Who Tweets? Deriving the Demographic Characteristics of Age, Occupation and Social Class from Twitter User Meta-Data

By Luke S Sloan, Jeffrey Morgan, P. Burnap, Matthew Leighton Williams. PLoS ONE, 2015

378 citations recorded by the source corpus.

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect “signatures” of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.

Subfields: Communication, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/b9d0be2104456b811cd4ec406978abd1df34f095

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

In Sociology and Political Science

Citations in the corpus

The Demographics of Social Media Users in the Russian-Language Internet (2017, background): “In [22], it was found out that most UK Twitter users prefer to indicate hobbies instead of their job.”
Studying User Income through Language, Behaviour and Affect in Social Media (2015, background): “A recent study has proven the effectiveness of building large corpora of users and their SOC occupation from social media finding many similarities to real world population distribution across jobs [23].”
Who Makes Trends? Understanding Demographic Biases in Crowdsourced Recommendations (2017, background): “Past works have attempted to infer a particular user’s gender and race from her name (Blevins and Mullen 2015; Mislove et al. 2011; Liu and Ruths 2013), or the age from Twitter proﬁle description (by searching for patterns like ‘21 yr old’ or ‘born in 1989’ ) (Sloan et al. 2015).”
Demographics of News Sharing in the U.S. Twittersphere (2017, background): “Some efforts attempt to infer the gender of a user from her name [22, 25, 27], or the age from Twitter profile descriptions [39], by using patterns like ‘ like 25 yr old ’ or ‘ born in 1990 ’.”
Computational Sociolinguistics: A Survey (2016, methodology): “Preoţiuc-Pietro et al. (2015) then mapped the derived occupations to income and Sloan et al. (2015) mapped the occupations to social class categories.”