User characterization for online social networks

Tuna, Tayfun; Akbaş, Esra; Aksoy, Ahmet; Canbaz, Muhammed Abdullah; Karabiyik, Umit; Gonen, Bilal; Aygün, Ramazan

doi:10.1007/s13278-016-0412-3

Cited by 35 publications

(13 citation statements)

References 82 publications

(141 reference statements)

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“…A 2016 survey [11] covers a wide bandwidth of different approaches for user characterization. The behavioral properties they report range from conscientiousness and extroversion to privacy behavior, deceptive traits and response to social attacks.…”

Section: A Related Workmentioning

confidence: 99%

Social network of extreme tweeters

Zheng

Gupta

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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The number of posts made by a single user account on a social media platform Twitter in any given time interval is usually quite low. However, there is a subset of users whose volume of posts is much higher than the median. In this paper, we investigate the content diversity and the social neighborhood of these extreme users and others. We define a metric called "interest narrowness", and identify that a subset of extreme users, termed anomalous users, write posts with very low topic diversity, including posts with no text content. Using a few interaction patterns we show that anomalous groups have the strongest withingroup interactions, compared to their interaction with others. Further, they exhibit different information sharing behaviors with other anomalous users compared to non-anomalous extreme tweeters.

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Section: A Related Workmentioning

confidence: 99%

Social network of extreme tweeters

Zheng

Gupta

2019

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

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“…Alowibdi et al (2015) also found statistical inconsistencies in geo-location update times that were useful for the detection of deceptive accounts. Tuna et al (2016) focus on deriving features such as gender and location from the language and the local text used in the content respectively.…”

Section: Similar Identity Attributes As Those Depicted Inmentioning

confidence: 99%

Cyber-security: Identity deception detection on social media platforms

Walt

Eloff

Grobler

2018

Computers & Security

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Social media platforms allow billions of individuals to share their thoughts, likes and dislikes in real-time, without any censorship. This freedom, however, comes at a cyber-security risk. Cyber threats are more difficult to detect in a cyber world where anonymity and false identities are ever-present. The speed at which these deceptive identities evolve calls for solutions to detect identity deception. Cybersecurity threats caused by humans on social media platforms are widespread and warrant attention. This research posits a solution towards the intelligent detection of deceptive identities contrived by human individuals on social media platforms (SMPs). Firstly, this research evaluates machine learning models by using attributes such as the "profile image" found on SMPs. To improve on the results delivered by these models, past research findings from the field of psychology, such as that humans lie about their gender, are used. Newly engineered features such as "gender-derived-from-the-profileimage" are evaluated to grasp whether these features detect deception with greater accuracy. Furthermore, research results from detecting non-human (also known as bot) accounts are also leveraged to improve on the initial results. These machine learning results are lastly applied to a proposed model for the intelligent detection and interpretation of identity deception on SMPs. This paper shows that the cyber-security threat of identity deception can potentially be minimized, should the vulnerability in the current way of setting up user accounts on SMPs be re-engineered in the future.

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“…In online social networks, user behavior based features are useful for solving different problems, such as, link prediction (Valverde-Rebaza and de Andrade Lopes, 2013), personality prediction (Adalı and Golbeck, 2014), user attribute prediction (Tuna et al, 2016), link sign prediction (Shahriari et al, 2016), prediction of positive and negative users in Twitter (Roshanaei and Mishra, 2015), etc. Hence, we believe social (behavioral) phenomena based topological features can contribute substantially to solve the RLTP problem.…”

Section: Topological Feature Studymentioning

confidence: 99%

Predicting interval time for reciprocal link creation using survival analysis

Dave

Hasan

Zhang

et al. 2018

Soc. Netw. Anal. Min.

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The majority of directed social networks, such as, Twitter, Flickr, and Google+ exhibit reciprocal altruism, a social psychology phenomenon, which drives a vertex to create a reciprocal link with another vertex which has created a directed link towards the former. In existing works, scientists have already predicted the possibility of the creation of reciprocal link-a task known as "reciprocal link prediction". However, an equally important problem is determining the interval time between the creation of the first link (also called parasocial link) and its corresponding reciprocal link. No existing works have considered solving this problem, which is the focus of this paper. Predicting the reciprocal link interval time is a challenging problem for two reasons: First, there is a lack of effective features, since well-known link prediction features are designed for undirected networks and for the binary classification task, hence they do not work well for the interval time prediction; Second, the presence of ever-waiting links (i.e., parasocial links for which a reciprocal link is not formed within the observation period) makes the traditional supervised regression methods unsuitable for such data. In this paper, we propose a solution for the reciprocal link interval time prediction task. We map this problem to a survival analysis task and show through extensive experiments on real-

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User characterization for online social networks

Cited by 35 publications

References 82 publications

Social network of extreme tweeters

Social network of extreme tweeters

Cyber-security: Identity deception detection on social media platforms

Predicting interval time for reciprocal link creation using survival analysis

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