It takes two to tango: Exploring social tie development with both online and offline interactions

Yin, Peifeng; He, Qi; Liu, Xingjie; Lee, Wang-Chien

doi:10.1002/sam.11265

Cited by 8 publications

(6 citation statements)

References 27 publications

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“…Understanding social bonds development among users is essential for user participation in co-creation online (Yin et al , 2015). One of the marketers said:…”

Section: Analysis and Resultsmentioning

confidence: 99%

Identification of barriers to co-create on-line: the perspectives of customers and companies

Chepurna

Criado

2018

JRIM

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Purpose -Value co-creation is an important topic of interest in marketing domain for the past decade.Co-creation via the internet has received a particular attention in the literature (O'Hern and Rindfleisch, 2010). Although there have been substantive number of studies of what motivates customers to participate in value co-creation in the internet-based platforms, there is a lack of research of what the deterrents are that may prevent customers from contributing their ideas online. This research was undertaken to define the deterrents from the customers and companies' point of view. Furthermore, the difference, if exists, between the users' and marketing professionals' ranking of the inhibitors to co-creation online is also studied.Design/methodology/approach -This exploratory qualitative research is based on 20 in-depth semistructured interviews with customers and 20 in-depth semi-structured interviews with marketing specialists from different companies. Spearman's rank correlation is applied to explore the relationship between the internet users' and marketers' responses.Findings -There are nine constraining factors. The results show that although there is a repetition of the mentioned constraining factors indicated by the both groups of the interviewees, the ranking of the barriers is distinctive.Research Implications -New conceptual information is received on what restrains customers from co-creation from both customers' and companies' point of view.Practical Implications -This paper explains the potential problems to be confronted when launching a co-creation project in the internet-based platforms and offers managers a preliminary guide to comprehension of the users' deterrents rating.Originality -The paper that defines deterrents to co-creation online.

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“…Understanding social bonds development among users is essential for user participation in co-creation online (Yin et al , 2015). One of the marketers said:…”

Section: Analysis and Resultsmentioning

confidence: 99%

Identification of barriers to co-create on-line: the perspectives of customers and companies

Chepurna

Criado

2018

JRIM

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“…Yin et al later extended the work by investigating the tie between offline and online interactions, and found that the collective level of online interaction is independent with offline interaction. However, mutual friends' activities (that can be observed online) may play a significant role in inducing offline activities [43]. These works illustrate initial steps towards studying offline-online interactions in event-based social media networks.…”

Section: Offline-online Interactionsmentioning

confidence: 84%

“…Multiple studies have found close ties between offline and online worlds, allowing interactions in the social networks to reflect those of the real world [23,43]. If such an assumption holds, one could predict whether a person is being sick just by observing his/her social network activities.…”

Section: Introductionmentioning

confidence: 99%

Modeling Individual-Level Infection Dynamics Using Social Network Information

Tuarob

Tucker

Salathé

et al. 2015

Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

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Epidemic monitoring systems engaged in accurate discovery of infected individuals enable better understanding of the dynamics of epidemics and thus may promote effective disease mitigation or prevention. Currently, infection discovery systems require either physical participation of potential patients or provision of information from hospitals and health-care services. While social media has emerged as an increasingly important knowledge source that reflects multiple real world events, there is only a small literature examining how social media information can be incorporated into computational epidemic models. In this paper, we demonstrate how social media information can be incorporated into and improve upon traditional techniques used to model the dynamics of infectious diseases. Using flu infection histories and social network data collected from 264 students in a college community, we identify social network signals that can aid identification of infected individuals. Extending the traditional SIRS model, we introduce and illustrate the efficacy of an Online-Interaction-Aware SusceptibleInfected-Recovered-Susceptible (OIA-SIRS) model based on four social network signals for modeling infection dynamics. Empirical evaluations of our case study, flu infection within a college community, reveal that the OIA-SIRS model is more accurate than the traditional model, and also closely tracks the real-world infection rates as reported by CDC ILINet and Google Flu Trend.

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“…First, based on the entire physical locations and held time of all the events in our data sets, we get 30 regions and 21-time slots. In particular, the 30 ) denotes whether user u has attended events held in region r (or on time slot t). Third, using MF technique, we factorize matrix MT into two matrices UT θ θ = ,…,…”

Section: User Preference and Item Attribute Computationmentioning

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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It takes two to tango: Exploring social tie development with both online and offline interactions

Cited by 8 publications

References 27 publications

Identification of barriers to co-create on-line: the perspectives of customers and companies

Identification of barriers to co-create on-line: the perspectives of customers and companies

Modeling Individual-Level Infection Dynamics Using Social Network Information

Social event planning using hybrid pairwise Markov random fields

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