<|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:"Identifying On-Site Users for Social Events: Mobility, Content, and Social Relationship."
Abstract:"The wide spread use of social network services, especially location based services, has transformed social networks into an important information source of real-world events. Many event detection systems using geo-tagged posts from social networks have been developed in recent years. Besides detecting real-world events, it is also desirable for government officials, news media, and police, etc., to identify on-site users of an event, from whom we could gather valuable information regarding the process of events and investigate suspects when an event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns and the low probability of users sharing their location information, it is difficult to identify on-site users while a social event unfolds, and research work in this area is still in its infancy. In this paper, we propose a Fused fEature Gaussian prOcess Regression (FEGOR) model, which exploits three influential factors in social networks for on-site user identification: mobility influence, content similarity, and social relationship. By integrating these factors, we are able to estimate the distance between a user and a social event even when the user's location profile is unknown, thus identify on-site users. Experiments on a real-world Twitter dataset demonstrate the effectiveness of our model, achieving a minimum mean absolute error of 1.7km and outperforming state-of-the-art methods.."
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 = [0];
Q3 = [0];
Q4 = [0];
Q5 = [1];
Q6 = [1].
Identifying On-Site Users for Social Events: Mobility, Content, and Social Relationship
The wide spread use of social network services, especially location based services, has transformed social networks into an important information source of real-world events. Many event detection systems using geo-tagged posts from social networks have been developed in recent years. Besides detecting real-world events, it is also desirable for government officials, news media, and police, etc., to identify on-site users of an event, from whom we could gather valuable information regarding the process of events and investigate suspects when an event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns and the low probability of users sharing their location information, it is difficult to identify on-site users while a social event unfolds, and research work in this area is still in its infancy. In this paper, we propose a Fused fEature Gaussian prOcess Regression (FEGOR) model, which exploits three influential factors in social networks for on-site user identification: mobility influence, content similarity, and social relationship. By integrating these factors, we are able to estimate the distance between a user and a social event even when the user's location profile is unknown, thus identify on-site users. Experiments on a real-world Twitter dataset demonstrate the effectiveness of our model, achieving a minimum mean absolute error of 1.7km and outperforming state-of-the-art methods.