Online profiling and clustering of Facebook users

Dam, Jan-Willem van; Velden, Michel van de

doi:10.1016/j.dss.2014.12.001

Cited by 69 publications

(27 citation statements)

References 28 publications

(37 reference statements)

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“…In a recent study researchers were able to identify four segments (or clusters) of users from a sample of 40,000 Facebook users 11 . Through the inspection of these users' profile pages, these clusters showed a high correlation of other shared interests which went beyond other football clubs.…”

Section: Social Network Analysismentioning

confidence: 99%

Yik Yak: a social media sensor

Leskovich

2015

SPIE Proceedings

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This is the first academic paper which focuses specifically on the new social media application Yik Yak. To provide a solid foundation, a brief overview of a few anonymous social media platforms is provided. A social media sensor framework is then presented which utilizes a three-layered approach to addressing the use of analytic tools. Specifically the use of keyword, geolocation, sentiment, and network analysis is explored through the perspective of social media as a sensor. Challenges and criticisms are exposed in addition to some possible solutions. A theoretical case study is then offered which outlines a potential use of social media as a senor for emergency managers. The paper culminates with a data collection for the development of a lexicon for Yik Yak. This data collection focuses on an 18 day study which collects Yik Yak posts and Twitter tweets simultaneously. The top 100 keywords for each platform are collected for every 24 hour period and placed through a relative change comparison. Overall, Yik Yak offers a more stable baseline as compared to Twitter.

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Section: Social Network Analysismentioning

confidence: 99%

Yik Yak: a social media sensor

Leskovich

2015

SPIE Proceedings

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“…Thus, many researchers are investigating different data collection frameworks for data elicitations and analysis. Reference (van Dam and van de Velden 2015) describes a data-collection framework that can be used to explore user profiles and identify segments based on these profiles. In that study, the authors visualize how data from Facebook can be operationalized to obtain insights into a given user connected to the Facebook social network.…”

Section: Mcaf: Multi-dimensional Clustering Algorithm For Friendsmentioning

confidence: 99%

mCAF: a multi-dimensional clustering algorithm for friends of social network services

Chang

Mishra

2016

SpringerPlus

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In recent years, social network services have grown rapidly. The number of friends of each user using social network services has also increased significantly and is so large that clustering and managing these friends has become difficult. In this paper, we propose an algorithm called mCAF that automatically clusters friends. Additionally, we propose methods that define the distance between different friends based on different sets of measurements. Our proposed mCAF algorithm attempts to reduce the effort and time required for users to manage their friends in social network services. The proposed algorithm could be more flexible and convenient by implementing different privacy settings for different groups of friends. According to our experimental results, we find that the improved ratios between mCAF and SCAN are 35.8 % in similarity and 84.9 % in F1 score.

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“…It is possible to say that OSNs are a mirror where users reveal a lot about themselves both in the way they share and how they share-it, and this personification of OSN profiles can be of great value for marketers and companies, as they can be used to identify different users as potential customers if they indicated a preference towards a product/brand by using the interaction actions present on the OSNs [5] (such as pressing the 'Like' button). OSNs connect people who share interests and activities across geographic borders and have become a social commerce platform for businesses in recent years [6].…”

Section: Motivation For the Usage Of Osnsmentioning

confidence: 99%

Multidimensional Approach to Online Interest Networks

Barbosa¹,

Santos²

2017

dtcse

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Abstract. Online social networks allow users to socialise with others and also construct and manage their identities via self-presentation. This creates a network of interests that is often represented by a single network that aggregates all of the connections, however it is possible to increase the possibilities by dividing it into layers of individual networks. In this paper we propose a model for a multidimensional network of interests based on the interests that online social network users provide on their profiles. This multidimensional approach can be useful for getting insights about user behaviour or even help organisations understand the impact of their online content.

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Online profiling and clustering of Facebook users

Cited by 69 publications

References 28 publications

Yik Yak: a social media sensor

Yik Yak: a social media sensor

mCAF: a multi-dimensional clustering algorithm for friends of social network services

Multidimensional Approach to Online Interest Networks

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