User Generated vs. Supported Contents: Which One Can Better Predict Basic Human Values?

Mukta, Md. Saddam Hossain; Ali, Mohammed Eunus; Mahmud, Jalal

doi:10.1007/978-3-319-47874-6_31

Cited by 15 publications

(11 citation statements)

References 23 publications

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“…Although no individual correlation between a LIWC measure and a PVQ measure was higher than r = .18, 13.8% to 18.2% of the variance in PVQ scores could be explained via the LIWC measures in total. Mukta et al (2016) used LIWC measures as well to predict the PVQ scores of 567 Facebook users from their status updates; they were able to explain between 13.3% and 21.1% of the variance in the PVQ measures. Gou et al (2014) used a linguistic model that was trained on texts created by Amazon MTurk workers to predict value orientations from tweets.…”

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

confidence: 99%

“…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%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…They identified values with a data‐driven approach. Mukta et al () identified values from both user‐generated (that is, statuses) and supported contents (that is, page‐likes) in Facebook. Hsieh et al() found a relation between user's reading interest and values in Twitter.…”

Section: Preliminaries and Related Workmentioning

confidence: 99%

Temporal modeling of basic human values from social network usage

Mukta

Ali

Mahmud

2018

Asso for Info Science & Tech

Self Cite

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Basic human values represent what we think are important to our lives that include security, independence, success, kindness, and pleasure. Each of us holds different values with different degrees of importance. Existing studies show that values of a person can be identified from their social network usage. However, the value priority of a person may change over time due to different factors such as time, event, influence, social structure, and technology. In this research, we are the first to investigate whether the change of value priorities can be identified from social network usage. We propose a weighted hybrid time‐series‐based model to capture the change of values of a social network user. We conducted an experimental study with 726 Facebook users and showed that our model accurately captures the value priority changes from the social network usage and achieves significantly higher accuracy than our baseline hidden Markov model‐based technique. We also validated the change of a user's value priorities in real life using a questionnaire‐based technique.

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“…All these social networking mediums are interlinked. A number of studies have been conducted on values [17], personality [18], and preferences [19]. In this paper, we analyze the sentiments derived from the post/tweets that found in social networks.…”

Section: Introductionmentioning

confidence: 99%

Identifying Human Personalized Sentiment with Streaming Data

Hossain¹,

Ahmed²,

Saha³

et al. 2017

IJCA

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Nowadays, social networks are becoming common platform of our emotion, sentiment, personality, and so on. A significant number of studies are also available about sentiment and emotion analysis from social network data. We observe that there are few studies are available those compute sentiment over real time data in Twitter and Foursquare. In this paper, we have conducted a research that can compute sentiment from real time data in a social network. We also use multiple techniques to compute sentiment such as sentiwordnet and textblob. We analyze the sentiments of a human from his/her twitter and from the location in foursquare of that person.

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User Generated vs. Supported Contents: Which One Can Better Predict Basic Human Values?

Cited by 15 publications

References 23 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

Temporal modeling of basic human values from social network usage

Identifying Human Personalized Sentiment with Streaming Data

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