Gender Inference using Statistical Name Characteristics in Twitter

Mueller, Juergen; Stumme, Gerd

doi:10.1145/2955129.2955182

Cited by 21 publications

(11 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We acknowledge that there is a distinction between gender and sex (West and Zimmerman, 1987), but we use gender estimates as a proxy for sex in order to be consistent with CDC measures. This technique combines three classification approaches: (a) matching users’ first names to data from the US Social Security Administration24 (which captures approximately 60% of Twitter names), (b) an SVM classifier applied to word and character n-gram features from users’ names25 and (c) a decision tree classifier applied to features constructed from the linguistic structure of users’ names, including the count of syllables, vowels, consonants, bouba (round) and kiki (sharp) vowels and consonants,26 27 and whether or not the last character is a vowel 28. For each user, we combined the predictions from all three classifiers using a weighted stacked logistic regression framework 29.…”

Section: Methodsmentioning

confidence: 99%

Social media captures demographic and regional physical activity

Cesare

Nguyen

Grant

et al. 2019

BMJ Open Sport Exerc Med

View full text Add to dashboard Cite

ObjectivesWe examined the use of data from social media for surveillance of physical activity prevalence in the USA.MethodsWe obtained data from the social media site Twitter from April 2015 to March 2016. The data consisted of 1 382 284 geotagged physical activity tweets from 481 146 users (55.7% men and 44.3% women) in more than 2900 counties. We applied machine learning and statistical modelling to demonstrate sex and regional variations in preferred exercises, and assessed the association between reports of physical activity on Twitter and population-level inactivity prevalence from the US Centers for Disease Control and Prevention.ResultsThe association between physical inactivity tweet patterns and physical activity prevalence varied by sex and region. Walking was the most popular physical activity for both men and women across all regions (15.94% (95% CI 15.85% to 16.02%) and 18.74% (95% CI 18.64% to 18.88%) of tweets, respectively). Men and women mentioned performing gym-based activities at approximately the same rates (4.68% (95% CI 4.63% to 4.72%) and 4.13% (95% CI 4.08% to 4.18%) of tweets, respectively). CrossFit was most popular among men (14.91% (95% CI 14.52% to 15.31%)) among gym-based tweets, whereas yoga was most popular among women (26.66% (95% CI 26.03% to 27.19%)). Men mentioned engaging in higher intensity activities than women. Overall, counties with higher physical activity tweets also had lower leisure-time physical inactivity prevalence for both sexes.ConclusionsThe regional-specific and sex-specific activity patterns captured on Twitter may allow public health officials to identify changes in health behaviours at small geographical scales and to design interventions best suited for specific populations.

show abstract

Section: Methodsmentioning

confidence: 99%

Social media captures demographic and regional physical activity

Cesare

Nguyen

Grant

et al. 2019

BMJ Open Sport Exerc Med

View full text Add to dashboard Cite

show abstract

“…Data from Twitter does not include demographic characteristics of users. To address this limitation, we developed a scalable and efficient ensemble approach for inferring gender by combining predictions from three previously proposed methods that focus only on the metadata available on users' profile (Burger et al, 2011;Mislove et al, 2011;Longley et al, 2015;Mueller and Stumme, 2016). The three approaches included, method (1), Twitter users' first names were matched to data from the U.S. Social Security Administration (Longley et al, 2015) (this captured approximately 60% of Twitter names); method (2), we used word and character n-grams from users' names and a Support Vector Machine (SVM) classifier with a linear kernel (Burger et al, 2011); and method (3), we applied a decision tree classifier to the linguistic structure of users' names-including the count of syllables, vowels, consonants, bouba (round) and kiki (sharp) vowels and consonants (Maurer et al, 2006;Nielsen and Rendall, 2011), and whether or not the last character was a vowel.…”

Section: Methodsmentioning

confidence: 99%

Use of social media, search queries, and demographic data to assess obesity prevalence in the United States

et al. 2019

View full text Add to dashboard Cite

Obesity is a global epidemic affecting millions. Implementation of interventions to curb obesity rates requires timely surveillance. In this study, we estimated sex-specific obesity prevalence using social media, search queries, demographics and built environment variables. We collected 3,817,125 and 1,382,284 geolocated tweets on food and exercise respectively, from Twitter's streaming API from April 2015 to March 2016. We also obtained searches related to physical activity and diet from Google Search Trends for the same time period. Next, we inferred the gender of Twitter users using machine learning methods and applied mixed-effects state-level linear regression models to estimate obesity prevalence. We observed differences in discussions of physical activity and foods, with males reporting higher intensity physical activities and lower caloric foods across 40 and 48 states, respectively. In addition, counties with the highest percentage of exercise and food tweets had lower male and female obesity prevalence. Lastly, our models separately captured overall male and female spatial trends in obesity prevalence. The average correlation between actual and estimated obesity prevalence was 0.797(95% CI, 0.796, 0.798) and 0.830 (95% CI, 0.830, 0.831) for males and females, respectively. Social media can provide timely community-level data on health information seeking and changes in behaviors, sentiments and norms. Social media data can also be combined with other data types such as, demographics, built environment variables, diet and physical activity indicators from other digital sources (e.g., mobile applications and wearables) to monitor health behaviors at different geographic scales, and to supplement delayed estimates from traditional surveillance systems.

show abstract

“…Previous works explored demographic characteristics using names from different sources or combined with other factors. Several works predicted gender based on users' names only [18,19]. Burger et al distinguished gender on Twitter using names and screen names for classification [20].…”

Section: Demographic Characteristics From Namesmentioning

confidence: 99%

Age Estimates from Name Characters

et al. 2021

View full text Add to dashboard Cite

Traditionally, we have been attempting to extract useful features from the massive amount of data generated daily. However, following the legal constraints regarding personal data protection and the challenges of potential data biases and manipulation, artificial intelligence that relies less on big data and more on reasoning ability has become an emerging trend. This paper demonstrates how to estimate age and gender using names only. The proposed two-layer comparative model was trained on Taiwanese names, and its generalizability was further examined on bilingual and cross-border names. By considering additional features of the contextual environment, the model achieves high accuracy in age and gender prediction on Taiwanese and bilingual names. However, the prediction results for ethnic-Chinese Malaysian names (in English) do not reach the same level. This is due to the linguistic differences among Chinese dialects; the features trained on Taiwanese names cannot be directly applied to English names in Malaysia. This study illustrates a path for accomplishing prediction tasks using minimal data and highlights a future possibility for further research.

show abstract

Gender Inference using Statistical Name Characteristics in Twitter

Cited by 21 publications

References 23 publications

Social media captures demographic and regional physical activity

Social media captures demographic and regional physical activity

Use of social media, search queries, and demographic data to assess obesity prevalence in the United States

Age Estimates from Name Characters

Contact Info

Product

Resources

About