<|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:"Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter."
Abstract:"In this work, we analyse mobile phone variation before and after the 2016 Central Italy earthquake in the affected areas, using Twitter and public reconstruction works data. We create three models and show that Twitter data and the related sentiment on the earthquake, as well as the distribution of emergency houses, can contribute to explaining population variations. Our final Generalised Poisson regression model explains more than 80% of the variance of the population's variation based on the percentage of negative polarity tweets, the number of emergency houses, the number of negative tweets on the earthquake weighted by the number of residents, number of tweets posted on the earthquake anniversary, the distance from the epicentre and several variables related to public reconstruction works (e.g. school, public housing, hydrological disruption, viability). We found that sentiment on the emergency house can be a proxy for population variation because people who live there did not displace from the crater area. The number of tweets posted during the anniversary day can, instead, indicate negative population variation because the higher the number of tweets, the more people can feel nostalgic after having relocated.."
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 = [];
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Q4 = [];
Q5 = [];
Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:
Q1 = [1];
Q2 = [2];
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Q4 = [23];
Q5 = [1];
Q6 = [1].
Explaining population variation after the 2016 Central Italy earthquake using Call Data Records and Twitter
In this work, we analyse mobile phone variation before and after the 2016 Central Italy earthquake in the affected areas, using Twitter and public reconstruction works data. We create three models and show that Twitter data and the related sentiment on the earthquake, as well as the distribution of emergency houses, can contribute to explaining population variations. Our final Generalised Poisson regression model explains more than 80% of the variance of the population's variation based on the percentage of negative polarity tweets, the number of emergency houses, the number of negative tweets on the earthquake weighted by the number of residents, number of tweets posted on the earthquake anniversary, the distance from the epicentre and several variables related to public reconstruction works (e.g. school, public housing, hydrological disruption, viability). We found that sentiment on the emergency house can be a proxy for population variation because people who live there did not displace from the crater area. The number of tweets posted during the anniversary day can, instead, indicate negative population variation because the higher the number of tweets, the more people can feel nostalgic after having relocated.