Location, Occupation, and Semantics Based Socioeconomic Status Inference on Twitter

Jacob Levy Abitbol 1 Màrton Karsai 1 Eric Fleury 2
1 DANTE - Dynamic Networks : Temporal and Structural Capture Approach
Inria Grenoble - Rhône-Alpes, LIP - Laboratoire de l'Informatique du Parallélisme, IXXI - Institut Rhône-Alpin des systèmes complexes
Abstract : The socioeconomic status of people depends on a combination of individual characteristics and environmental variables, thus its inference from online behavioral data is a difficult task. Attributes like user semantics in communication, habitat, occupation, or social network are all known to be determinant predictors of this feature. In this paper we propose three different data collection and combination methods to first estimate and, in turn, infer the socioeconomic status of French Twitter users from their online semantics. Our methods are based on open census data, crawled professional profiles, and remotely sensed, expert annotated information on living environment. Our inference models reach similar performance of earlier results with the advantage of relying on broadly available datasets and of providing a generalizable framework to estimate socioeconomic status of large numbers of Twitter users. These results may contribute to the scientific discussion on social stratification and inequalities, and may fuel several applications.
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Conference papers
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https://hal.inria.fr/hal-02061219
Contributor : Márton Karsai <>
Submitted on : Thursday, March 7, 2019 - 10:36:25 PM
Last modification on : Wednesday, April 3, 2019 - 1:12:17 AM

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Jacob Levy Abitbol, Màrton Karsai, Eric Fleury. Location, Occupation, and Semantics Based Socioeconomic Status Inference on Twitter. ICDMW 2018 - IEEE International Conference on Data Mining Workshops, Nov 2018, Singapore, Singapore. pp.1192-1199, ⟨10.1109/ICDMW.2018.00171⟩. ⟨hal-02061219⟩

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