With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations

Gulati, Avni; Eirinaki, Magdalini

doi:10.1145/3308558.3313745

Cited by 7 publications

(7 citation statements)

References 25 publications

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“…Furthermore, crowdsourcing is commonly used to recover psychosocial attributes [8,15,33]. Several recommender approaches utilize social concepts like homophily [27,28,63,66], and diffusions [28,48]. Arous et al [4] use crowdsourcing to find influencers, aggregating open-ended answers, but don't provide an influence model capable of querying arbitrary users.…”

Section: Related Workmentioning

confidence: 99%

Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

Ram¹,

Rizoiu²

2021

Preprint

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Social influence pervades our everyday lives and lays the foundation for complex social phenomena, such as the spread of misinformation and the polarization of communities. In a crisis like the COVID-19 pandemic, social influence can determine whether lifesaving information is adopted, public health measures are observed, or immunization campaigns meet their targets. Existing literature studying online social influence suffers from several drawbacks. First, a disconnect appears between psychology approaches, which are generally performed and tested in controlled lab experiments, and the quantitative methods, which are usually data-driven and rely on network and event analysis. The former are slow, expensive to deploy, and typically do not generalize well to topical issues (such as an ongoing pandemic); the latter often oversimplify the complexities of social influence and ignore psychosocial literature. This work bridges this gap and presents three contributions towards modeling and empirically quantifying online influence. The first contribution is a data-driven Generalized Influence Model that incorporates two novel psychosocial-inspired mechanisms: the conductance of the diffusion network and the social capital distribution. The second contribution is a framework to empirically rank users' social influence using a human-in-the-loop active learning method combined with crowdsourced pairwise influence comparisons. We build a human-labeled ground truth, calibrate our generalized influence model and perform a large-scale evaluation of influence. We find that our generalized model outperforms the current state-ofthe-art approaches and corrects the inherent biases introduced by the widely used follower count. As the third contribution, we apply the influence model to discussions around COVID-19. We quantify users' influence, and we tabulate it against their professions. We find that the executives, media, and military are more influential than pandemic-related experts such as life scientists and healthcare professionals. These findings call on such professionals to own up to their responsibility for shaping social reactions.

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Section: Related Workmentioning

confidence: 99%

Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

Ram¹,

Rizoiu²

2021

Preprint

View full text Add to dashboard Cite

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“…The third dataset, Yelp [86], is a location-based social network (LBSN) containing 1.5M vertices, 10M edges, and 6M check-ins. For Timik and Yelp, we follow the settings in [7,27,68] to treat POIs in the above datasets as the candidate items in SVGIC. The preference utility and social utility values are learned by the PIERT learning framework [45] which jointly models the social influence between users and the latent topics of items.…”

Section: Experiments Setup and Evaluation Planmentioning

confidence: 99%

Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping

Ko¹,

Lai²,

Shuai³

et al. 2020

Preprint

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Shopping in VR malls has been regarded as a paradigm shift for E-commerce, but most of the conventional VR shopping platforms are designed for a single user. In this paper, we envisage a scenario of VR group shopping, which brings major advantages over conventional group shopping in brickand-mortar stores and Web shopping: 1) configure flexible display of items and partitioning of subgroups to address individual interests in the group, and 2) support social interactions in the subgroups to boost sales. Accordingly, we formulate the Social-aware VR Group-Item Configuration (SVGIC) problem to configure a set of displayed items for flexibly partitioned subgroups of users in VR group shopping. We prove SVGIC is APX-hard and also NP-hard to approximate within 32 31 − . We design an approximation algorithm based on the idea of Co-display Subgroup Formation (CSF) to configure proper items for display to different subgroups of friends. Experimental results on real VR datasets and a user study with hTC VIVE manifest that our algorithms outperform baseline approaches by at least 30.1% of solution quality.

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“…There is growing interest in understanding the role of positive and negative network ties or links-recurring relationships that involve enduring valenced interpersonal judgments-in explaining actors' attitudes, behaviors, cognition, and outcomes [25]. Identification of tie valences can be helpful for a variety of practical downstream tasks such as recommending products [26,51] or friends [22], estimating the impact of a publication [7], and predicting the dynamics of complex social networks [24,49]. However, despite the amount of time that individuals spend within social structures involving hierarchy and some level of competition, such as work organizations, little is known about tie valence within these networks.…”

Section: Introductionmentioning

confidence: 99%

Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction

Singh

Lee

Labianca

et al. 2023

ACM Trans. Knowl. Discov. Data

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Individuals interacting in organizational settings involving varying levels of formal hierarchy naturally form a complex network of social ties having different tie valences (e.g., positive and negative connections). Social ties critically affect employees’ satisfaction, behaviors, cognition, and outcomes — yet identifying them solely through survey data is challenging because of the large size of some organizations or the often hidden nature of these ties and their valences. We present a novel deep learning model encompassing NLP and graph neural network techniques that identifies positive and negative ties in a hierarchical network. The proposed model uses human resource attributes as node information and web-logged work conversation data as link information. Our findings suggest that the presence of conversation data improves the tie valence classification by 8.91% compared to employing user attributes alone. This gain came from accurately distinguishing positive ties, particularly for male, non-minority, and older employee groups. We also show a substantial difference in conversation patterns for positive and negative ties with positive ties being associated with more messages exchanged on weekends, and lower use of words related to anger and sadness. These findings have broad implications for facilitating collaboration and managing conflict within organizational and other social networks.

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With a Little Help from My Friends (and Their Friends): Influence Neighborhoods for Social Recommendations

Cited by 7 publications

References 25 publications

Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

Conductance and Social Capital: Modeling and Empirically Measuring Online Social Influence

Optimizing Item and Subgroup Configurations for Social-Aware VR Shopping

Multi-Stage Machine Learning Model for Hierarchical Tie Valence Prediction

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