<|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:"Social sensing of flood impacts in India: A case study of Kerala 2018."
Abstract:"Flooding is a major hazard that is responsible for substantial damage and risks to human health worldwide. The 2018 flood event in Kerala, India, killed 433 people and displaced more than 1 million people from their homes. Accurate and timely information can help mitigate the impacts of flooding through better preparedness (e.g. forecasting of flood impacts) and situational awareness (e.g. more effective civil response and relief). However, good information on flood impacts is difficult to source; governmental records are often slow and costly to produce, while insurance claim data is commercially sensitive and does not exist for many vulnerable populations. Here we explore social sensing - the systematic collection and analysis of social media data to observe real-world events - as a method to locate and characterise the impacts (social, economic and other) of the 2018 Kerala Floods. Data is collected from two social media platforms, Telegram and Twitter, as well as a citizen-produced relief coordination web application, Kerala Rescue, and a government flood damage database, Rebuild Kerala. After careful filtering to retain only flood-related social media posts, content is analysed to map the extent of flood impacts and to identify different kinds of impact (e.g. requests for help, reports of medical or other issues). Maps of flood impacts derived from Telegram and Twitter both show substantial agreement with Kerala Rescue and the damage reports from Rebuild Kerala. Social media content also detects similar kinds of impact to those reported through the more structured Kerala Rescue application. Overall, the results suggest that social sensing can be an effective source of flood impact information that produces outputs in broad agreement with government sources. Furthermore, social sensing information can be produced in near real-time, whereas government records take several months to produce. This suggests that social sensing may be a useful data source to guide decisions around flood relief and emergency response.."
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 = [1,2];
Q3 = [2];
Q4 = [26];
Q5 = [1];
Q6 = [1].
Social sensing of flood impacts in India: A case study of Kerala 2018
Flooding is a major hazard that is responsible for substantial damage and risks to human health worldwide. The 2018 flood event in Kerala, India, killed 433 people and displaced more than 1 million people from their homes. Accurate and timely information can help mitigate the impacts of flooding through better preparedness (e.g. forecasting of flood impacts) and situational awareness (e.g. more effective civil response and relief). However, good information on flood impacts is difficult to source; governmental records are often slow and costly to produce, while insurance claim data is commercially sensitive and does not exist for many vulnerable populations. Here we explore social sensing - the systematic collection and analysis of social media data to observe real-world events - as a method to locate and characterise the impacts (social, economic and other) of the 2018 Kerala Floods. Data is collected from two social media platforms, Telegram and Twitter, as well as a citizen-produced relief coordination web application, Kerala Rescue, and a government flood damage database, Rebuild Kerala. After careful filtering to retain only flood-related social media posts, content is analysed to map the extent of flood impacts and to identify different kinds of impact (e.g. requests for help, reports of medical or other issues). Maps of flood impacts derived from Telegram and Twitter both show substantial agreement with Kerala Rescue and the damage reports from Rebuild Kerala. Social media content also detects similar kinds of impact to those reported through the more structured Kerala Rescue application. Overall, the results suggest that social sensing can be an effective source of flood impact information that produces outputs in broad agreement with government sources. Furthermore, social sensing information can be produced in near real-time, whereas government records take several months to produce. This suggests that social sensing may be a useful data source to guide decisions around flood relief and emergency response.