Inferring international and internal migration patterns from Twitter data

Zagheni, Emilio; Garimella, Venkata Rama Kiran; Weber, Ingmar; State, Bogdan

doi:10.1145/2567948.2576930

Cited by 191 publications

(171 citation statements)

References 22 publications

Supporting

Mentioning

165

Contrasting

Unclassified

Order By: Relevance

“…If two reported locations of the same user are different, there might be a change of his/her usual residence, we infer that a migration happened during the past five years. Scholars have suggested that if users provide information over a long period of time, they are more likely to provide reliable information (Zagheni, Garimella, & Weber, 2014). Even though our samples are not representative of the whole population in China, we find that the distribution of three million users is positively correlated with the actual population distribution.…”

Section: Datacontrasting

confidence: 58%

Measuring Polycentricity of Mega-City Regions in China Based on the Intercity Migration Flows

Mu¹,

Yeh²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:This paper uses the intercity migration flows to examine relations between Chinese cities, identify the important mega-city regions and measure each region's polycentricity from an interaction perspective. Data set contains the long-term residential migration trajectories of three million Sina weibo users across 345 cities. Cities with close connectivity deployed around one or several mega cities are identified as mega-city regions. Features of the mega-city regions are characterized by the strength of migration flows, density of connections, and regional migration patterns. The results show that the disparities exist in different mega-city regions; most mega-city regions are lack of polycentricity.

show abstract

Section: Datacontrasting

confidence: 58%

Measuring Polycentricity of Mega-City Regions in China Based on the Intercity Migration Flows

Mu¹,

Yeh²

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Ojala et al (2017) use Google Correlate to detect evidence for different socio-economic contexts related to fertility (e.g., teen fertility, fertility in high income households, etc.). Email data have been used to track migrants (Zagheni and Weber, 2012); Facebook data to monitor migrant stocks ; patterns of short-and longterm migration (Zagheni et al, 2014); and family change have been derived from Twitter data (Billari et al, 2017). These applications are important, and have demonstrated that the combination of survey and internet data improve predictive power and the accuracy of the described demographic phenomena.…”

Section: The Incomplete Data Revolution In Demographymentioning

confidence: 99%

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

2018

View full text Add to dashboard Cite

The idea of organizing PEOPLES stemmed from two related observations, namely the availability of large amounts of spontaneous data covering a range of personal aspects and the fact that such aspects are usually studied in isolation. Social media users nowadays freely express what is on their mind at any moment in time, at any location, and about virtually anything. These large amounts of spontaneously produced texts open up a unique opportunity to learn more about such users, e.g., predicting demographic variables (age, gender), but also personality types, as well as emotions and opinion expressions. This observation is not new, of course, and this opportunity has largely been exploited in the recent years, with abundant works on sentiment analysis, emotion detection, and personality. However, such traits of human personality and behavior have indeed attracted a substantial amount of attention but have been mostly studied in isolation, often in different -but related -communities, such as NLP, CL, AI. Therefore, we thought that the time was ripe to bring these communities a step closer to study people's traits and expressions jointly and in their interplay on such large volumes of available data.The communities' response, with 25 received submissions coming from 11 different countries and going well beyond typical NLP topics, proves again this year that there is wide interest at this intersection, and we are happy to be able to provide a context for exchanging ideas.Following the reviewers's advice, 14 papers were selected for inclusion in the proceedings. They cover a wide range of topics related to the three main PEOPLES themes (personality, emotion and opinion), their interaction and the impact of their modeling on social aspects like well-being, political preferences, humor and language use.To further enrich this volume, we additionally invited our keynote speakers to submit position papers that accompany their talks, and are excited that both of our keynotes submitted excellent papers touching upon issues of making NLP models more demographically aware and how researchers from related fields such as demography can benefit from NLP techniques.We hope that this is just the second edition of what will become series of workshops bringing together researchers in Computational Linguistics, Natural Language Processing and Computational Social Science, who share an interest in personality, opinion and emotion detection, and especially in researching the intertwining of such traits and expressions.We would like to thank our program committee consisting of 33 researchers from a variety of backgrounds for their insightful and constructive reviews. Without their support, this workshop would not have been possible. In addition, we thank all authors for submitting papers and making PEOPLES a big success. Also thanks to our two invited speakers, Dirk Hovy and Letizia Mencarini (Bocconi University, Italy), for having accepted to come to the workshop and share their expertise and ideas on PEOPLES' topics. We thank NAACL for...

show abstract

“…Sensemaking involves interlocked processes of creating schemas or frames and critically evaluating frames against data [21,30]. Lariat supports sensemaking by helping the user construct groups focused on the text-based content of messages, while also showing these groups in comparison to one another, and along several quantitative dimensions.…”

Section: Sensemaking Workflowsmentioning

confidence: 99%

“…This data is potentially a rich source of insight about human behavior, and it is drawing the attention of social scientists from many fields, including communication [2], organizational systems [8], information and computer science [9,27], and sociology [30]. Unfortunately, datasets obtained from social media platforms are challenging to work with.…”

Section: Introductionmentioning

confidence: 99%

Lariat: A Visual Analytics Tool for Social Media Researchers to Explore Twitter Datasets

Chen

Brooks

Kocielnik

et al. 2017

Proceedings of the 50th Hawaii International Conference on System Sciences (2017)

View full text Add to dashboard Cite

show abstract

Inferring international and internal migration patterns from Twitter data

Cited by 191 publications

References 22 publications

Measuring Polycentricity of Mega-City Regions in China Based on the Intercity Migration Flows

Measuring Polycentricity of Mega-City Regions in China Based on the Intercity Migration Flows

Proceedings of the Second Workshop on Computational Modeling of People’s Opinions, Personality, and Emotions in Social Media

Lariat: A Visual Analytics Tool for Social Media Researchers to Explore Twitter Datasets

Contact Info

Product

Resources

About