On the Volume of Geo-referenced Tweets and Their Relationship to Events Relevant for Migration Tracking (original) (raw)

Analyzing the EU Migration Crisis as Reflected on Twitter

KN - Journal of Cartography and Geographic Information

The proliferation of social media has resulted in its extensive use as a valuable source of information for researchers. This paper aims to use Twitter data to analyze and visualize tweets about the migration crisis in the European Union from 2016 to 2021. The paper uses a methodology to structure data for better understanding of complex social media data. The methods and metrics include the facet model of location based social media, the HyperLogLog data structure and novel uses of the metric typicality. The authors have also developed a web based interactive application closely following the methodology used to organize the dataset. Additionally the work also includes maps using spatial typicality which could be utilized for studying spatial phenomenon. The case study selected also provides unique insights and sets a template for working with multi-lingual geo-social media data. The authors believe that these methods and metrics could be reproduced for other case studies and aid i...

Digital Footprints of International Migration on Twitter

Advances in Intelligent Data Analysis XVIII, 2020

Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant's country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.

Analyzing Refugee Migration Patterns Using Geo-tagged Tweets

ISPRS International Journal of Geo-Information, 2017

Over the past few years, analysts have begun to materialize the "Citizen as Sensors" principle by analyzing human movements, trends and opinions, as well as the occurrence of events from tweets. This study aims to use geo-tagged tweets to identify and visualize refugee migration patterns from the Middle East and Northern Africa to Europe during the initial surge of refugees aiming for Europe in 2015, which was caused by war and political and economic instability in those regions. The focus of this study is on exploratory data analysis, which includes refugee trajectory extraction and aggregation as well as the detection of topical clusters along migration routes using the V-Analytics toolkit. Results suggest that only few refugees use Twitter, limiting the number of extracted travel trajectories to Europe. Iterative exploration of filter parameters, dynamic result mapping, and content analysis were essential for the refinement of trajectory extraction and cluster detection. Whereas trajectory extraction suffers from data scarcity, hashtag-based topical clustering draws a clearer picture about general refugee routes and is able to find geographic areas of high tweet activities on refugee related topics. Identified spatio-temporal clusters can complement migration flow data published by international authorities, which typically come at the aggregated (e.g., national) level. The paper concludes with suggestions to address the scarcity of geo-tagged tweets in order to obtain more detailed results on refugee migration patterns.

MMoveT15: A Twitter Dataset for Extracting and Analysing Migration-Movement Data of the European Migration Crisis 2015

IEEE 28th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE), 2019

In the 2015 migration crisis thousands of refugees and migrants crossed the border to Hungary, Austria and Germany. The movements of these people are reflected in social media, especially on Twitter. In this paper we present a dataset of 3275 Tweets from the months September and October 2015. These Tweets are annotated regarding their relevance of containing quantitative movement information of refugees/migrants into Hungary, Austria and Germany. We present this dataset for a posterior analysis of the 2015 migration crisis or as a basis for creating an automated extraction / prediction system.

DETECTING EVENTS IN EGYPT BASED ON GEO-REFERENCED TWEETS

Migration is a major challenge of many European member states, early preparedness is imperative for target states for multiple reasons such as provision of adequate search and rescue measures. In order to develop early indicators of developing migration flows, we investigated the daily number of geo referenced Tweets in Egypt during the period from October 2013 until March 2014 by using the data handling tool Ubicity. Moreover, we identified certain days where relevant political events have taken place in order to identify possible event triggered changes of the daily number of Tweets. In addition, we extracted the daily number of Tweets in the big cities Cairo and Alexandria. We observed an increase of the daily numbers of Tweets during the period of ostracism of the political party “Muslim Brotherhood”. The largest number of daily Tweets was observed that day, when a rare snowstorm occurred in Egypt. We found good correlation between the number of Tweets in the whole Egypt and those of the two cities Cairo and Alexandria.

An analysis of twitter as a relevant human mobility proxy

GeoInformatica, 2022

During the last years, the analysis of spatio-temporal data extracted from Online Social Networks (OSNs) has become a prominent course of action within the human-mobility mining discipline. Due to the noisy and sparse nature of these data, an important effort has been done on validating these platforms as suitable mobility proxies. However, such a validation has been usually based on the computation of certain features from the raw spatiotemporal trajectories extracted from OSN documents. Hence, there is a scarcity of validation studies that evaluate whether geo-tagged OSN data are able to measure the evolution of the mobility in a region at multiple spatial scales. For that reason, this work proposes a comprehensive comparison of a nation-scale Twitter (TWT) dataset and an official mobility survey from the Spanish National Institute of Statistics. The target time period covers a three-month interval during which Spain was heavily affected by the COVID-19 pandemic. Both feeds have been compared in this context by considering different mobility-related features and spatial scales. The results show that TWT could capture only a limited number features of the latent mobility behaviour of Spain during the study period.

Using Twitter Data to Estimate the Relationship between Short-term Mobility and Long-term Migration

Proceedings of the 2017 ACM on Web Science Conference

Migration estimates are sensitive to de nitions of time interval and duration. For example, when does a tourist become a migrant? As a result, harmonizing across di erent kinds of estimates or data sources can be di cult. Moreover in countries like the United States, that do not have a national registry system, estimates of internal migration typically rely on survey data that can require over a year from data collection to publication. In addition, each survey can ask only a limited set questions about migration (e.g., where did you live a year ago? where did you live ve years ago?). We leverage a sample of geo-referenced Twi er tweets for about 62,000 users, spanning the period between 2010 and 2016, to estimate a series of US internal migration ows under varying time intervals and durations. Our ndings, expressed in terms of 'migration curves', document, for the rst time, the relationships between short-term mobility and long-term migration. e results open new avenues for demographic research. More speci cally, future directions include the use of migration curves to produce probabilistic estimates of long-term migration from short-term (and vice versa) and to nowcast mobility rates at di erent levels of spatial and temporal granularity using a combination of previously published American Community Survey data and up-to-date data from a panel of Twi er users. CCS CONCEPTS •Applied computing → Sociology; •Human-centered computing → HCI theory, concepts and models;

Utilizing Geo-tagged Tweets to Understand Evacuation Dynamics during Emergencies

Companion of the The Web Conference 2018 on The Web Conference 2018 - WWW '18

Hurricane evacuation is a complex process and a better understanding of the evacuation behavior of the coastal residents could be helpful in planning better evacuation policy. Traditionally, various aspects of the household evacuation decisions have been determined by post-evacuation questionnaire surveys, which are usually time-consuming and expensive. Increased activity of users on social media, especially during emergencies, along with the geo-tagging of the posts, provides an opportunity to gain insights into user's decision-making process, as well as to gauge public opinion and activities using the social media data as a supplement to the traditional survey data. This paper leverages the geo-tagged Tweets posted in the New York City (NYC) in wake of Hurricane Sandy to understand the evacuation behavior of the residents. Based on the geo-tagged Tweet locations, we classify the NYC Twitter users into one of the three categories: outside evacuation zone, evacuees, and non-evacuees and examine the types of Tweets posted by each group during different phases of the hurricane. We establish a strong link between the social connectivity with the decision of the users to evacuate or stay. We analyze the geo-tagged Tweets to understand evacuation and return time and evacuation location patterns of evacuees. The analysis presented in this paper could be useful for authorities to plan a better evacuation campaign to minimize the risk to the life of the residents of the emergency hit areas. CCS CONCEPTS • Information systems → Mobile information processing systems; • Networks → Social media networks; • Human-centered computing → Social media;

Blending Noisy Social Media Signals with Traditional Movement Variables to Predict Forced Migration

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Worldwide displacement due to war and conflict is at all-time high. Unfortunately, determining if, when, and where people will move is a complex problem. This paper proposes integrating both publicly available organic data from social media and newspapers with more traditional indicators of forced migration to determine when and where people will move. We combine movement and organic variables with spatial and temporal variation within different Bayesian models and show the viability of our method using a case study involving displacement in Iraq. Our analysis shows that incorporating open-source generated conversation and event variables maintains or improves predictive accuracy over traditional variables alone. This work is an important step toward understanding how to leverage organic big data for societal-scale problems. CCS CONCEPTS • Information systems → Data mining; • Human-centered computing → Social engineering (social sciences);