"How Old Do You Think I Am?" A Study of Language and Age in Twitter

By Dong Nguyen, Rilana Gravel, Dolf Trieschnigg, Theo Meder. 2013

347 citations recorded by the source corpus.

In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages.

Subfields: Artificial Intelligence, Sociology and Political Science, Natural Language Processing

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Citations in the corpus

Language (Technology) is Power: A Critical Survey of “Bias” in NLP (2020, background): “For example, how are social groups delineated when defining demographic attribute prediction tasks (e.g., Koppel et al., 2002; Rosenthal and McKeown, 2011; Nguyen et al., 2013)?”
Ethical and social risks of harm from Language Models (2021, background): “Language utterances (e.g. tweets) are already being analysed to predict private information such as political orientation (Makazhanov et al., 2014; Preoţiuc-Pietro et al., 2017), age (Morgan-Lopez et al., 2017; Nguyen et al., 2013), and health data such as addiction relapses (Golbeck, 2018).”
The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task (2017, methodology): “…to answer this question, we consider the highest absolute positive and negative coefficients in the logistic regression classifier in Experiments 1 and 2, an approach widely used as a method of extracting the most salient features (Nguyen et al., 2013; Burke et al., 2013; Brooks et al., 2013).”
A Survey on Bias and Fairness in Machine Learning (2019, background): “An example of this type of bias can be seen in [97] where the differences in use of language across different gender and age groups is discussed.”
Adversarial Removal of Demographic Attributes from Text Data (2018, background): “…textual information can be predictive of age, race, gender, and many other social factors of the author (Kop-pel et al., 2002; Burger et al., 2011; Nguyen et al., 2013; Weren et al., 2014; Verhoeven and Daele-mans, 2014; Rangel et al., 2016; Verhoeven et al., 2016; Blodgett et al., 2016), or even…”
Taxonomy of Risks posed by Language Models (2022, background): “Tweets) are already being analysed to predict private information such as political orientation [121, 144], age [131, 135], and health data such as addiction relapses [63].”
Overview of the Author Profiling Task at PAN 2013 (2013, background): “[22] studied the use of language and age among Dutch Twitter users, where the documents are really”
The Tweets They Are a-Changin: Evolution of Twitter Users and Behavior (2014, methodology): “Prediction of User Characteristics While it is largely orthogonal to our work, there has been significant work in prediction of user profile fields, including user’s location, gender, race/ethnicity, and age (Mislove et al. 2011; Pennacchiotti and Popescu 2011a; 2011b; Bergsma et al. 2013; Nguyen et”
Analyzing Biases in Human Perception of User Age and Gender from Text (2016, methodology): “On Twitter, Nguyen et al. (2013) analyzed the relationship be-tween language use and age, modelled as a continuous variable.”
Why Gender and Age Prediction from Tweets is Hard: Lessons from a Crowdsourcing Experiment (2014, methodology): “Females tend to use more pronouns, emoticons, emotion words, and blog words ( lol, omg, etc. ), while males tend to use more numbers, technology words, and links (Rao et al., 2010; Bamman et al., 2014; Nguyen et al., 2013).”
Write Like I Write: Herding in the Language of Online Reviews (2014, methodology): “It has been argued that style also changes with life experience (Nguyen et al. 2013).”
TweetGenie: Development, Evaluation, and Lessons Learned (2014, methodology): “Goals The original research (Nguyen et al., 2013) was carried out to support analyses of trends and to study sociolinguistic aspects of language use.”
Semantic Variation in Online Communities of Practice (2018, neutral): “…a substantial amount of work has used NLP techniques to study correlations between linguistic variables and macro-sociological categories such as age (Nguyen et al., 2013), gender (Nguyen et al., 2014; Burger et al., 2011; Ciot et al., 2013), and other demographic factors (Eisenstein et al., 2014).”
Overview of the 3rd Author Profiling Task at PAN 2015 (2015, background): “[44] studied the use of language and age among Dutch Twitter users, where the documents are really short, with an average length of less than 10 terms.”
Understanding User Profiles on Social Media for Fake News Detection (2018, methodology): “For age prediction, previous studies focus on extracting features from text posted by users [16].”
CLiPS Stylometry Investigation (CSI) corpus: A Dutch corpus for the detection of age, gender, personality, sentiment and deception in text (2014, background): “At the moment, there exist a number of Dutch corpora for the detection of age, gender (Peersman et al., 2011; Nguyen et al., 2013), authorship and personality (Luyckx and Daelemans, 2008).”
Computational Sociolinguistics: A Survey (2016, methodology): “Females tend to use more pronouns [6, 7, 8, 17, 194, 231, 233] (in particular first person singular [186, 194, 233]).”