From Post to Values: Mining Schwartz Values of Individuals from Social Media

Sun, Mingwei; Zhang, Huaping; Zhao, Yang; Shang, Jianyun

doi:10.1007/978-3-662-45558-6_19

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…Manual coding methodologies are ill-suited to use for large bodies of text (Portman, 2014), while most others to date are extremely brief and thus unable to capture most references to values (Bardi et al, 2008). Much research to date is not specifically designed to align with a theory (LIWC-based approaches; Chen et al, 2014;Christen et al, 2016;Mukta et al, 2016) or, in more extreme cases, use machine-learning approaches where results strongly depend on the specific texts used for training the algorithm (Gou et al, 2014;Sun et al, 2014) and therefore have limited generalizability. Finally, some are impossible to validate independently (IBM Watson, 2018).…”

Section: Previous Attempts To Estimate Individuals' Value Orientations From Textual Datamentioning

confidence: 99%

“…They analysed 200 latest tweets collected from 256 Twitter users and were able to find only small correlations ( rv = .026) between values as measured with the PVQ and the text‐based value scores. Sun et al (2014) analysed the 400 most recent contributions of 101 Chinese graduate students on Chinese social media sites (e.g. Weibo) who had also filled in, among other measures, the PVQ.…”

Section: Introductionmentioning

confidence: 99%

“…Hall & Caton, 2017;Kosinski, Stillwell, & Graepel, 2013;Park et al, 2015;Schwartz et al, 2013), demonstrating that personality can be reliably measured from behavioural traces. Values are arguably lagging behind with relatively few published 'Big Data' approaches appearing just recently (Boyd et al, 2015;Chen, Hsieh, Mahmud, & Nichols, 2014;Christen, Narvaez, Tanner, & Ott, 2016;Gou, Zhou, & Yang, 2014;Mukta, Ali, & Mahmud, 2016;Sun, Zhang, Zhao, & Shang, 2014;Wilson, Shen, & Mihalcea, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Development and Validation of the Personal Values Dictionary: A Theory–Driven Tool for Investigating References to Basic Human Values in Text

et al. 2020

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Estimating psychological constructs from natural language has the potential to expand the reach and applicability of personality science. Research on the Big Five has produced methods to reliably assess personality traits from text, but the development of comparable tools for personal values is still in the early stages. Based on the Schwartz theory of basic human values, we developed a dictionary for the automatic assessment of references to personal values in text. To refine and validate the dictionary, we used Facebook updates, blog posts, essays, and book chapters authored by over 180 000 individuals. The results show high reliability for the dictionary and a pattern of correlations between the value types in line with the circumplex structure. We found small to moderate (rs = .1–.4) but consistent correlations between dictionary scores and self‐reported scores for 7 out of 10 values. Correlations between the dictionary scores and age, gender, and political orientation of the author and scores for other established dictionaries mostly followed theoretical predictions. The Personal Values Dictionary can be used to assess references to value orientations in textual data, such as tweets, blog posts, or status updates, and will stimulate further research in methods to assess human basic values from text.

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Section: Previous Attempts To Estimate Individuals' Value Orientations From Textual Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Development and Validation of the Personal Values Dictionary: A Theory–Driven Tool for Investigating References to Basic Human Values in Text

et al. 2020

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“…Accordingly, the authors agree that Schwartz’s (1992) value model is a valuable framework for analyzing different motivations. Similar to a researcher’s efforts to extract personality traits from texts, there are also studies, such as those conducted by Sun et al (2014) or Chen et al (2014), that attempt to extract a person's values from social media posts based on Schwartz’s (1992) model.…”

Section: Literature Reviewmentioning

confidence: 99%

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

Egger

2023

CBTH

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Purpose This study aims to understand how traveling Instagrammers should be characterized by bridging socio-psychological concepts with Instagram usage and travel typologies. Design/methodology/approach Using analysis of variance, differences between non-Instagrammers and passive and active users were identified with regard to their personality traits and human values; traveling Instagrammers and their travel types were characterized in more detail using k-means clustering, and, finally, posting behavior was examined using multiple regression. Findings The results demonstrate significant differences between the user groups in terms of their personality traits, their human values and their travel behavior. The multiple regressions provide deeper insights into tourists’ Instagram posting. Research limitations/implications This study was conducted on 1,100 German travelers, meaning that the results are only valid for this cultural context. All data were collected using an online panel and participants received incentives, and thus extrinsic rewards, instead of using merely intrinsic motivation to take part in the survey. Originality/value This study contributes to a better understanding of Instagram usage behavior when traveling and adds valuable insights to this field of research. At the same time, the results are relevant for tourism businesses wishing to optimize their communication and marketing strategies.

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“…Several studies used the Linguistic Inquiry and Word Count (LIWC, Pennebaker et al, 2007), the most commonly used language analysis tool for investigating the relation between word use and psychological variables, to analyse the user-generated content on social networking sites, such as Reddit (Chen et al, 2014), Facebook (Mukta et al, 2010) and Weibo (Sun et al, 2014) and to (successfully) predict the PVQ scores from users' status updates. For example, in the study by Chen et al (2014), for each Reddit user, one LIWC measure for each LIWC category based on the user's posts/comments was calculated.…”

Section: Introductionmentioning

confidence: 99%

The Development and Preliminary Validation of the Serbian Value Lexicon - An Archival Approach to Value Measurement

Pavlović,

Vesić Pavlović

2023

SSRN Journal

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From Post to Values: Mining Schwartz Values of Individuals from Social Media

Cited by 4 publications

References 20 publications

Development and Validation of the Personal Values Dictionary: A Theory–Driven Tool for Investigating References to Basic Human Values in Text

Development and Validation of the Personal Values Dictionary: A Theory–Driven Tool for Investigating References to Basic Human Values in Text

Characterizing traveling Instagrammers: an exploration of socio-psychological concepts, travel typologies and posting motives

The Development and Preliminary Validation of the Serbian Value Lexicon - An Archival Approach to Value Measurement

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