Social fMRI: Investigating and shaping social mechanisms in the real world

Aharony, Nadav; Pan, Wei; Ip, Cory; Khayal, Inas S.; Pentland, Alex

doi:10.1016/j.pmcj.2011.09.004

Cited by 439 publications

(438 citation statements)

References 24 publications

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“…These techniques can be used to detect social relations [1,13], evolving behavioral trends [7,24], mobility patterns [15], environmental hazards [18,25], socio-economical properties [14], and various security related features [4,5].…”

Section: Related Workmentioning

confidence: 99%

Detecting Anomalous Behaviors Using Structural Properties of Social Networks

Altshuler

Fire²,

Shmueli

et al. 2013

Social Computing, Behavioral-Cultural Modeling and Prediction

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Abstract. In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous "real world event". We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation.

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Section: Related Workmentioning

confidence: 99%

Detecting Anomalous Behaviors Using Structural Properties of Social Networks

Altshuler

Fire²,

Shmueli

et al. 2013

Social Computing, Behavioral-Cultural Modeling and Prediction

Self Cite

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“…We assume that the movement of the agents in the network are random 1 . Hence : The following Lemma produces the probability that two arbitrarily selected vertices would have degrees which differ in more than a certain threshold :…”

Section: Theoremmentioning

confidence: 99%

Trends Prediction Using Social Diffusion Models

Altshuler

Pan

Pentland

2012

Lecture Notes in Computer Science

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Abstract. The importance of the ability to predict trends in social media has been growing rapidly in the past few years with the growing dominance of social media in our everyday's life. Whereas many works focus on the detection of anomalies in networks, there exist little theoretical work on the prediction of the likelihood of anomalous network pattern to globally spread and become "trends". In this work we present an analytic model for the social diffusion dynamics of spreading network patterns. Our proposed method is based on information diffusion models, and is capable of predicting future trends based on the analysis of past social interactions between the community's members. We present an analytic lower bound for the probability that emerging trends would successfully spread through the network. We demonstrate our model using two comprehensive social datasets -the Friends and Family experiment that was held in MIT for over a year, where the complete activity of 140 users was analyzed, and a financial dataset containing the complete activities of over 1.5 million members of the eToro social trading community.

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“…On the other hand, such studies give us an unprecedented window into the lives of individuals and entire communities. As demonstrated in [2], conducting user-centric data collection provides a wide range of signals on participants and allows us to directly gather ground-truth and ancillary information such as demographic information, physiological and psychological information, self-perceptions and attitudes and a variety of additional data types gathered via surveys, interviews, and interactions with the subject population. These types of information are usually not available for datasets "donated" to researchers by mobile service providers and other commercial entities.…”

Section: Introductionmentioning

confidence: 99%

“…Some studies are all encompassing and broadly defined living laboratory or "social observatory" types of studies. These include the Reality Mining study [7], the Social Evolution study [10], and the Friends and Family Study [2]. There are also more targeted studies, focusing on specific research questions or data types, like physical activity levels or environmental impacts.…”

Section: Introductionmentioning

confidence: 99%

“…To carry out our research we use the Friends and Family dataset which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community and collected over the course of a year [2], as well as self-reported personal and socialtie information. We build voting classifiers for predicting personal properties such as nationality and religion, based on the participants' SMS messages graph's topology.…”

Section: Introductionmentioning

confidence: 99%

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