1,152 research outputs found
Inferring the strength of social ties: a community-driven approach
Online social networks are growing and becoming denser. The social
connections of a given person may have very high variability: from close
friends and relatives to acquaintances to people who hardly know. Inferring the
strength of social ties is an important ingredient for modeling the interaction
of users in a network and understanding their behavior. Furthermore, the
problem has applications in computational social science, viral marketing, and
people recommendation.
In this paper we study the problem of inferring the strength of social ties
in a given network. Our work is motivated by a recent approach [27], which
leverages the strong triadic closure (STC) principle, a hypothesis rooted in
social psychology [13]. To guide our inference process, in addition to the
network structure, we also consider as input a collection of tight communities.
Those are sets of vertices that we expect to be connected via strong ties. Such
communities appear in different situations, e.g., when being part of a
community implies a strong connection to one of the existing members.
We consider two related problem formalizations that reflect the assumptions
of our setting: small number of STC violations and strong-tie connectivity in
the input communities. We show that both problem formulations are NP-hard. We
also show that one problem formulation is hard to approximate, while for the
second we develop an algorithm with approximation guarantee. We validate the
proposed method on real-world datasets by comparing with baselines that
optimize STC violations and community connectivity separately
Intertwined Viral Marketing through Online Social Networks
Traditional viral marketing problems aim at selecting a subset of seed users
for one single product to maximize its awareness in social networks. However,
in real scenarios, multiple products can be promoted in social networks at the
same time. At the product level, the relationships among these products can be
quite intertwined, e.g., competing, complementary and independent. In this
paper, we will study the "interTwined Influence Maximization" (i.e., TIM)
problem for one product that we target on in online social networks, where
multiple other competing/complementary/independent products are being promoted
simultaneously. The TIM problem is very challenging to solve due to (1) few
existing models can handle the intertwined diffusion procedure of multiple
products concurrently, and (2) optimal seed user selection for the target
product may depend on other products' marketing strategies a lot. To address
the TIM problem, a unified greedy framework TIER (interTwined Influence
EstimatoR) is proposed in this paper. Extensive experiments conducted on four
different types of real-world social networks demonstrate that TIER can
outperform all the comparison methods with significant advantages in solving
the TIM problem.Comment: 11 pages, 5 figures, Accepted by ASONAM 201
Social Influence in Social Advertising: Evidence from Field Experiments
Social advertising uses information about consumers' peers, including peer
affiliations with a brand, product, organization, etc., to target ads and
contextualize their display. This approach can increase ad efficacy for two
main reasons: peers' affiliations reflect unobserved consumer characteristics,
which are correlated along the social network; and the inclusion of social cues
(i.e., peers' association with a brand) alongside ads affect responses via
social influence processes. For these reasons, responses may be increased when
multiple social signals are presented with ads, and when ads are affiliated
with peers who are strong, rather than weak, ties.
We conduct two very large field experiments that identify the effect of
social cues on consumer responses to ads, measured in terms of ad clicks and
the formation of connections with the advertised entity. In the first
experiment, we randomize the number of social cues present in word-of-mouth
advertising, and measure how responses increase as a function of the number of
cues. The second experiment examines the effect of augmenting traditional ad
units with a minimal social cue (i.e., displaying a peer's affiliation below an
ad in light grey text). On average, this cue causes significant increases in ad
performance. Using a measurement of tie strength based on the total amount of
communication between subjects and their peers, we show that these influence
effects are greatest for strong ties. Our work has implications for ad
optimization, user interface design, and central questions in social science
research.Comment: 16 pages, 8 figures, ACM EC 201
Using Social Media to Predict the Future: A Systematic Literature Review
Social media (SM) data provides a vast record of humanity's everyday
thoughts, feelings, and actions at a resolution previously unimaginable.
Because user behavior on SM is a reflection of events in the real world,
researchers have realized they can use SM in order to forecast, making
predictions about the future. The advantage of SM data is its relative ease of
acquisition, large quantity, and ability to capture socially relevant
information, which may be difficult to gather from other data sources.
Promising results exist across a wide variety of domains, but one will find
little consensus regarding best practices in either methodology or evaluation.
In this systematic review, we examine relevant literature over the past decade,
tabulate mixed results across a number of scientific disciplines, and identify
common pitfalls and best practices. We find that SM forecasting is limited by
data biases, noisy data, lack of generalizable results, a lack of
domain-specific theory, and underlying complexity in many prediction tasks. But
despite these shortcomings, recurring findings and promising results continue
to galvanize researchers and demand continued investigation. Based on the
existing literature, we identify research practices which lead to success,
citing specific examples in each case and making recommendations for best
practices. These recommendations will help researchers take advantage of the
exciting possibilities offered by SM platforms
Randomized experiments to detect and estimate social influence in networks
Estimation of social influence in networks can be substantially biased in
observational studies due to homophily and network correlation in exposure to
exogenous events. Randomized experiments, in which the researcher intervenes in
the social system and uses randomization to determine how to do so, provide a
methodology for credibly estimating of causal effects of social behaviors. In
addition to addressing questions central to the social sciences, these
estimates can form the basis for effective marketing and public policy.
In this review, we discuss the design space of experiments to measure social
influence through combinations of interventions and randomizations. We define
an experiment as combination of (1) a target population of individuals
connected by an observed interaction network, (2) a set of treatments whereby
the researcher will intervene in the social system, (3) a randomization
strategy which maps individuals or edges to treatments, and (4) a measurement
of an outcome of interest after treatment has been assigned. We review
experiments that demonstrate potential experimental designs and we evaluate
their advantages and tradeoffs for answering different types of causal
questions about social influence. We show how randomization also provides a
basis for statistical inference when analyzing these experiments.Comment: Forthcoming in Spreading Dynamics in Social System
Universal Components of Real-world Diffusion Dynamics based on Point Processes
Bursts in human and natural activities are highly clustered in time,
suggesting that these activities are influenced by previous events within the
social or natural system. Bursty behavior in the real world conveys information
of underlying diffusion processes, which have been the focus of diverse
scientific communities from online social media to criminology and
epidemiology. However, universal components of real-world diffusion dynamics
that cut across disciplines remain unexplored. Here, we introduce a wide range
of diffusion processes across disciplines and propose universal components of
diffusion frameworks. We apply these components to diffusion-based studies of
human disease spread, through a case study of the vector-borne disease dengue.
The proposed universality of diffusion can motivate transdisciplinary research
and provide a fundamental framework for diffusion models.Comment: 11 pages, 2 figure
Understanding Moderators of Peer Influence for Engineering Viral Marketing Seeding Simulations and Strategies
Seeding as an emerging viral marketing strategy requires a better understanding on how various contextual factors that embedded in social networks affect peer influence and product diffusion. Realistic simulations for seeding need to incorporate empirical insights about the complexities (various moderators) and dynamics (temporal changes) of peer influence by analyzing real-world data. We analyze the impacts of peer influence moderators in a large-scale phone call network of 0.48 million customers with 364 million calls and 3.9 million video-on-demand purchases, to design empirical models and engineer data-driven simulations of product diffusion, as well as developing and evaluating seeding strategies. We intend to contribute to existing research by 1) enriching the theoretical and empirical understanding of peer influence moderators for stakeholders, 2) combining econometric models and analyses with data-driven simulations towards a complex system approach for devising and evaluating effective seeding strategies in different scenarios
Towards Investigating Substructures and Role Recognition in Goal Oriented Online Communities
In this paper, we apply social network analytic methods to unveil the
structural dynamics of a popular open source goal oriented IRC community,
Ubuntu. The primary objective is to track the development of this ever growing
community over time using a social network lens and examine the dynamically
changing participation patterns of people. Specifically, our research seeks out
to investigate answers to the following question: How can the communication
dynamics help us in delineating important substructures in the IRC network?
This gives an insight into how open source learning communities function
internally and what drives the exhibited IRC behavior. By application of a
consistent set of social network metrics, we discern factors that affect
people's embeddedness in the overall IRC network, their structural influence
and importance as discussion initiators or responders. Deciphering these
informal connections are crucial for the development of novel strategies to
improve communication and foster collaboration between people conversing in the
IRC channel, there by stimulating knowledge flow in the network. Our approach
reveals a novel network skeleton, that more closely resembles the behavior of
participants interacting online. We highlight bottlenecks to effective
knowledge dissemination in the IRC, so that focused attention could be provided
to communities with peculiar behavioral patterns. Additionally, we explore
interesting research directions in augmenting the study of communication
dynamics in the IRC.Comment: Extended journal version of IEEE paper accepted in "4th IEEE
International Advance Computing Conference (IACC), India
Interpretable Stochastic Block Influence Model: measuring social influence among homophilous communities
Decision-making on networks can be explained by both homophily and social
influence. While homophily drives the formation of communities with similar
characteristics, social influence occurs both within and between communities.
Social influence can be reasoned through role theory, which indicates that the
influences among individuals depend on their roles and the behavior of
interest. To operationalize these social science theories, we empirically
identify the homophilous communities and use the community structures to
capture the "roles", which affect the particular decision-making processes. We
propose a generative model named Stochastic Block Influence Model and jointly
analyze both the network formation and the behavioral influence within and
between different empirically-identified communities. To evaluate the
performance and demonstrate the interpretability of our method, we study the
adoption decisions of microfinance in an Indian village. We show that although
individuals tend to form links within communities, there are strong positive
and negative social influences between communities, supporting the weak tie
theory. Moreover, we find that communities with shared characteristics are
associated with positive influence. In contrast, the communities with a lack of
overlap are associated with negative influence. Our framework facilitates the
quantification of the influences underlying decision communities and is thus a
useful tool for driving information diffusion, viral marketing, and technology
adoptions
Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in Human Communication
In this paper, we investigate the phenomenon of "age-specific small worlds"
using data from a large-scale mobile communication network approximating
interaction patterns at societal scale. Rather than asking whether two random
individuals are separated by a small number of links, we ask whether
individuals in specific age groups live in a small world in relation to
individuals from other age groups. Our analysis shows that there is systematic
variation in this age-relative small world effect. Young people live in the
"smallest world," being separated from other young people and their parent's
generation via a smaller number of intermediaries than older individuals. The
oldest people live in the "least small world," being separated from their same
age peers and their younger counterparts by a larger number of intermediaries.
Variation in the small world effect is specific to age as a node attribute
(being absent in the case of gender) and is consistently observed under several
data robustness checks. The discovery of age-specific small worlds is
consistent with well-known social mechanisms affecting the way age interacts
with network connectivity and the relative prevalence of kin ties and non-kin
ties observed in this network. This social pattern has significant implications
for our understanding of generation-specific dynamics of information cascades,
diffusion phenomena, and the spread of fads and fashions.Comment: 16 page
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