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Inferring Twitters’ Socio-demographics to Correct Sampling Bias of Social Media Data for Augmenting Travel Behavior Analysis
16 references·3 citing·47 second hop
Inferring Twitters’ Socio-demographics to Correct Sampling Bias of Social Media Data for Augmenting Travel Behavior Analysis (2021)Inferring Twitters’ Socio-demographics to…Understanding the Demographics of Twitter Users (2021)Understanding the Demographic…Characterizing Geographic Variation in Well-Being Using Tweets (2021)Characterizing Geographic Var…Understanding and Modeling the Activity-Travel Behavior of University Commuters at a Large Canadian University (2018)Understanding and Modeling th…Forecasting current and next trip purpose with social media data and Google Places (2018)Forecasting current and next …Education at a Glance 2018 (2018)Education at a Glance 2018Potentials of using social media to infer the longitudinal travel behavior: A sequential model-based clustering method (2017)Potentials of using social me…Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges (2016)Exploring the capacity of soc…Activity space estimation with longitudinal observations of social media data (2016)Activity space estimation wit…Exploring the potential of phone call data to characterize the relationship between social network and travel behavior (2015)Exploring the potential of ph…Personality, Gender, and Age in the Language of Social Media: The Open-Vocabulary Approach (2013)Personality, Gender, and Age …Predicting the Political Alignment of Twitter Users (2011)Predicting the Political Alig…Political Polarization on Twitter (2011)Political Polarization on Twi…ePluribus: Ethnicity on Social Networks (2010)ePluribus: Ethnicity on Socia…Understanding individual human mobility patterns (2008)Understanding individual huma…Patterns of age‐based linguistic variation in American English1 (2008)Patterns of age‐based linguis…Education At A Glance (2005)Education At A GlanceOn the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto (2023)On the Relationship Between C…Measuring the impact of slow zones on street life using social media (2022)Measuring the impact of slow …Investigation of social media representation bias in disasters: Towards a systematic framework (2022)Investigation of social media…Asian American ethnic identification by surname (2000)From Unequal Access to Differentiated Use: A Literature Review and Agenda for Research on Digital Inequality (2003)From Unequal Access to Differentiated Use: A Literature Review and Agenda for Research on Digital Inequality (2004)Predicting Sexual Assault Perpetration Among Heterosexually Active Young Men (2016)Twitter's Glass Ceiling: The Effect of Perceived Gender on Online Visibility (2021)Who Does What on the Web: A Large-Scale Study of Browsing Behavior (2021)The origin of bursts and heavy tails in human dynamics (2005)The scaling laws of human travel (2006)Limits of Predictability in Human Mobility (2010)A universal model for mobility and migration patterns (2012)An alternative "description of personality": The Big-Five factor structure. (1990)Personality Structure: Emergence of the Five-Factor Model (1990)An Introduction to the Five‐Factor Model and Its Applications (1992)Automatic personality assessment through social media language. 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References

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  • 2018Understanding and Modeling the Activity-Travel Behavior of University Commuters at a Large Canadian University

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53 cited
  • 2015Exploring the potential of phone call data to characterize the relationship between social network and travel behavior51 cited
  • Cited by

    • 2022Measuring the impact of slow zones on street life using social media13 cited
    • 2022Investigation of social media representation bias in disasters: Towards a systematic framework11 cited
    • 2023On the Relationship Between Crowdsourced Sentiments and Mobility Trends During COVID-19: A Case Study of Kyoto8 cited