Modeling relationship strength in online social networks

Xiang, Rongjing; Neville, Jennifer; Rogati, Monica

doi:10.1145/1772690.1772790

Cited by 539 publications

(355 citation statements)

References 16 publications

Supporting

Mentioning

339

Contrasting

Unclassified

Order By: Relevance

“…For example, users' personal characteristics such as age, gender, and cuisine preferences were used in [21], and social affinity was considered in [22,23]. User's history of online activity can also be collected, for example, search history; history of map browsing and spatial searching logs [24][25][26], place reviews and ratings [27][28][29], as well as explicit interaction on LBSN, by tagging and commenting on places [30,31]. In this work, users' location tracks are considered as the primary source of user-place relationships, as these represent explicit interaction with geographic places, normally recording actual visits to places.…”

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

View full text Add to dashboard Cite

Knowledge of users' visits to places is one of the keys to understanding their interest in places. Usercontributed annotations of place, the types of places they visit, and the activities they carry out, add a layer of important semantics that, if considered, can result in more refined representations of user profiles. In this paper, semantic information is summarised as tags for places and a folksonomy data model is used to represent spatial and semantic relationships between users, places, and tags. The model allows simple co-occurrence methods and similarity measures to be applied to build different views of personalised user profiles. Basic profiles capture direct user interactions, while enriched profiles offer an extended view of users' association with places and tags that take into account relationships in the folksonomy. The main contributions of this work are the proposal of a uniform approach to the creation of user profiles on the Social Web that integrates both the spatial and semantic components of user-provided information, and the demonstration of the effectiveness of this approach with realistic datasets.

show abstract

Section: Related Workmentioning

confidence: 99%

Spatio-semantic user profiles in location-based social networks

Mohamed

Abdelmoty

2017

Int J Data Sci Anal

View full text Add to dashboard Cite

show abstract

“…These two works use a supervised learning model that needs human intervention to work properly. Aiming at the same objective, Xiang et al [29] proposed a model to infer relationship strength based on profile similarity and interaction activity, with the goal of automatically distinguishing strong relationships from weak ones. It is worth noting that this model relies on an unsupervised learning method, but it lacks a empirical evaluation with real users.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, weak ties may refer to coworkers or less trusted friends. More recent works have proposed models to predict tie strength in SNSs [8,13,29]. These works showed that it is possible to infer tie strength from the available personal data in a SNS.…”

Section: Introductionmentioning

confidence: 99%

BFF: A tool for eliciting tie strength and user communities in social networking services

et al. 2013

View full text Add to dashboard Cite

López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014 Abstract The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.

show abstract

“…[5] studied the topological properties of the social network formed by Twitter users. And, [6] analyzed the relationship strength in the social network of Facebook and LinkedIn. Beyond link structure, [2] studied how to identify influential users in Twitter.…”

Section: Related Workmentioning

confidence: 99%

Learning to blend vitality rankings from heterogeneous social networks

Bian

Chang

et al. 2012

Neurocomputing

View full text Add to dashboard Cite

Heterogeneous social network services, such as Facebook and Twitter, have emerged as popular, and often effective channels for Web users to capture updates from their friends. The explosion in popularity of these social network services, however, has created the problem of "information overload". The problem is becoming more severe as more and more users have engaged in more than one social networks simultaneously, each of which usually yields different friend connections and various sources of updates. Thus, it has made necessity to perform effective information filtering to retrieve information really attractive to web users from each of social networks and further blend them into a unified ranking list. In this paper, we introduce the problem of blending vitality rankings from heterogeneous social networks, where vitality denotes all kinds of updates user receives in various social networks. We propose a variety of content, users, and users correlation features for this task. Since vitalities from different social networks are likely to have different sets of features, we employ a divide-and-conquer strategy in order to fully exploit all available features for vitalities from each social network, respectively. Our experimental results, obtained from a large scale evaluation over two popular social networks, demonstrate the effectiveness of our method for putting vitalities that really interest users into higher orders in the blended ranking list. We complement our results with a thorough investigation of the feature importance and model selection with respect to both blending strategy and ranking for each social network.

show abstract

Modeling relationship strength in online social networks

Cited by 539 publications

References 16 publications

Spatio-semantic user profiles in location-based social networks

Spatio-semantic user profiles in location-based social networks

BFF: A tool for eliciting tie strength and user communities in social networking services

Learning to blend vitality rankings from heterogeneous social networks

Contact Info

Product

Resources

About