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

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TLDR
The results are a good basis to use communication patterns as future key indicator for migration analysis, and the natural disasters identified in Japan do not show a clear relationship with the changes in the degree of use of the social media tool Twitter.
Abstract
Migration is a major challenge for the European Union, resulting in early preparedness being an imperative for target states and their stakeholders such as border police forces. This preparedness is necessary for multiple reasons, including the provision of adequate search and rescue measures. To support preparedness, there is a need for early indicators for detection of developing migratory push-factors related to imminent migration flows. To address this need, we have investigated the daily number of geo-referenced Tweets in three regions of Ukraine and the whole of Japan from August 2014 until October 2014. This analysis was done by using the data handling tool Ubicity. Additionally, we have identified days when relevant natural, civil or political events took place in order to identify possible event triggered changes of the daily number of Tweets. In all the examined Ukrainian regions a considerable increase in the number of daily Tweets was observed for the election day of a new parliament. Furthermore, we identified a significant decrease in the number of daily Tweets for the Crimea for the whole examined period which could be related to the political changes that took place. The natural disasters identified in Japan do not show a clear relationship with the changes in the degree of use of the social media tool Twitter. The results are a good basis to use communication patterns as future key indicator for migration analysis.

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Journal ArticleDOI

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TL;DR: In this article, the authors discuss the importance of leveraging mobile phone data as an alternative data source to gather precious and previously unavailable insights on various aspects of migration, and highlight pending challenges that would need to be addressed before we can effectively benefit from the availability of mobile data to help make better decisions that would ultimately improve millions of people's lives.
Posted Content

Loglinear model selection and human mobility

TL;DR: In this paper, the authors developed a new algorithm for selecting graphical loglinear models that is suitable for analyzing hyper-sparse contingency tables, which can be used to represent patterns of human mobility.
References
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Event Detection in Twitter using Aggressive Filtering and Hierarchical Tweet Clustering

TL;DR: It is shown that aggressive filtering of tweets based on length and structure, combined with hierarchical clustering of tweets and ranking of the resulting clusters, achieves encouraging results.
Proceedings ArticleDOI

Less After-the-Fact: Investigative visual analysis of events from streaming twitter

TL;DR: This paper presents the GTAC, a Geo and Temporal Association Creator (GTAC) which extracts structured representations of events from the Twitter stream and supports event-level investigative analysis of social media data through interactively visualizing the event indicators.

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TL;DR: A holistic view of crowdwork on social media platforms as collective intelligence manifested within a global cognitive system is concluded, explaining how information is processed through a variety of different activities at different layers within a complex information space that includes crowdworkers, virtual organizations, and social media sites that host both the information and the information processing.

Avalanche: Prepare, manage, and understand crisis situations using social media analytics.

TL;DR: The vision for a Social Media-ready command and control center for event detection and a short analysis of tweets issued in NYC during Hurricane Sandy in late October 2012 are described.