132 research outputs found
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
Dissecting AI-Generated Fake Reviews: Detection and Analysis of GPT-Based Restaurant Reviews on Social Media
Recent advances in generative models such as GPT may be used to fabricate indistinguishable fake customer reviews at a much lower cost, posing challenges for social media platforms to detect this kind of content. This study addresses two research questions: (1) the effective detection of AI-generated restaurant reviews generated from high-quality elite authentic reviews, and (2) the comparison of out-of-sample predicted AI-generated reviews and authentic reviews across multiple dimensions of review, user, restaurant, and content characteristics. We fine-tuned a GPT text detector to predict fake reviews, significantly outperforming existing solutions. We applied the model to predict non-elite reviews that already passed the Yelp filtering system, revealing that AI-generated reviews typically score higher ratings, users posting such content have less established Yelp reputations and AI-generated reviews are more comprehensible and less linguistically complex than human-generated reviews. Notably, machine-generated reviews are more prevalent in low-traffic restaurants in terms of customer visits
Combat AI With AI: Counteract Machine-Generated Fake Restaurant Reviews on Social Media
Recent advances in generative models such as GPT may be used to fabricate
indistinguishable fake customer reviews at a much lower cost, thus posing
challenges for social media platforms to detect these machine-generated fake
reviews. We propose to leverage the high-quality elite restaurant reviews
verified by Yelp to generate fake reviews from the OpenAI GPT review creator
and ultimately fine-tune a GPT output detector to predict fake reviews that
significantly outperforms existing solutions. We further apply the model to
predict non-elite reviews and identify the patterns across several dimensions,
such as review, user and restaurant characteristics, and writing style. We show
that social media platforms are continuously challenged by machine-generated
fake reviews, although they may implement detection systems to filter out
suspicious reviews.Comment: Paper submitted to KDD2023. 8 pages, 5 figure
Compact -Point Finite Difference Methods with High Accuracy Order and/or -Matrix Property for Elliptic Cross-Interface Problems
In this paper we develop finite difference schemes for elliptic problems with
piecewise continuous coefficients that have (possibly huge) jumps across fixed
internal interfaces. In contrast with such problems involving one smooth
non-intersecting interface, that have been extensively studied, there are very
few papers addressing elliptic interface problems with intersecting interfaces
of coefficient jumps. It is well known that if the values of the permeability
in the four subregions around a point of intersection of two such internal
interfaces are all different, the solution has a point singularity that
significantly affects the accuracy of the approximation in the vicinity of the
intersection point. In the present paper we propose a fourth-order -point
finite difference scheme on uniform Cartesian meshes for an elliptic problem
whose coefficient is piecewise constant in four rectangular subdomains of the
overall two-dimensional rectangular domain. Moreover, for the special case when
the intersecting point of the two lines of coefficient jumps is a grid point,
such a compact scheme, involving relatively simple formulas for computation of
the stencil coefficients, can even reach sixth order of accuracy. Furthermore,
we show that the resulting linear system for the special case has an
-matrix, and prove the theoretical sixth order convergence rate using the
discrete maximum principle. Our numerical experiments demonstrate the fourth
(for the general case) and sixth (for the special case) accuracy orders of the
proposed schemes. In the general case, we derive a compact third-order finite
difference scheme, also yielding a linear system with an -matrix. In
addition, using the discrete maximum principle, we prove the third order
convergence rate of the scheme for the general elliptic cross-interface
problem.Comment: 25 pages, 13 figure
Sixth-Order Hybrid FDMs and/or the M-Matrix Property for Elliptic Interface Problems with Mixed Boundary Conditions
In this paper, we develop sixth-order hybrid finite difference methods (FDMs)
for the elliptic interface problem in
, where is a smooth interface inside
. The variable scalar coefficient and source are possibly
discontinuous across . The hybrid FDMs utilize a 9-point compact
stencil at any interior regular point of the grid and a 13-point stencil at
irregular points near . For interior regular points away from ,
we obtain a sixth-order 9-point compact FDM satisfying the M-matrix property.
Consequently, for the elliptic problem without interface (i.e., is
empty), our compact FDM satisfies the discrete maximum principle, which
guarantees the theoretical sixth-order convergence. We also derive sixth-order
compact (4-point for corners and 6-point for edges) FDMs having the M-matrix
property at any boundary point subject to (mixed) Dirichlet/Neumann/Robin
boundary conditions. For irregular points near , we propose fifth-order
13-point FDMs, whose stencil coefficients can be effectively calculated by
recursively solving several small linear systems. Theoretically, the proposed
high order FDMs use high order (partial) derivatives of the coefficient ,
the source term , the interface curve , the two jump functions along
, and the functions on . Numerically, we always use
function values to approximate all required high order (partial) derivatives in
our hybrid FDMs without losing accuracy. Our proposed FDMs are independent of
the choice representing and are also applicable if the jump conditions
on only depend on the geometry (e.g., curvature) of the curve
. Our numerical experiments confirm the sixth-order convergence in the
norm of the proposed hybrid FDMs for the elliptic interface
problem
Asymmetric Peer Influence in Smartphone Adoption in a Large Mobile Network
Understanding adoption patterns of smartphones is of vital importance to telecommunication managers in today’s highly dynamic mobile markets. In this paper, we leverage the network structure and specific position of each individual in the social network to account for and measure the potential heterogeneous role of peer influence in the adoption of the iPhone 3G. We introduce the idea of coreperiphery as a meso-level organizational principle to study the social network, which complements the use of centrality measures derived from either global network properties (macro-level) or from each individual\u27s local social neighbourhood (micro-level). Using millions of call detailed records from a mobile network operator in one country for a period of eleven months, we identify overlapping social communities as well as core and periphery individuals in the network. Our empirical analysis shows that core users exert more influence on periphery users than vice versa. Our findings provide important insights to help identify influential members in the social network, which is potentially useful to design optimal targeting strategies to improve current network-based marketing practices
a big data approach
Purpose: The purpose of this paper is to propose and demonstrate how Tourism2vec, an adaptation of a natural language processing technique Word2vec, can serve as a tool to investigate tourism spatio-temporal behavior and quantifying tourism dynamics. Design/methodology/approach: Tourism2vec, the proposed destination-tourist embedding model that learns from tourist spatio-temporal behavior is introduced, assessed and applied. Mobile positioning data from international tourists visiting Tuscany are used to construct travel itineraries, which are subsequently analyzed by applying the proposed algorithm. Locations and tourist types are then clustered according to travel patterns. Findings: Municipalities that are similar in terms of their scores of their neural embeddings tend to have a greater number of attractions than those geographically close. Moreover, clusters of municipalities obtained from the K-means algorithm do not entirely align with the provincial administrative segmentation.authorsversionpublishe
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