Developing Age and Gender Predictive Lexica over Social Media

Sap, Maarten; Park, Gregory; Eichstaedt, Johannes C.; Kern, Margaret L.; Stillwell, David; Kosinski, Michal; Ungar, Lyle H.; Schwartz, H. Andrew

doi:10.3115/v1/d14-1121

Cited by 191 publications

(212 citation statements)

References 27 publications

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“…Their results were also consistent with Psyzcynski and Greenberg's theory of depression, in that texters with a smaller amount of self-focus were associated with more successful conversations. In addition, Schwartz et al (2014) showed that regression models based on Facebook language can be used to predict an individuals degree of depression.…”

Section: Linguistic and Social Indicatorsmentioning

confidence: 99%

See 1 more Smart Citation

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

Hollingshead¹,

Ireland²,

Loveys³

2017

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Section: Linguistic and Social Indicatorsmentioning

confidence: 99%

“…However, for at least some participants, there was skepticism related to how well social media represents the mental health of users. (Schwartz et al, 2014), and using unsupervised Machine Learning techniques to explore depression-related language on Twitter (Resnik et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

Hollingshead¹,

Ireland²,

Loveys³

2017

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“…We used the demographic classification tool from the World Well-Being Project (Sap et al, 2014) 2 . For each depression and PTSD user we estimated their gender, forcing the classifier to make a binary decision as to whether the user was 'Female' or 'Male', and used the age estimate as-is (an ostensibly continuous variable).…”

Section: Age-and Gender-matched Controlsmentioning

confidence: 99%

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Coppersmith¹,

Dredze

Harman

et al. 2015

Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Realit

245

199

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This paper presents a summary of the Computational Linguistics and Clinical Psychology (CLPsych) 2015 shared and unshared tasks. These tasks aimed to provide apples-to-apples comparisons of various approaches to modeling language relevant to mental health from social media. The data used for these tasks is from Twitter users who state a diagnosis of depression or post traumatic stress disorder (PTSD) and demographically-matched community controls. The unshared task was a hackathon held at Johns Hopkins University in November 2014 to explore the data, and the shared task was conducted remotely, with each participating team submitted scores for a held-back test set of users. The shared task consisted of three binary classification experiments: (1) depression versus control, (2) PTSD versus control, and (3) depression versus PTSD. Classifiers were compared primarily via their average precision, though a number of other metrics are used along with this to allow a more nuanced interpretation of the performance measures.

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“…In order to estimate which tweets are more likely to be written by females or a older user, we use the classifier introduced in (Sap et al, 2014). This is a regularized Linear SVM that obtains state-of-theart prediction results on user gender (91.9% accuracy) and age (r = .835) prediction from social media text.…”

Section: Methodsmentioning

confidence: 99%

Controlling Human Perception of Basic User Traits

Preoţiuc-Pietro

Guntuku

Ungar

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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Much of our online communication is textmediated and, lately, more common with automated agents. Unlike interacting with humans, these agents currently do not tailor their language to the type of person they are communicating to. In this pilot study, we measure the extent to which human perception of basic user trait information -gender and age -is controllable through text. Using automatic models of gender and age prediction, we estimate which tweets posted by a user are more likely to mis-characterize his traits. We perform multiple controlled crowdsourcing experiments in which we show that we can reduce the human prediction accuracy of gender to almost random -an over 20% drop in accuracy. Our experiments show that it is practically feasible for multiple applications such as text generation, text summarization or machine translation to be tailored to specific traits and perceived as such.

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Developing Age and Gender Predictive Lexica over Social Media

Cited by 191 publications

References 27 publications

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

Proceedings of the Fourth Workshop on Computational Linguistics and Clinical Psychology –- From Linguistic Signal to Clinical Reality

CLPsych 2015 Shared Task: Depression and PTSD on Twitter

Controlling Human Perception of Basic User Traits

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