Location, Occupation, and Semantics based Socioeconomic Status Inference on Twitter This work was supported by the SoSweet ANR project (ANR-15-CE38-0011) and the ACADEMICS project financed by IDEX LYON.
TABLE I: Number of users and estimated fractions of low and high SES in each dataset
['[EMPTY]', '[BOLD] Census', '[BOLD] Occupation', '[BOLD] Expert']
To solve the SES inference problem we used the above described three datasets (for a summary We defined the inference task as a two-way classification problem by dividing the user set of each dataset into two groups. For the census and occupation datasets the lower and higher SES classes were separated by the average income computed from the whole distribution, while in the case of the expert annotated data we assigned people from the lowest five SES labels to the lower SES class in the two-way task. The relative fractions of people assigned to the two classes are depicted in Fig.