Compositional Demographic Word Embeddings

Welch, Charles; Kummerfeld, Jonathan K.; Pérez-Rosas, Verónica; Mihalcea, Rada

doi:10.18653/v1/2020.emnlp-main.334

Cited by 18 publications

(12 citation statements)

References 38 publications

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“…It may well be the case in the UK that word choice varies more between areas and genders than age cohorts, for example, a reviewer who cites the product's "lush vanilla taste" may reside in the West of England, while calling a bad service "shite" may indicate they are Scottish. This is an interesting counterfinding to Welch et al (2020) which found better embedding performance with age-and genderaware representations in a global population. Differing privacy requirements for separate attributes are a feature of multiple variations on differential privacy regimes (Kamalaruban et al, 2020;Alaggan et al, 2017;Jorgensen et al, 2015).…”

Section: Discussionmentioning

confidence: 73%

CAPE: Context-Aware Private Embeddings for Private Language Learning

Plant¹,

Gkatzia²,

Giuffrida³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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Neural language models have contributed to state-of-the-art results in a number of downstream applications including sentiment analysis, intent classification and others. However, obtaining text representations or embeddings using these models risks encoding personally identifiable information learned from language and context cues that may lead to privacy leaks. To ameliorate this issue, we propose Context-Aware Private Embeddings (CAPE), a novel approach which combines differential privacy and adversarial learning to preserve privacy during training of embeddings. Specifically, CAPE firstly applies calibrated noise through differential privacy to maintain the privacy of text representations by preserving the encoded semantic links while obscuring sensitive information. Next, CAPE employs an adversarial training regime that obscures identified private variables. Experimental results demonstrate that our proposed approach is more effective in reducing private information leakage than either single intervention, with approximately a 3% reduction in attacker performance compared to the best-performing current method.

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Section: Discussionmentioning

confidence: 73%

CAPE: Context-Aware Private Embeddings for Private Language Learning

Plant¹,

Gkatzia²,

Giuffrida³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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“…We searched for English language Reddit posts with the phrase "I'm <age>" that were posted between December 1 and December 31, 2019. In the literature, similar approaches have been used to construct large Reddit data sets containing self-reported demographics [33][34][35]. We were interested in youth (13-17 years), young adult (18-20 years), and adult (21-54 years) age segments.…”

Section: Identifying Self-reporting Of Age On Redditmentioning

confidence: 99%

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation (Preprint)

Chew¹,

Kery²,

Baum³

et al. 2020

Preprint

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BACKGROUND Social media are important for monitoring perceptions of public health issues and for educating target audiences about health; however, limited information about the demographics of social media users makes it challenging to identify conversations among target audiences and limits how well social media can be used for public health surveillance and education outreach efforts. Certain social media platforms provide demographic information on followers of a user account, if given, but they are not always disclosed, and researchers have developed machine learning algorithms to predict social media users’ demographic characteristics, mainly for Twitter. To date, there has been limited research on predicting the demographic characteristics of Reddit users. OBJECTIVE We aimed to develop a machine learning algorithm that predicts the age segment of Reddit users, as either adolescents or adults, based on publicly available data. METHODS This study was conducted between January and September 2020 using publicly available Reddit posts as input data. We manually labeled Reddit users’ age by identifying and reviewing public posts in which Reddit users self-reported their age. We then collected sample posts, comments, and metadata for the labeled user accounts and created variables to capture linguistic patterns, posting behavior, and account details that would distinguish the adolescent age group (aged 13 to 20 years) from the adult age group (aged 21 to 54 years). We split the data into training (n=1660) and test sets (n=415) and performed 5-fold cross validation on the training set to select hyperparameters and perform feature selection. We ran multiple classification algorithms and tested the performance of the models (precision, recall, F1 score) in predicting the age segments of the users in the labeled data. To evaluate associations between each feature and the outcome, we calculated means and confidence intervals and compared the two age groups, with 2-sample t tests, for each transformed model feature. RESULTS The gradient boosted trees classifier performed the best, with an F1 score of 0.78. The test set precision and recall scores were 0.79 and 0.89, respectively, for the adolescent group (n=254) and 0.78 and 0.63, respectively, for the adult group (n=161). The most important feature in the model was the number of sentences per comment (permutation score: mean 0.100, SD 0.004). Members of the adolescent age group tended to have created accounts more recently, have higher proportions of submissions and comments in the r/teenagers subreddit, and post more in subreddits with higher subscriber counts than those in the adult group. CONCLUSIONS We created a Reddit age prediction algorithm with competitive accuracy using publicly available data, suggesting machine learning methods can help public health agencies identify age-related target audiences on Reddit. Our results also suggest that there are characteristics of Reddit users’ posting behavior, linguistic patterns, and account features that distinguish adolescents from adults.

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“…Reddit users have been found to be primarily male, young adults (under 30), located in the USA and primarily identify as christian or atheist (Welch et al, 2020). It is possible that results presented in this paper do not generalize to populations that differ significantly from the population of Reddit users.…”

Section: Personalized Word Embeddingsmentioning

confidence: 76%

Exploring the Value of Personalized Word Embeddings

Welch

Kummerfeld

Pérez-Rosas

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics

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In this paper, we introduce personalized word embeddings, and examine their value for language modeling. We compare the performance of our proposed prediction model when using personalized versus generic word representations, and study how these representations can be leveraged for improved performance. We provide insight into what types of words can be more accurately predicted when building personalized models. Our results show that a subset of words belonging to specific psycholinguistic categories tend to vary more in their representations across users and that combining generic and personalized word embeddings yields the best performance, with a 4.7% relative reduction in perplexity. Additionally, we show that a language model using personalized word embeddings can be effectively used for authorship attribution.

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Compositional Demographic Word Embeddings

Cited by 18 publications

References 38 publications

CAPE: Context-Aware Private Embeddings for Private Language Learning

CAPE: Context-Aware Private Embeddings for Private Language Learning

Predicting Age Groups of Reddit Users Based on Posting Behavior and Metadata: Classification Model Development and Validation (Preprint)

Exploring the Value of Personalized Word Embeddings

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