Question: What dataset do the authors use?
Evidence: Online social networks have become one of the most disruptive communication platforms, as everyday billions of individuals use them to interact with each other. Their penetration in our everyday lives seems ever-growing and has in turn generated a massive volume of publicly available data open to analysis. The digital footprints left across these multiple media platforms provide us with a unique source to study and understand how the linguistic phenotype of a given user is related to social attributes such as socioeconomic status (SES).
The quantification and inference of SES of individuals is a long lasting question in the social sciences. It is a rather difficult problem as it may depend on a combination of individual characteristics and environmental variables BIBREF0 . Some of these features can be easier to assess like income, gender, or age whereas others, relying to some degree on self-definition and sometimes entangled with privacy issues, are harder to assign like ethnicity, occupation, education level or home location. Furthermore, individual SES correlates with other individual or network attributes, as users tend to build social links with others of similar SES, a phenomenon known as status homophily BIBREF1 , arguably driving the observed stratification of society BIBREF2 . At the same time, shared social environment, similar education level, and social influence have been shown to jointly lead socioeconomic groups to exhibit stereotypical behavioral patterns, such as shared political opinion BIBREF3 or similar linguistic patterns BIBREF4 . Although these features are entangled and causal relation between them is far from understood, they appear as correlations in the data.
Datasets recording multiple characteristics of human behaviour are more and more available due to recent developments in data collection technologies and increasingly popular online platforms and personal digital devices. The automatic tracking of online activities, commonly associated with profile data and meta-information; the precise recording of daily activities, interaction dynamics and mobility patterns collected through mobile personal devices; together with the detailed and expert annotated census data all provide new grounds for the inference of individual features or behavioral patterns BIBREF5 . The exploitation of these data sources has already been proven to be fruitful as cutting edge recommendation systems, advanced methods for health record analysis, or successful prediction tools for social behaviour heavily rely on them BIBREF6 . Nevertheless, despite the available data, some inference tasks, like individual SES prediction, remain an open challenge.
The precise inference of SES would contribute to overcome several scientific challenges and could potentially have several commercial applications BIBREF7 . Further, robust SES inference would provide unique opportunities to gain deeper insights on socioeconomic inequalities BIBREF8 , social stratification BIBREF2 , and on the driving mechanisms of network evolution, such as status homophily or social segregation.
In this work, we take a horizontal approach to this problem and explore various ways to infer the SES of a large sample of social media users. We propose different data collection and combination strategies using open, crawlable, or expert annotated socioeconomic data for the prediction task. Specifically, we use an extensive Twitter dataset of 1.3M users located in France, all associated with their tweets and profile information; 32,053 of them having inferred home locations. Individual SES is estimated by relying on three separate datasets, namely socioeconomic census data; crawled profession information and expert annotated Google Street View images of users' home locations. Each of these datasets is then used as ground-truth to infer the SES of Twitter users from profile and semantic features similar to BIBREF9 . We aim to explore and assess how the SES of social media users can be obtained and how much the inference problem depends on annotation and the user's individual and linguistic attributes.
We provide in Section SECREF2 an overview of the related literature to contextualize the novelty of our work. In Section SECREF3 we provide a detailed description of the data collection and combination methods. In Section SECREF4 we introduce the features extracted to solve the SES inference problem, with results summarized in Section SECREF5 . Finally, in Section SECREF6 and SECREF7 we conclude our paper with a brief discussion of the limitations and perspectives of our methods.
Specifically, we use an extensive Twitter dataset of 1.3M users located in France, all associated with their tweets and profile information; 32,053 of them having inferred home locations.
Answer: The authors use an extensive Twitter dataset of 1.3M users located in France, all associated with their tweets and profile information; 32,053 of them having inferred home locations.