To Buy or Not to Buy? Understanding the Role of Personality Traits in Predicting Consumer Behaviors

Liu, Zhe; Wang, Yi; Mahmud, Jalal; Akkiraju, Rama; Schoudt, Jerald; Xu, Anbang; Donovan, Bryan A.

doi:10.1007/978-3-319-47874-6_24

Cited by 20 publications

(12 citation statements)

References 25 publications

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“…Our work also suggests the importance of personalization for persuasion: with different personality types being open to different styles of argument. Future work might be based on methods for profiling participant personality from simple online behaviors (Di Eugenio et al, 2013;Liu et al, 2016;Pan and Zhou, 2014;Yee et al, 2011), or from user-generated content such as first-person narratives or conversations (Mairesse and Walker, 2006a;Mairesse and Walker, 2006b;Rahimtoroghi et al, 2016;Rahimtoroghi et al, 2014). We could then select personalized arguments to meet a participant's processing style.…”

Section: Discussionmentioning

confidence: 99%

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Lukin¹,

Anand²,

Walker³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments.

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Section: Discussionmentioning

confidence: 99%

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Lukin¹,

Anand²,

Walker³

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

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“…However, the two methods may not measure the same construct, but different constructs from differing perspectives. Additionally, although there is initial evidence that NLP outputs can be predictors of consumer behavior [ 70 ], there is not yet certainty that those variables are predictors of therapeutic or behavioral change or that they impact psychosocial outcomes. On the other hand, symptom reduction measures have a better representation and acceptance as predictors of therapeutic and behavioral change within the extant literature.…”

Section: Discussionmentioning

confidence: 99%

Natural Language Processing Tools for Assessing Progress and Outcome of Two Veteran Populations: Cohort Study From a Novel Online Intervention for Posttraumatic Growth

Norman

Govindjee

Norman³

et al. 2020

JMIR Form Res

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Background Over 100 million Americans lack affordable access to behavioral health care. Among these, military veterans are an especially vulnerable population. Military veterans require unique behavioral health services that can address military experiences and challenges transitioning to the civilian sector. Real-world programs to help veterans successfully transition to civilian life must build a sense of community, have the ability to scale, and be able to reach the many veterans who cannot or will not access care. Digitally based behavioral health initiatives have emerged within the past few years to improve this access to care. Our novel behavioral health intervention teaches mindfulness-based cognitive behavioral therapy and narrative therapy using peer support groups as guides, with human-facilitated asynchronous online discussions. Our study applies natural language processing (NLP) analytics to assess effectiveness of our online intervention in order to test whether NLP may provide insights and detect nuances of personal change and growth that are not currently captured by subjective symptom measures. Objective This paper aims to study the value of NLP analytics in assessing progress and outcomes among combat veterans and military sexual assault survivors participating in novel online interventions for posttraumatic growth. Methods IBM Watson and Linguistic Inquiry and Word Count tools were applied to the narrative writings of combat veterans and survivors of military sexual trauma who participated in novel online peer-supported group therapies for posttraumatic growth. Participants watched videos, practiced skills such as mindfulness meditation, told their stories through narrative writing, and participated in asynchronous, facilitated online discussions with peers. The writings, including online postings, by the 16 participants who completed the program were analyzed after completion of the program. Results Our results suggest that NLP can provide valuable insights on shifts in personality traits, personal values, needs, and emotional tone in an evaluation of our novel online behavioral health interventions. Emotional tone analysis demonstrated significant decreases in fear and anxiety, sadness, and disgust, as well as increases in joy. Significant effects were found for personal values and needs, such as needing or desiring closeness and helping others, and for personality traits of openness, conscientiousness, extroversion, agreeableness, and neuroticism (ie, emotional range). Participants also demonstrated increases in authenticity and clout (confidence) of expression. NLP results were generally supported by qualitative observations and analysis, structured data, and course feedback. Conclusions The aggregate of results in our study suggest that our behavioral health intervention was effective and that NLP can provide valuable insights on shifts in personality traits, personal values, and needs, as well as measure changes in emotional tone. NLP’s sensitivity to changes in emotional tone, values, and personality strengths suggests the efficacy of NLP as a leading indicator of treatment progress.

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“…Arnoux et al [8] have shown that the IBM Watson Personality Insight API performs well in comparison to other techniques. Other prior studies [45,46] have also used the API and demonstrated reasonable performance. We found that the majority of the traits had a high score of 1.00 and low score of 0.01.…”

Section: Feature Selectionmentioning

confidence: 95%

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

et al. 2021

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Background Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions. Objective The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets. Methods In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets. Results Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found. Conclusions Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

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To Buy or Not to Buy? Understanding the Role of Personality Traits in Predicting Consumer Behaviors

Cited by 20 publications

References 25 publications

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion

Natural Language Processing Tools for Assessing Progress and Outcome of Two Veteran Populations: Cohort Study From a Novel Online Intervention for Posttraumatic Growth

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

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