<|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:"Improving Traffic Load Distribution Fairness in Mobile Social Networks."
Abstract:"Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most social nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node's sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node's periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.."
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 = [0];
Q2 = [];
Q3 = [];
Q4 = [];
Q5 = [];
Q6 = [1].
Improving Traffic Load Distribution Fairness in Mobile Social Networks
Mobile social networks suffer from an unbalanced traffic load distribution due to the heterogeneity in mobility of nodes (humans) in the network. A few nodes in these networks are highly mobile, and the proposed social-based routing algorithms are likely to choose these most social nodes as the best message relays. Finally, this could lead to inequitable traffic load distribution and resource utilisation, such as faster battery drain and/or storage consumption of the most (socially) popular nodes. We propose a framework called Traffic Load Distribution Aware (TraLDA) to improve traffic load balancing across network nodes. We present a novel method for calculating node popularity which takes into account both node inherent and social-relations popularity. The former is purely determined by the node's sociability level in the network, and in TraLDA is computed using the Kalman prediction which considers the node's periodicity behaviour. However, the latter takes the benefit of interactions with more popular neighbours (acquaintances) to boost the popularity of lower (social) level nodes. Using extensive simulations in the Opportunistic Network Environment (ONE) driven by real human mobility scenarios, we show that our proposed strategy enhances the traffic load distribution fairness of the classical, yet popular social-aware routing algorithms BubbleRap and SimBet without negatively impacting the overall delivery performance.