1,339 research outputs found
Investigating Churn in Physical Activity Challenges: Evidence from a U.S. Online Social Network
Physical activities have been found to be positively contagious, as active exercisers tend to motivate their friends to do more exercise. However, it is not clearly understood if inactive exercising behaviors are also socially contagious. As insufficient physical activity is a huge threat to people's health, understanding the potential negative contagion in physical activities is crucial. We approach this problem by studying the effect of individuals' churn of the online physical activity challenges relying on the physical activity and a large social network data from a renowned U.S. fitness platform. The underexplored online physical activity challenges provide a natural setup to measure churn and opportunities to study the contagion heterogeneities. Consistent with previous findings, we confirm that physical activity churn is socially contagious. Interestingly, unlike the inside-out positive contagion, our analyses reveal that the contagion of churn happens outside-in on the social network. Implications of such findings are discussed
The Role of Peer Influence in Churn in Wireless Networks
Subscriber churn remains a top challenge for wireless carriers. These
carriers need to understand the determinants of churn to confidently apply
effective retention strategies to ensure their profitability and growth. In
this paper, we look at the effect of peer influence on churn and we try to
disentangle it from other effects that drive simultaneous churn across friends
but that do not relate to peer influence. We analyze a random sample of roughly
10 thousand subscribers from large dataset from a major wireless carrier over a
period of 10 months. We apply survival models and generalized propensity score
to identify the role of peer influence. We show that the propensity to churn
increases when friends do and that it increases more when many strong friends
churn. Therefore, our results suggest that churn managers should consider
strategies aimed at preventing group churn. We also show that survival models
fail to disentangle homophily from peer influence over-estimating the effect of
peer influence.Comment: Accepted in Seventh ASE International Conference on Social Computing
(Socialcom 2014), Best Paper Award Winne
From continuous to discontinuous transitions in social diffusion
Models of social diffusion reflect processes of how new products, ideas or
behaviors are adopted in a population. These models typically lead to a
continuous or a discontinuous phase transition of the number of adopters as a
function of a control parameter. We explore a simple model of social adoption
where the agents can be in two states, either adopters or non-adopters, and can
switch between these two states interacting with other agents through a
network. The probability of an agent to switch from non-adopter to adopter
depends on the number of adopters in her network neighborhood, the adoption
threshold and the adoption coefficient , two parameters defining a Hill
function. In contrast, the transition from adopter to non-adopter is
spontaneous at a certain rate . In a mean-field approach, we derive the
governing ordinary differential equations and show that the nature of the
transition between the global non-adoption and global adoption regimes depends
mostly on the balance between the probability to adopt with one and two
adopters. The transition changes from continuous, via a transcritical
bifurcation, to discontinuous, via a combination of a saddle-node and a
transcritical bifurcation, through a supercritical pitchfork bifurcation. We
characterize the full parameter space. Finally, we compare our analytical
results with Montecarlo simulations on annealed and quenched degree regular
networks, showing a better agreement for the annealed case. Our results show
how a simple model is able to capture two seemingly very different types of
transitions, i.e., continuous and discontinuous and thus unifies underlying
dynamics for different systems. Furthermore the form of the adoption
probability used here is based on empirical measurements.Comment: 7 pages, 3 figure
The role of geography in the complex diffusion of innovations
The urban-rural divide is increasing in modern societies calling for
geographical extensions of social influence modelling. Improved understanding
of innovation diffusion across locations and through social connections can
provide us with new insights into the spread of information, technological
progress and economic development. In this work, we analyze the spatial
adoption dynamics of iWiW, an Online Social Network (OSN) in Hungary and
uncover empirical features about the spatial adoption in social networks.
During its entire life cycle from 2002 to 2012, iWiW reached up to 300 million
friendship ties of 3 million users. We find that the number of adopters as a
function of town population follows a scaling law that reveals a strongly
concentrated early adoption in large towns and a less concentrated late
adoption. We also discover a strengthening distance decay of spread over the
life-cycle indicating high fraction of distant diffusion in early stages but
the dominance of local diffusion in late stages. The spreading process is
modelled within the Bass diffusion framework that enables us to compare the
differential equation version with an agent-based version of the model run on
the empirical network. Although both models can capture the macro trend of
adoption, they have limited capacity to describe the observed trends of urban
scaling and distance decay. We find, however that incorporating adoption
thresholds, defined by the fraction of social connections that adopt a
technology before the individual adopts, improves the network model fit to the
urban scaling of early adopters. Controlling for the threshold distribution
enables us to eliminate the bias induced by local network structure on
predicting local adoption peaks. Finally, we show that geographical features
such as distance from the innovation origin and town size influence prediction
of adoption peak at local scales.Comment: 21 pages, 11 figures, 4 table
Identifying influencers in a social network : the value of real referral data
Individuals influence each other through social interactions and marketers aim to leverage this interpersonal influence to attract new customers. It still remains a challenge to identify those customers in a social network that have the most influence on their social connections. A common approach to the influence maximization problem is to simulate influence cascades through the network based on the existence of links in the network using diffusion models. Our study contributes to the literature by evaluating these principles using real-life referral behaviour data. A new ranking metric, called Referral Rank, is introduced that builds on the game theoretic concept of the Shapley value for assigning each individual in the network a value that reflects the likelihood of referring new customers. We also explore whether these methods can be further improved by looking beyond the one-hop neighbourhood of the influencers. Experiments on a large telecommunication data set and referral data set demonstrate that using traditional simulation based methods to identify influencers in a social network can lead to suboptimal decisions as the results overestimate actual referral cascades. We also find that looking at the influence of the two-hop neighbours of the customers improves the influence spread and product adoption. Our findings suggest that companies can take two actions to improve their decision support system for identifying influential customers: (1) improve the data by incorporating data that reflects the actual referral behaviour of the customers or (2) extend the method by looking at the influence of the connections in the two-hop neighbourhood of the customers
Gadget Factories Charge Up China’s Activism
This document is part of a digital collection provided by the Martin P. Catherwood Library, ILR School, Cornell University, pertaining to the effects of globalization on the workplace worldwide. Special emphasis is placed on labor rights, working conditions, labor market changes, and union organizing.CLW_2011_Report_China_gadget_factories.pdf: 17 downloads, before Oct. 1, 2020
The effect of friends’ churn on consumer behavior in mobile networks
We study how consumers decide which tariff plan to choose and whether to churn when their friends churn in the mobile industry. We develop a theoretical model showing conditions under which users remain with their carrier and conditions under which they churn when their friends do. We then use a large and rich anonymized longitudinal panel of call detailed records to characterize the consumers’ path to death with unprecedented level of detail. We explore the structure of the network inferred from these data to derive instruments for friends’ churn, which is typically endogenous in network settings. This allows us to econometrically identify the effect of peer influence in our setting. On average, we find that each additional friend that churns increases the monthly churn rate by 0.06 percent. The observed monthly churn rate across our dataset is 2.15 percent. We also find that firms introducing the pre-paid tariff plans that charge the same price to call users inside and outside the carrier help retain consumers that would otherwise churn. In our setting, without this tariff plan the monthly churn rate could have been as high as 8.09 percent. We perform a number of robustness checks, in particular to how we define friends in the social graph, and show that our results remain unchanged. Our paper shows that the traditional definition of customer lifetime value underestimates the value of consumers and, in particular, that of consumers with more friends due to the effect of contagious churn and, therefore, managers should actively take into account the structure of the social network when prioritizing whom to target during retention campaigns.info:eu-repo/semantics/acceptedVersio
Beautiful and damned. Combined effect of content quality and social ties on user engagement
User participation in online communities is driven by the intertwinement of
the social network structure with the crowd-generated content that flows along
its links. These aspects are rarely explored jointly and at scale. By looking
at how users generate and access pictures of varying beauty on Flickr, we
investigate how the production of quality impacts the dynamics of online social
systems. We develop a deep learning computer vision model to score images
according to their aesthetic value and we validate its output through
crowdsourcing. By applying it to over 15B Flickr photos, we study for the first
time how image beauty is distributed over a large-scale social system.
Beautiful images are evenly distributed in the network, although only a small
core of people get social recognition for them. To study the impact of exposure
to quality on user engagement, we set up matching experiments aimed at
detecting causality from observational data. Exposure to beauty is
double-edged: following people who produce high-quality content increases one's
probability of uploading better photos; however, an excessive imbalance between
the quality generated by a user and the user's neighbors leads to a decline in
engagement. Our analysis has practical implications for improving link
recommender systems.Comment: 13 pages, 12 figures, final version published in IEEE Transactions on
Knowledge and Data Engineering (Volume: PP, Issue: 99
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