Research On Evolving And Transparency Of Social Networks Based On Crowdsourcing Platform LineMe

With the rapid development of Internet,we are entering the age of big data and cloud computing.Not only did the huge amount of digital data accelerate the speed of disruptive innovations in many scientific areas,but had changed the way we tackling problems traditionally.Data is knowledge,data is also the sharpest sword to exploit the possibilities in the future.Social science,as a systematic and long standing category to understand the social behaviours of individuals within the society,has achieved a great deal of interesting and instructive conclusions relating to individuals and social phenomenons.Social science is showing vitality when getting rid of the traditional data-collection methods which are based on questionnaires.However,it is far from enough to reveal the nature of the society deeply and rigorously.Restricted by technologies and experiments,the data we are mining for knowledge is full of biases and noises.The debate over whether such data sets could fully reflect our real social connections is rising.It is imperative for us to develop new tools and generate new ideas to break through the limit of the data which will lay firm foundations for exploring the mysteries of the modern society.As an efficiency scientific method taking advantage of collective intelligence,crowdsourcing has been attracting much more attention from the public.We can achieve high quality research data under controllable costs while researchers have already gain previous experiences through crowdsourcing experiments.In this paper,we will combine crowdsourcing with social network data to collect social ties of individuals in offline scenes through the platform called LineMe which was designed and developed by us.We performed a crowdsourcing experiment following 155 participants lasting for five weeks.We were to discuss the possibilities of social network data-collection,network evolving and the problem of tie strength.First of all,we analyze the evolvement of the friend network.We find that different attributes of the network vary differently during the evolvement process,and that shared friends,which represents the network structure,indeed influence the formation of social ties.In addition,with crowdsourcing,we are able to choose a small part of the individuals in the society to rebuild the connections of nearly the whole network.Less cost than ever,but still high quality and amount of network data.These individuals are with high degrees and clustered together in the center of the network.They have comprehensive perception of the social context around and possess much more information of the network.Moreover,although the majority of the ties can be recovered by crowdsourcing,we still find covert links in the data which are the blind spots in the network.The individuals have a limited capability of perception of ties of which the perceptible radius is within two degrees.Ultimately,we also propose a novel and objective dimension of tie strength called Knowable Degree,which quantitates the intimacy between two individuals based on how much other social members perceive the tie.Furthermore,in contrast to the belief that weak ties are bridge links between communities,we find there exists critical links with high tie strength among communities maintaining the network integrity.