Inferring User Social Class in Online Social Networks

Filho, Renato Miranda; Borges, Guilherme; Almeida, Jussara M.; Pappa, Gisele L.

doi:10.1145/2659480.2659502

Cited by 8 publications

(8 citation statements)

References 12 publications

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“…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.…”

Section: Discussionmentioning

confidence: 79%

“…Ai et al [27] evaluate the inference accuracy gained on latent attribute inference models by augmenting the user characteristics with features derived from the Twitter profiles and postings of friends. Filho et al [28] propose a method to automatically generate a user social class, taking advantage of Foursquare user interactions and Twitter messages. Volokova et al [29] propose an approach to predict latent personal attributes, including user demographics, online personality, emotions, and sentiments from texts published on Twitter.…”

Section: Plos Onementioning

confidence: 99%

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

Díaz

Henríquez

2021

PLoS ONE

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The Chilean health authorities have implemented a sanitary strategy known as dynamic quarantine or strategic quarantine to cope with the COVID-19 pandemic. Under this system, lockdowns were established, lifted, or prolonged according to the weekly health authorities’ assessment of municipalities’ epidemiological situation. The public announcements about the confinement situation of municipalities country-wide are made typically on Tuesdays or Wednesdays before noon, have received extensive media coverage, and generated sharp stock market fluctuations. Municipalities are the smallest administrative division in Chile, with each city broken down typically into several municipalities. We analyze social media behavior in response to the confinement situation of the population at the municipal level. The dynamic quarantine scheme offers a unique opportunity for our analysis, given that municipalities display a high degree of heterogeneity, both in size and in the socioeconomic status of their population. We exploit the variability over time in municipalities’ confinement situations, resulting from the dynamic quarantine strategy, and the cross-sectional variability in their socioeconomic characteristics to evaluate the impact of these characteristics on social sentiment. Using event study and panel data methods, we find that proxies for social sentiment based on Twitter queries are negatively related (more pessimistic) to increases in the number of confined people, but with a statistically significant effect concentrated on people from the wealthiest cohorts of the population. For indicators of social sentiment based on Google Trends, we found that search intensity during the periods surrounding government announcements is positively related to increases in the total number of confined people. Still, this effect does not seem to be dependent on the segments of the population affected by the quarantine. Furthermore, we show that the observed heterogeneity in sentiment mirrors heterogeneity in stock market reactions to government announcements. We provide evidence that the observed stock market behavior around quarantine announcements can be explained by the number of people from the wealthiest segments of the population entering or exiting lockdown.

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Section: Discussionmentioning

confidence: 79%

Section: Plos Onementioning

confidence: 99%

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

Díaz

Henríquez

2021

PLoS ONE

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“…Supporting friend-searching Discovery of social relationships [133,142] Profiling of moving objects [133,142] Interest recommendation [134,142] Suggesting routes and places Profiling of moving objects [172][173][174] Trip recommendation [172][173][174] Understanding communities Discovery of social relationships [175] Profiling of moving objects [175,176] Table 3. Cont.…”

Section: Energymentioning

confidence: 99%

“…Social services are mainly related to profiling individual movement patterns, discovering social relationships, and recommending interests. Addressing these application issues can help facilitate social services, recommend potential friends [133,134,142], suggest places and routes [172][173][174], understand community life [175,176], etc.…”

Section: Social and Commercial Services And Public Administrationmentioning

confidence: 99%

Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

Wang

Miwa

Morikawa

2020

Sensors

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The increasingly wide usage of smart infrastructure and location-aware terminals has helped increase the availability of trajectory data with rich spatiotemporal information. The development of data mining and analysis methods has allowed researchers to use these trajectory datasets to identify urban reality (e.g., citizens’ collective behavior) in order to solve urban problems in transportation, environment, public security, etc. However, existing studies in this field have been relatively isolated, and an integrated and comprehensive review is lacking the problems that have been tackled, methods that have been tested, and services that have been generated from existing research. In this paper, we first discuss the relationships among the prevailing trajectory mining methods and then, classify the applications of trajectory data into three major groups: social dynamics, traffic dynamics, and operational dynamics. Finally, we briefly discuss the services that can be developed from studies in this field. Practical implications are also delivered for participants in trajectory data mining. With a focus on relevance and association, our review is aimed at inspiring researchers to identify gaps among tested methods and guiding data analysts and planners to select the most suitable methods for specific problems.

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“…Uddin et al (2014) classified Twitter users into six types including personal, professional, business, spammer, news feed and marketing services. Other studies addressed the detection of automated Twitter accounts (bots) from human users (Chu et al , 2012), organisations from individual users (McCorriston et al , 2015), students from non-students (Al-Qurishi et al , 2015), users of different occupations (Preoţiuc-Pietro et al , 2015) and social classes (Filho et al , 2014). In sports, Yang et al (2013) classified Twitter followers of sports clubs into fans and non-fans.…”

Section: Introductionmentioning

confidence: 99%

“Less is more”

Zhang

Bors

2019

OIR

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Purpose This work studies automated user classification on Twitter in the public health domain, a task that is essential to many public health-related research works on social media but has not been addressed. The purpose of this paper is to obtain empirical knowledge on how to optimise the classifier performance on this task. Design/methodology/approach A sample of 3,100 Twitter users who tweeted about different health conditions were manually coded into six most common stakeholders. The authors propose new, simple features extracted from the short Twitter profiles of these users, and compare a large set of classification models (including state-of-the-art) that use more complex features and with different algorithms on this data set. Findings The authors show that user classification in the public health domain is a very challenging task, as the best result the authors can obtain on this data set is only 59 per cent in terms of F1 score. Compared to state-of-the-art, the methods can obtain significantly better (10 percentage points in F1 on a “best-against-best” basis) results when using only a small set of 40 features extracted from the short Twitter user profile texts. Originality/value The work is the first to study the different types of users that engage in health-related communication on social media, applicable to a broad range of health conditions rather than specific ones studied in the previous work. The methods are implemented as open source tools, and together with data, are the first of this kind. The authors believe these will encourage future research to further improve this important task.

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Inferring User Social Class in Online Social Networks

Cited by 8 publications

References 12 publications

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

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

Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

“Less is more”

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