Entity Matching in Online Social Networks

Peled, Olga; Fire, Michael; Rokach, Lior; Elovici, Yuval

doi:10.1109/socialcom.2013.53

Cited by 74 publications

(50 citation statements)

References 6 publications

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“…In other words, two different user profiles from two different OSNs can be matched based on the features extracted from user profile and networks. There are some studies about profile matching and while some of them just use profiles of users (Peled et al, 2013), some of them use friend networks. Peled et al (2013) provide a four-step algorithm for entity resolution based on user profiles: data acquisition, feature extraction, training set construction, and building the model.…”

Section: Matching Profiles Across Multiple Networkmentioning

confidence: 99%

“…There are some studies about profile matching and while some of them just use profiles of users (Peled et al, 2013), some of them use friend networks. Peled et al (2013) provide a four-step algorithm for entity resolution based on user profiles: data acquisition, feature extraction, training set construction, and building the model. In the data acquisition stage, irrelevant data are filtered out after crawling user profiles.…”

Section: Matching Profiles Across Multiple Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

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Social networks in any form, specifically online social networks (OSNs), are becoming a part of our everyday life in this new millennium especially with the advanced and simple communication technologies through easily accessible devices such as smartphones and tablets. The data generated through the use of these technologies need to be analyzed for forensic purposes when criminal and terrorist activities are involved. In order to deal with the forensic implications of social networks, current research on both digital forensics and social networks need to be incorporated and understood. This will help digital forensics investigators to predict, detect and even prevent any criminal activities in different forms. It will also help researchers to develop new models/techniques in the future. This paper provides literature review of the social network forensics methods, models, and techniques in order to provide an overview to the researchers for their future works as well as the law enforcement investigators for their investigations when crimes are committed in the cyber space. It also provides awareness and defense methods for OSN users in order to protect them against to social attacks.

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Section: Matching Profiles Across Multiple Networkmentioning

confidence: 99%

Section: Matching Profiles Across Multiple Networkmentioning

confidence: 99%

A Survey of Social Network Forensics

Karabiyik¹,

Canbaz²,

Aksoy³

et al. 2016

JDFSL

View full text Add to dashboard Cite

show abstract

“…Peled [18] presented a supervised learning method to match user profiles, and then proved its good performance in matching user profiles, though incomplete records with missing data could significantly increase the error rate of the comparison algorithm. Xu [19] showed that combing social features with personal features could improve the performance of criminal identity matching.…”

Section: Related Workmentioning

confidence: 99%

“…Studies have shown that individuals tend to use the same username, or a similar one in different online services. Peled [18] believed that name-based features are the most important ones in entity matching. Therefore, we take online shopping account profile attributes into consideration.…”

Section: Features Extraction Of Online Shopping Accountmentioning

confidence: 99%

Prediction of purchase behaviors across heterogeneous social networks

Wang

Liu

et al. 2015

J Supercomput

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Due to the development of web services, many social network sites, as well as online shopping sites have been booming in the past decade, where it is a common phenomenon that people are likely to use multiple services at the same time. On the one hand, previous research findings indicate the data sparsity issues of online shopping accounts, which is caused by the heavy-tailed distribution of user information. On the other hand, in social network sites, the personal information and the corresponding statuses of an account are abundant, and their genuineness is guaranteed either by the service provider, or by the willingness of the account owner to connect to his or her friends in reality. Making use of the correlation between accounts of a same individual is a crucial prerequisite for many interesting cross network applications, such as improving the recommendation performance of the online shopping sites using extra information from social network services. In this paper, we firstly propose a game-theoretic method to identify correlation accounts of individuals between social network sites and online shopping sites with stable matching model, incorporating account profiles as well as historical behaviors. Using the above account relationships, we then put forward a predicting method that combines heterogeneous social network information and online shopping information, to predict the purchasing behaviors. The results show that our method identifies up to 70 % of the correlation accounts between Facebook and eBay, one of the most popular social network sites and online shopping 123Prediction of purchase behaviors across heterogeneous... 3321 sites in the world, respectively. The experimental results also show that using the correlation account sets, the accuracy of our purchase predicting method outperforms the state-of-the-art methods by 5 %.

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“…This problem has been attracting many researchers. For instance, Raad et al [16] and Peled et al [15] proposed a model to measure the similarity between user profiles. Anderson et al [1] calculated the similarity between user characteristics.…”

Section: Introductionmentioning

confidence: 99%

Estimating the similarity of social network users based on behaviors

Nguyen

Tran

Dam

et al. 2018

Vietnam J Comput Sci

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Recently, with the express growth of social network, users have joined more and more of these networks and live their life virtually. Consequently, they create a huge data on these social networks: their profile, interest, and behavior such as post, comment, like, joining groups or communities, etc. This brings some new challenges to researchers: do users having the same profile/interest show the same behavior? And vice versa, do users having the same behavior have interest in the same things? One of the basic issues in these challenges is the problem of estimating the similarity among users on these social networks based on their profile, interest, and behavior. This paper presents a model for estimating the similarity between users based on their behavior on social networks. The considered behaviors are activities including posting entries, liking these entries, commenting and liking the comment in these entries. The model is then evaluated with a dataset-collected users from Twitter. The results show that the model estimates correctly the similarity among users in the majority of the cases.

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Entity Matching in Online Social Networks

Cited by 74 publications

References 6 publications

A Survey of Social Network Forensics

A Survey of Social Network Forensics

Prediction of purchase behaviors across heterogeneous social networks

Estimating the similarity of social network users based on behaviors

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