User Recognition From Social Behavior in Computer-Mediated Social Context

Sultana, Madeena; Paul, Padma Polash; Gavrilova, Marina L.

doi:10.1109/thms.2017.2681673

Cited by 38 publications

(61 citation statements)

References 38 publications

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“…The feasibility of the SBB features was further analyzed by the same authors in [11]. A set of social behavioral features from the online social interactions of 241 Twitter users has been identified in [17]. Authors found that only ten recent tweets are enough to recognize 58% of users at rank-1 and established the stability of SB features over time for both frequent and infrequent OSN users.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The concept of generating temporal profile was first introduced in [17]. The temporal profile of users reveal their posting patterns in social network.…”

Section: E Temporal Profilementioning

confidence: 99%

“…All experiments have been carried out on the Windows 10 operating system, 1.8 GHz Quad-Core Intel Core i5 processor with 8GB RAM. The dataset consists of 250 users, whose profiles are publicly available [17], [33]. The dataset contains four sessions of social interactions with the time interval of four to six weeks between each of the sessions for the same set of users.…”

Section: Fusion Techniquesmentioning

confidence: 99%

“…The reply network, retweet network, URL network, hashtag network and temporal profile for the 241 users are generated similarly to [17], [33]. Fig.…”

Section: B Performance Comparison Of Proposed Writing Profiles With mentioning

confidence: 99%

“…The goal of this experiment is to demonstrate the performance of score level fusion algorithms on our proposed SBB system. In this experiment, the score level fusion algorithms, [17] 89.00% URL Network [17] 45.00% Reply Network [17] 62.00% Hashtag Network [17] 52.00% Temporal Network [17] 13.00% Retweet Network [33] 90.00% URL Network [33] 53.00% Reply Network [33] 68.00% Hashtag Network [33] 61.00% Writing Profile (Proposed Trait) 91.70%…”

Section: Experimentation On Score Level Fusion Algorithms For Sbbmentioning

confidence: 99%

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Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Tumpa

Gavrilova

2020

IEEE Access

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The goal of a biometric system is to recognize individuals based on their unique physiological or behavioral traits. Online Social Networking (OSN) platforms have become an integral part of the daily life of individuals, where they leave a recognizable trail of behavioral information. Social Behavioral Biometric (SBB), being an emerging trend, focuses on such trails to distinguish between individuals. This research investigates the impact of users' writing profiles on OSN to conclude whether such profiles contribute to SBB. The distinctiveness of the SBB features that are extracted from the social behavioral data of Twitter is studied. A person identification system that relies on users' writing profiles, reply, retweet, shared weblink, trendy topic networks and temporal profiles is proposed. Score and rank level weighted fusion algorithm performance is compared on a social interaction database of 241 Twitter users. The experimental results establish that the users' writing profiles have the highest impact over other social biometric features and that score level fusion algorithms perform better than rank level fusion on SBB. The proposed system has achieved recognition rate of 99.45% at rank-1 after cross-validation using genetic algorithm based score level fusion algorithm. The system outperformed all prior researches on SBB in terms of identification accuracy.

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Section: Literature Reviewmentioning

confidence: 99%

“…The concept of generating temporal profile was first introduced in [17]. The temporal profile of users reveal their posting patterns in social network.…”

Section: E Temporal Profilementioning

confidence: 99%

Section: Fusion Techniquesmentioning

confidence: 99%

“…The reply network, retweet network, URL network, hashtag network and temporal profile for the 241 users are generated similarly to [17], [33]. Fig.…”

Section: B Performance Comparison Of Proposed Writing Profiles With mentioning

confidence: 99%