Understanding individuals' personal values from social media word use

Chen, Jilin; Hsieh, Gary; Mahmud, Jalal; Nichols, Jeffrey

doi:10.1145/2531602.2531608

Cited by 119 publications

(112 citation statements)

References 33 publications

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“…This finding is in line with studies demonstrating the use of predictive models for measuring personality (Chen, Hsieh, Mahmud, & Nichols, 2014;Lambiotte & Kosinski, 2014;Yarkoni, 2010) and indicates that predictive scoring algorithms are suitable for scoring image-based response scales. Moderate correlations between the image-based and the text-based measures for Curiosity and Openness demonstrated good concurrent validity of the image-based format (r = 0.5, p b 0.001).…”

Section: Discussionsupporting

confidence: 88%

From Likert scales to images: Validating a novel creativity measure with image based response scales

Leutner¹,

Yearsley²,

Codreanu³

et al. 2017

Personality and Individual Differences

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The use of image-based testing to assess individual differences has increased substantially in recent years, with proponents arguing that they offer a more engaging alternative to text-based psychometric tests. Yet research examining the validity of these tests is near to non-existent. Traditional image-based formats have been little more than an adaptation of self-reports, with images replacing questions but not response options. The current study develops a novel image-based creativity measure, where images replace conventional response scales, and scores on the measures are obtained using a linear regression scoring algorithm to predict three self-reported creativity measures. Using sequential forward selection on a set of 77 image-based items, an optimal solution of 14 items that were valid predictors of self-reported creativity scores were identified. The image-based measure had good test-retest reliability. Implications are discussed in terms of the usefulness of image-based testing for practitioners seeking engaging and short test formats.

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Section: Discussionsupporting

confidence: 88%

From Likert scales to images: Validating a novel creativity measure with image based response scales

Leutner¹,

Yearsley²,

Codreanu³

et al. 2017

Personality and Individual Differences

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show abstract

“…For deriving personality traits Linguist Inquiry and Word Count (LIWC) [20] had been used frequently. References [20][21][22][23][24][25][26][27][28][29] have shown that lexicons used by people can be used for understanding their personal values and how to use these traits for a recommendation. Though all these approaches have been used extensively in analyzing personality traits, these also have shortcomings of predefined word category correlation.…”

Section: Related Workmentioning

confidence: 99%

Twitter Can Predict Your Next Place of Visit

Chauhan

Tejwani

Toshniwal

2016

International Journal of Distributed Sensor Networks

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The present work focuses on predicting users' next place of visit using their past tweets. We hypothesize that tweets of the person have predictive power on his location and therefore can be used to predict his next place of visit. This problem is important for location based advertising and recommender based services. To predict the next place of visit, we calculate the probabilities of visiting different types of places using bank of binary classifiers and Markov models. More specifically, we train bank of binary classifiers on past tweets and calculated the probabilities of visiting next places. Since bank of binary classifiers is based on a bagof-words model, to account for time of last visited place and place itself, we built Markov models for different time duration to calculate probabilities of visiting next place. Empirical evaluation shows that by combining the probabilities obtained from bank of binary classifiers and Markov models the accuracy of predicting next place increased from 65% to 80%.

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“…• match specified (or measured) Big Five personality traits and associated personality facets [8]; match basic human values as described by Schwartz [9] and Chen [10] • match a writer's word choices: favored words, word music, word length, favored mood • match writing patterns: n-grams (2-, 3-, 4-, and 5-grams); an n-gram is a series of n words in a row that has appeared in a naturally occurring, existing text.…”

Section: Mimicking Writersmentioning

confidence: 99%

“…The computation of personality scores needs to be fast, which is accomplished by pre-computing as much of the LIWC categories as possible and using caches. InkWell also targets values [10], which are computed similarly.…”

Section: Inkwell Flowmentioning

confidence: 99%

InkWell

Gabriel

Chen

Nichols

2015

Proceedings of the 2015 ACM SIGCHI Conference on Creativity and Cognition

Self Cite

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InkWell is a writer's assistant-a natural language revision program designed to assist creative writers by producing stylistic variations on texts based on craft-based facets of creative writing and by mimicking aspects of specified writers and their personality traits. It is built on top of an optimization process that produces variations on a supplied text, evaluates those variations quantitatively, and selects variations that best satisfy the goals of writing craft and writer mimicry. We describe the design and capabilities of InkWell, and present an early evaluation of its effectiveness and uses with two established literary writers along with an experiment using InkWell to write haiku on its own.

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Understanding individuals' personal values from social media word use

Cited by 119 publications

References 33 publications

From Likert scales to images: Validating a novel creativity measure with image based response scales

From Likert scales to images: Validating a novel creativity measure with image based response scales

Twitter Can Predict Your Next Place of Visit

InkWell

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