Socio-spatial influence maximization in location-based social networks

Hosseinpour, Mohammad; Malek, M. R.; Claramunt, Christophe

doi:10.1016/j.future.2019.06.024

Cited by 14 publications

(10 citation statements)

References 32 publications

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“…Some researchers are studying the role of trust on influence in marketing (Liu et al, 2015) and on decision-making (Capuano, 2019). Others are exploring the ways to maximise SMI influence (Taninmis et al, 2019, Yerasani et al, 2019Hosseinpour et al, 2019). Efforts have been made by some researchers to study the personality traits of these SMI (Oyibo andVassileva, 2019, Freberg et al, 2011;Erz et al, 2018) .…”

Section: Introductionmentioning

confidence: 99%

Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance

Singh

Cascini

McComb

2020

Proc. Des. Soc.: Des. Conf.

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Social media influencers (SMI) are gaining interest and many are studying their influence on the online audience, little is known about the role played by them in offline teams. One such attempt to study the effect of influencers in co-design team is presented in this paper, where individuals who are confident in their abilities drive the team process. Thus, self-efficacy is considered for determining influencer behaviour. Results expose the relationship between self-efficacy and influencer status on the design process, besides briefly highlighting the effects on above-average teams.

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Section: Introductionmentioning

confidence: 99%

Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance

Singh

Cascini

McComb

2020

Proc. Des. Soc.: Des. Conf.

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“…Yu et al 25 studied how to select a set of potential participants who not only have large preferences on a given event but also can influence a large number of people to participate this event. Hosseinpour et al 31 considered both network and spatial properties, and extended the traditional influence maximization problem in social networks to the location‐based social networks. On the basis of the line graph, for a given query point, they proposed to identify a set of influential users in social networks.…”

Section: Related Workmentioning

confidence: 99%

Social event planning using hybrid pairwise Markov random fields

Xiao

Saidutta

Fekri

2021

Int J of Intelligent Sys

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Event‐based social networks (EBSNs) have become increasingly popular, which provide online social event management platforms for event organizers to publish and share social events (e.g., outdoor activities). In EBSNs, a major challenge for a social event organizer is how to plan a social event to attract the maximum number of attendance. To organize an event, three essential elements are required, namely, what (i.e., event content), where (i.e., event location), and when (i.e., event time). In this paper, we focus on the social event planning problem, which selects a location and time to hold a social event for the organizer with the given event content, to maximize the total number of participants. The solution of the social event planning problem could support decision‐making for social event organizers. For simplicity, we denote a location and time pair as an item in this paper. To solve the social event planning problem, we present a hybrid pairwise Markov random field (H‐PMRF) model which takes latent preferences of users, latent attributes of items, similarities between users and similarities between items into consideration. In particular, we construct an undirected graph where each node represents a user's decision on a specific item and each edge represents the relationship between the nodes, define the node potentials and edge potentials which model the dependency relationships between nodes, and give a joint probability distribution over the graph. Further, we adopt the Loopy Belief Propagation algorithm to compute the posterior probability distribution of each node in H‐PMRF and select the location and time to hold the event which could attract the maximum number of participants. We collect real‐world data set from DoubanEvent website and conduct extensive experiments on it. Experimental results show that the proposed model outperforms several baselines.

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“…Yelp's website ("Yelp.com"), is a crowdsourced local business review and social networking site. Users can rate and leave a review, share experiences, and check in to a location with their phones to upload photos of their experiences, including the real estate visited (Hosseinpour et al, 2019;Singh et al, 2019). Nevertheless, despite its numerous advantages, crowdsourcing also suffers from a general lack of quality assurance (Mohammadi & Malek, 2015a).…”

Section: Literature Reviewmentioning

confidence: 99%

Visual Description of the Indoor Space of Real Estate in Crowd-Sourcing Environments

Haghbayan

Malek

Tashayo

2020

Real Estate Management and Valuation

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Emerging the power of collective intelligence through crowdsourcing could create a clear path for visualizing real estate as well. One of the Crowdsourcing applications is describing the indoor space of a real estate. This paper aims to describe real estate in the context of spatial crowdsourcing. Qualitative and quantitative criteria were used in this study to describe the real estate space, topological relationships, directional relations, color, location, dimensions, and height as qualitative criteria. Quantitative criteria were selected as the dimensions and height. The proposed model was evaluated by two groups: those who had never seen the real estate and others that had already seen the same real estate. We implemented a website called SAMA1 to evaluate the proposed model with crowdsourcing data using online collaborative tools. SAMA is using tools, such as a sketch plan, photo, text, virtual tour, and visual descriptions. To evaluate SAMA, we compared it with four representative commercial websites, and the impact of the tools was precisely examined. The obtained results demonstrate that the proposed model can be utilized to visually describe the indoor space of real estate in crowd-sourcing environments.

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Socio-spatial influence maximization in location-based social networks

Cited by 14 publications

References 32 publications

Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance

Analysing the Effect of Self-Efficacy and Influencers on Design Team Performance

Social event planning using hybrid pairwise Markov random fields

Visual Description of the Indoor Space of Real Estate in Crowd-Sourcing Environments

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