Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

Nasim, Mehwish; Charbey, Raphaël; Prieur, Christophe; Brandes, Ulrik

doi:10.1109/tcss.2016.2618998

Cited by 24 publications

(7 citation statements)

References 27 publications

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“…They used self network structure, by analyzing the user's interaction friendship circle, the SVM classifier is used to find out the friends who are not included in their personal data at present. M Nasim et al proposed a method based on multiple network and interaction characteristics, which used a variety of classification algorithms for friend relationship inference [76]. The results show that the performance of friend relationship inference is improved by using interactive information as the agent of friendship without network structure.…”

Section: B Interaction-based Approachesmentioning

confidence: 99%

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Piao

Cui

2021

IEEE Access

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With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users' personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users' privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

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Section: B Interaction-based Approachesmentioning

confidence: 99%

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Piao

Cui

2021

IEEE Access

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“…Privacy is major concern Algorithm for news feed is not known Filtering is not done properly [16] It has been observed that individuals who are friends with each others have similar interests Two evaluation metrics were used to judge the performance of classifier ROC and PR used to The aim of this research is to know the methods used by researchers to predict the behavior of social media users. In this research, data were collected based on the use of three different social networking sites such as Facebook, Instagram, and Twitter.…”

Section: Analysis Of Raw Datamentioning

confidence: 99%

An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram

Singh

Halgamuge

Moses

2019

Social Network Analytics

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“…A significant challenge for such models is noise reduction through filtering "fake news", removing misclassified or irrelevant tweets, or mitigating the effects of missing data. This is of particular concern, as the changing limits on accessing social media data remains a major challenge for researchers [26]. Access to data through APIs and third parties can be inconsistent, incomplete, and corrupted by noise in the form of bots.…”

Section: Problem Contextmentioning

confidence: 99%

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

Nasim

Nguyen

Lothian³

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Content polluters, or bots that hijack a conversation for political or advertising purposes are a known problem for event prediction, election forecasting and when distinguishing real news from fake news in social media data. Identifying this type of bot is particularly challenging, with state-of-the-art methods utilising large volumes of network data as features for machine learning models. Such datasets are generally not readily available in typical applications which stream social media data for real-time event prediction. In this work we develop a methodology to detect content polluters in social media datasets that are streamed in real-time. Applying our method to the problem of civil unrest event prediction in Australia, we identify content polluters from individual tweets, without collecting social network or historical data from individual accounts. We identify some peculiar characteristics of these bots in our dataset and propose metrics for identification of such accounts. We then pose some research questions around this type of bot detection, including: how good Twitter is at detecting content polluters and how well state-of-the-art methods perform in detecting bots in our dataset. CCS CONCEPTS• Information systems → Social networking sites; • Security and privacy → Social network security and privacy; KEYWORDSCivil unrest, Social bots, Content polluters, Missing links, Twitter * Currently works at Tyto.ai † D2D CRC Stream Lead This paper is published under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. Authors reserve their rights to disseminate the work on their personal and corporate Web sites with the appropriate attribution.

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Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

Cited by 24 publications

References 27 publications

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

Privacy Inference Attack Against Users in Online Social Networks: A Literature Review

An Analysis of Demographic and Behavior Trends Using Social Media: Facebook, Twitter, and Instagram

Real-time Detection of Content Polluters in Partially Observable Twitter Networks

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