enhances the accuracy and robustness of personality detection.
  arXiv  Detail & Related papers  (2024-06-23T11:18:55Z)
• Large Language Models Can Infer Personality from Free-Form User Interactions [0.0]
  GPT-4 can infer personality with moderate accuracy, outperforming previous approaches. Results show that the direct focus on personality assessment did not result in a less positive user experience. Preliminary analyses suggest that the accuracy of personality inferences varies only marginally across different socio-demographic subgroups.
  arXiv  Detail & Related papers  (2024-05-19T20:33:36Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• Rethinking the Evaluation of Dialogue Systems: Effects of User Feedback on Crowdworkers and LLMs [57.16442740983528]
  In ad-hoc retrieval, evaluation relies heavily on user actions, including implicit feedback. The role of user feedback in annotators' assessment of turns in a conversational perception has been little studied. We focus on how the evaluation of task-oriented dialogue systems ( TDSs) is affected by considering user feedback, explicit or implicit, as provided through the follow-up utterance of a turn being evaluated.
  arXiv  Detail & Related papers  (2024-04-19T16:45:50Z)
• Can ChatGPT Read Who You Are? [9.694940903078656]
  We report the results of a comprehensive user study featuring texts written in Czech by a representative population sample of 155 participants. We compare the personality trait estimations made by ChatGPT against those by human raters and report ChatGPT's competitive performance in inferring personality traits from text.
  arXiv  Detail & Related papers  (2023-12-26T14:43:04Z)
• PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detection [50.66968526809069]
  We propose a novel personality detection method, called PsyCoT, which mimics the way individuals complete psychological questionnaires in a multi-turn dialogue manner. Our experiments demonstrate that PsyCoT significantly improves the performance and robustness of GPT-3.5 in personality detection.
  arXiv  Detail & Related papers  (2023-10-31T08:23:33Z)
• Editing Personality for Large Language Models [73.59001811199823]
  This paper introduces an innovative task focused on editing the personality traits of Large Language Models (LLMs) We construct a new benchmark dataset PersonalityEdit to address this task.
  arXiv  Detail & Related papers  (2023-10-03T16:02:36Z)
• Understanding How People Rate Their Conversations [73.17730062864314]
  We conduct a study to better understand how people rate their interactions with conversational agents. We focus on agreeableness and extraversion as variables that may explain variation in ratings.
  arXiv  Detail & Related papers  (2022-06-01T00:45:32Z)
• My tweets bring all the traits to the yard: Predicting personality and relational traits in Online Social Networks [4.095574580512599]
  This study aims to provide a prediction model for a holistic personality profiling in Online Social Networks (OSNs) We first designed a feature engineering methodology that extracts a wide range of features from OSN accounts of users. Then, we designed a machine learning model that predicts scores for the psychological traits of the users based on the extracted features.
  arXiv  Detail & Related papers  (2020-09-22T20:30:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.