Assessing Socioeconomic Status of Twitter Users: A Survey

Abstract

Every day, the emotion and opinion of different people across the world are reflected in the form of short messages using microblogging platforms. Despite the existence of enormous potential introduced by this data source, the Twitter community is still ambiguous and is not fully explored yet. While there are a huge number of studies examining the possibilities of inferring gender and age, there exist hardly researches on socioeconomic status (SES) inference of Twitter users. As socioeconomic status is essential to treating diverse questions linked to human behavior in several fields (sociology, demography, public health, etc.), we conducted a comprehensive literature review of SES studies, inference methods, and metrics. With reference to the research on literature's results, we came to outline the most critical challenges for researchers. To the best of our knowledge, this paper is the first review that introduces the different aspects of SES inference. Indeed, this article provides the benefits for practitioners who aim to process and explore Twitter SES inference.

Social sentiment segregation: Evidence from Twitter and Google Trends in Chile during the COVID-19 dynamic quarantine strategy

“…As sentiment analysis is becoming a pervasive tool to evaluate the impact of economic and social policies, it should be considered whether observable social sentiment indicators reflect the feelings towards such policies of the population as a whole or those of specific groups. Moreover, our empirical approach, which hinges on the socioeconomic heterogeneity of Chilean municipalities and the dynamic features of the pandemic strategy, allows directly identifying the socioeconomic status of Twitter users, a rather hard task to achieve [16,28,59]. Additionally, and as a secondary result of our analysis, we demonstrate a substantial degree of socioeconomic segregation in stock market reactions to government announcements.…”

“…However, to the best of our knowledge, we are the first to take advantage of the heterogeneity on social sentiment resulting from dynamic quarantines. Furthermore, our empirical setup constitutes a novel approach that allows extracting the socioeconomic status of users of social network platforms [16]. Secondly, and from a methodological point of view, the high degree of intracity socioeconomic segregation that the country exhibits, together with the characteristics of the dynamic quarantine scheme, allows us to investigate the effects of the quarantine announcements on market sentiment at the smallest administrative level in Chile, which allows for the construction of better counterfactuals for our analyses [4].…”

Occupation Prediction with Multimodal Learning from Tweet Messages and Google Street View Images

“…Predicting the occupation of social media users, involves analyzing content posted on social media platforms (e.g., Twitter) with language processing (Preoţiuc-Pietro et al, 2015a;Liang et al, 2018;Pardo and Rosso, 2019;Das et al, 2021) and image processing techniques (Wieczorek et al, 2018;Hu et al, 2021;Li et al, 2021) to infer the profession or job role of individuals based on their online behavior, interactions, and profile information (Preoţiuc-Pietro et al, 2015a;Hu et al, 2021). As one of the important tasks for demographic inference of social media data, also known as social media user profiling (Liang et al, 2018;Ikeda et al, 2013), user occupation prediction and categorization are important in understanding and interpreting the behaviors of various user groups (Preoţiuc-Pietro et al, 2015a), thus enabling applications for a variety of disciplines, such as sociology, demography, and public health (Ghazouani et al, 2020;Khanam et al, 2021).…”