Combining Twitter and Mobile Phone Data to Observe Border-Rush: The Turkish-European Border Opening
- URL: http://arxiv.org/abs/2405.12642v2
- Date: Wed, 22 May 2024 07:59:01 GMT
- Title: Combining Twitter and Mobile Phone Data to Observe Border-Rush: The Turkish-European Border Opening
- Authors: Carlos Arcila Calderón, Bilgeçağ Aydoğdu, Tuba Bircan, Bünyamin Gündüz, Onur Önes, Albert Ali Salah, Alina Sîrbu,
- Abstract summary: Following Turkey's 2020 decision to revoke border controls, many individuals journeyed towards the Greek, Bulgarian, and Turkish borders.
However, the lack of verifiable statistics on irregular migration and discrepancies between media reports and actual migration patterns require further exploration.
This study is to bridge this knowledge gap by harnessing novel data sources, specifically mobile phone and Twitter data.
- Score: 2.5693085674985117
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Following Turkey's 2020 decision to revoke border controls, many individuals journeyed towards the Greek, Bulgarian, and Turkish borders. However, the lack of verifiable statistics on irregular migration and discrepancies between media reports and actual migration patterns require further exploration. The objective of this study is to bridge this knowledge gap by harnessing novel data sources, specifically mobile phone and Twitter data, to construct estimators of cross-border mobility and to cultivate a qualitative comprehension of the unfolding events. By employing a migration diplomacy framework, we analyse emergent mobility patterns at the border. Our findings demonstrate the potential of mobile phone data for quantitative metrics and Twitter data for qualitative understanding. We underscore the ethical implications of leveraging Big Data, particularly considering the vulnerability of the population under study. This underscores the imperative for exhaustive research into the socio-political facets of human mobility, with the aim of discerning the potentialities, limitations, and risks inherent in these data sources and their integration. This scholarly endeavour contributes to a more nuanced understanding of migration dynamics and paves the way for the formulation of regulations that preclude misuse and oppressive surveillance, thereby ensuring a more accurate representation of migration realities.
Related papers
- Model Inversion Attacks: A Survey of Approaches and Countermeasures [59.986922963781]
Recently, a new type of privacy attack, the model inversion attacks (MIAs), aims to extract sensitive features of private data for training.
Despite the significance, there is a lack of systematic studies that provide a comprehensive overview and deeper insights into MIAs.
This survey aims to summarize up-to-date MIA methods in both attacks and defenses.
arXiv Detail & Related papers (2024-11-15T08:09:28Z) - Exploring Federated Learning Dynamics for Black-and-White-Box DNN Traitor Tracing [49.1574468325115]
This paper explores the adaptation of black-and-white traitor tracing watermarking to Federated Learning.
Results show that collusion-resistant traitor tracing, identifying all data-owners involved in a suspected leak, is feasible in an FL framework, even in early stages of training.
arXiv Detail & Related papers (2024-07-02T09:54:35Z) - Language Models Can Reduce Asymmetry in Information Markets [100.38786498942702]
We introduce an open-source simulated digital marketplace where intelligent agents, powered by language models, buy and sell information on behalf of external participants.
The central mechanism enabling this marketplace is the agents' dual capabilities: they have the capacity to assess the quality of privileged information but also come equipped with the ability to forget.
To perform well, agents must make rational decisions, strategically explore the marketplace through generated sub-queries, and synthesize answers from purchased information.
arXiv Detail & Related papers (2024-03-21T14:48:37Z) - The diaspora model for human migration [0.07852714805965527]
Existing models primarily rely on population size and travel distance to explain flow fluctuations.
We propose the diaspora model of migration, incorporating intensity (the number of people moving to a country) and assortativity (the destination within the country)
Our model considers only the existing diaspora sizes in the destination country, influencing the probability of migrants selecting a specific residence.
arXiv Detail & Related papers (2023-09-06T15:17:53Z) - Bias and Fairness in Large Language Models: A Survey [73.87651986156006]
We present a comprehensive survey of bias evaluation and mitigation techniques for large language models (LLMs)
We first consolidate, formalize, and expand notions of social bias and fairness in natural language processing.
We then unify the literature by proposing three intuitive, two for bias evaluation, and one for mitigation.
arXiv Detail & Related papers (2023-09-02T00:32:55Z) - MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations
and their Driving Factors [1.6973426830397942]
This study is the analysis of social media platform to quantify public attitudes towards migrations.
The tweets spanning from 2013 to Jul-2021 in the European countries which are hosts to immigrants are collected.
The external databases are used to identify the potential social and economic factors causing negative attitudes of the people about migration.
arXiv Detail & Related papers (2021-08-17T12:50:39Z) - Leveraging Mobile Phone Data for Migration Flows [5.0161988361764775]
Statistics on migration flows are often derived from census data, which suffer from intrinsic limitations.
Alternative data sources, such as surveys and field observations, also suffer from reliability, costs, and scale limitations.
The ubiquity of mobile phones enables an accurate and efficient collection of up-to-date data related to migration.
arXiv Detail & Related papers (2021-05-31T13:41:47Z) - Explainable Patterns: Going from Findings to Insights to Support Data
Analytics Democratization [60.18814584837969]
We present Explainable Patterns (ExPatt), a new framework to support lay users in exploring and creating data storytellings.
ExPatt automatically generates plausible explanations for observed or selected findings using an external (textual) source of information.
arXiv Detail & Related papers (2021-01-19T16:13:44Z) - Unsupervised embedding of trajectories captures the latent structure of
scientific migration [4.028844692958469]
We show the ability of the model word2vec to encode nuanced relationships between discrete locations from migration trajectories.
We show that the power of word2vec to encode migration patterns stems from its mathematical equivalence with the gravity model of mobility.
Using techniques that leverage its semantic structure, we demonstrate that embeddings can learn the rich structure that underpins scientific migration.
arXiv Detail & Related papers (2020-12-04T18:58:41Z) - Forecasting asylum-related migration flows with machine learning and
data at scale [0.0]
We show that adaptive machine learning algorithms can effectively forecast asylum-related migration flows.
We exploit three tiers of data - geolocated events and internet searches in countries of origin, detections of irregular crossings at the EU border, and asylum recognition rates in countries of destination.
arXiv Detail & Related papers (2020-11-09T11:31:17Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.