<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Migrant Racialization on Twitter during a border and a pandemic crisis."
Abstract:"This work examines how the outbreak of the COVID-19 pandemic reshaped the migration debate on Twitter. Through co-hashtag network analysis, time-frequency and content analysis, it shows that the pandemic was related with positive (humanitarian) and negative (threat) stances about migration. The positive side focused on the need to protect refugees stranded at camps in Greece from COVID-19. The negative focused on the Greek-Turkish land-border crisis (Evros crisis), using COVID-19 to reinforce migrants as racialized others. These findings fit the problematization of positive and negative migrant representations in the Global north as Eurocentric. In the case of camps, refugees fit well within the victim/helpless frame, justifying humanitarianism, this time on health grounds. Regarding the border crisis, refugees also fit the Eurocentric frame of violent/male/inferior other who could spread a deadly virus. Overall, COVID-19 intertwined with migration in Twitter debates, reinforcing the racialized, Eurocentric representational field on migrants from the Global south.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [4,5];
Q3 = [1];
Q4 = [];
Q5 = [1];
Q6 = [1].
Migrant Racialization on Twitter during a border and a pandemic crisis
This work examines how the outbreak of the COVID-19 pandemic reshaped the migration debate on Twitter. Through co-hashtag network analysis, time-frequency and content analysis, it shows that the pandemic was related with positive (humanitarian) and negative (threat) stances about migration. The positive side focused on the need to protect refugees stranded at camps in Greece from COVID-19. The negative focused on the Greek-Turkish land-border crisis (Evros crisis), using COVID-19 to reinforce migrants as racialized others. These findings fit the problematization of positive and negative migrant representations in the Global north as Eurocentric. In the case of camps, refugees fit well within the victim/helpless frame, justifying humanitarianism, this time on health grounds. Regarding the border crisis, refugees also fit the Eurocentric frame of violent/male/inferior other who could spread a deadly virus. Overall, COVID-19 intertwined with migration in Twitter debates, reinforcing the racialized, Eurocentric representational field on migrants from the Global south.