scholarly journals Problematic Content in Spanish Language Comments in YouTube Videos about Venezuelan Refugees and Migrants

Author(s):  
Luis Aguirre ◽  
Emese Domahidi

On YouTube, we found extensive content relating to the recent Venezuelan refugee movement that mostly affects neighboring countries like Peru and Ecuador. While there are several studies on general hate speech on social media, only a few have focused on the online discussion of the Venezuelan migration crisis representing the Latin American perspective. Here, we analyzed via manual coding and computational text analysis 235,251 comments from 200 YouTube videos (selected according to theoretical criteria) in the Spanish language on the Venezuelan refugee crisis. In our sample, we found a high number of problematic comments in videos on Venezuelan refugees and migrants, of which 32% were offensive comments and 20% were hateful comments. The most common linguistic patterns revealed references to xenophobic, racist, and sexist content, and showed that offensive content and hate speech are not easy to separate. Only a small amount of around 8% of highly active users is responsible for about 40% of the problematic content and these users actively comment on multiple videos, indicating a network structure in our sample. Our results enlighten a much-neglected topic in the discussion about Venezuelan refugees and migrants on YouTube and contribute to an enhanced understanding of online hate speech from a Latin American perspective for better and early detection.

2016 ◽  
Vol 13 (2) ◽  
pp. 307-319 ◽  
Author(s):  
Philip L. Martin

The European Union’s 28 member nations received over 1.2 million asylum seekers in 2015, including 1.1 million in Germany[1] and over 150,000 in Sweden. The US, by comparison, has been receiving 75,000 asylum applications a year. One reason for the upsurge in asylum applicants is that German Chancellor Angela Merkel in August 2015 announced that Syrians could apply for asylum in Germany even if they passed through safe countries en route. The challenges of integrating asylum seekers are becoming clearer, prompting talk of reducing the influx, reforming EU institutions, and integrating migrants.[1] Some 1.1 million foreigners were registered in Germany’s EASY system in 2015, but only 476,500 were able to complete asylum applications because of backlogs in asylum offices.


2019 ◽  
Vol 26 (4) ◽  
pp. 437-456
Author(s):  
María Julia Ochoa Jiménez

Abstract:In Latin America, conflict-of-law norms have not appropriately considered the cultural diversity that exists in their legal systems. However, developments towards the recognition of Indigenous peoples’ human rights, at the international and national levels, impose the task of considering such diversity. In that regard, within the conflict-of-law realm, interpersonal law offers a useful perspective. This article proposes a conflict-of-law rule that can contribute to clarity and legal certainty, offering a sound way of dealing at the national level with Indigenous peoples’ claims for restitution of property with a cultural value for them, which is framed in international instruments on human rights.


2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Gisele Alexandre ◽  
Lylian Rodriguez ◽  
Javier Arece ◽  
José Delgadillo ◽  
Gary Wayne Garcia ◽  
...  

2011 ◽  
Vol 106 (suppl 1) ◽  
pp. 91-104 ◽  
Author(s):  
Juan Pablo Quintero ◽  
André Machado Siqueira ◽  
Alberto Tobón ◽  
Silvia Blair ◽  
Alberto Moreno ◽  
...  

Author(s):  
Noman Ashraf ◽  
Abid Rafiq ◽  
Sabur Butt ◽  
Hafiz Muhammad Faisal Shehzad ◽  
Grigori Sidorov ◽  
...  

On YouTube, billions of videos are watched online and millions of short messages are posted each day. YouTube along with other social networking sites are used by individuals and extremist groups for spreading hatred among users. In this paper, we consider religion as the most targeted domain for spreading hate speech among people of different religions. We present a methodology for the detection of religion-based hate videos on YouTube. Messages posted on YouTube videos generally express the opinions of users’ related to that video. We provide a novel dataset for religious hate speech detection on Youtube comments. The proposed methodology applies data mining techniques on extracted comments from religious videos in order to filter religion-oriented messages and detect those videos which are used for spreading hate. The supervised learning algorithms: Support Vector Machine (SVM), Logistic Regression (LR), and k-Nearest Neighbor (k-NN) are used for baseline results.


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