Temporal Pattern in Tweeting Behavior for Persons' Identity Verification

Sultana, Madeena; Gavrilova, Marina L.

doi:10.1109/smc.2018.00424

Cited by 7 publications

(18 citation statements)

References 21 publications

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“…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. In [18], authors proposed a system based on the temporal information of the users, extracted from OSN.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The temporal profile of users reveal their posting patterns in social network. The temporal profile of each user can be created by extracting the features from the timestamps of users' profiles [18], such as average probability of tweeting per day, average probability of tweeting per hour, average probability of tweeting per week, seven days interval period, seven days tweeting period, average probabilities of original tweet, retweet, and reply/mention per day, etc.…”

Section: E Temporal Profilementioning

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%

Section: E Temporal Profilementioning

confidence: 99%

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Tumpa

Gavrilova

2020

IEEE Access

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“…Instance-based approaches are usually implemented using clustering algorithms or machine learning classifiers such as Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), or Decision Trees (DTs). Some studies have been conducted specifically using short digital texts such as Tweets [6], [16], [17], [18], [19], [30], [33], SMS [4], [5], or small pieces of texts from blog posts [35]. They are indicated in bold in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In another set of works, Sultana et al [17], [18], [19] proposed several features aimed at quantifying interaction patterns and temporal behaviour of Twitter users, rather than analyzing the actual content of the messages. User interaction patterns are measured by creating profiles of friendship (other users with whom a user maintains frequent relationships via retweet, reply and mention), contextual information (shared hashtags and URLs), and temporal interaction (posting patterns) [17], [18].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The features employed captures the writing style of the author at different levels of analysis, from individual characters or groups or characters, to the type of words used, or the organization of sentences. We also analyze specific particularities of Tweet messages, namely, the use of URLs, mentions to other users, hashtags, and quotations, whose use in the literature is restricted to a few studies [17], [18], [19]. Our experiments show that such particular features help to improve the performance when added to any of the other features.…”

Section: Introductionmentioning

confidence: 98%

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Writer Identification Using Microblogging Texts for Social Media Forensics

Alonso-Fernandez,

Belvisi,

Hernandez-Diaz

et al. 2020

Preprint

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Establishing the authorship of online texts is a fundamental issue to combat several cybercrimes. Unfortunately, some platforms limit the length of the text, making the challenge harder. Here, we aim at identifying the author of Twitter messages limited to 140 characters. We evaluate popular stylometric features, widely used in traditional literary analysis, which capture the writing style at different levels (character, word, and sentence). We use a public database of 93 users, containing 1142 to 3209 Tweets per user. We also evaluate the influence of the number of Tweets per user for enrolment and testing. If the amount is sufficient (>500), a Rank 1 of 97-99% is achieved. If data is scarce (e.g. 20 Tweets for testing), the Rank 1 with the best individual feature method ranges from 54.9% (100 Tweets for enrolment) to 70.6% (1000 Tweets). By combining the available features, a substantial improvement is observed, reaching a Rank 1 of 70% when using 100 Tweets for enrolment and only 20 for testing. With a bigger hit list size, accuracy of the latter case increases to 86.4% (Rank 5) or 95% (Rank 20). This demonstrates the feasibility of identifying writers of digital texts, even with few data available.

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Human Micro-Expression: A Novel Social Behavioral Biometric for Person Identification

et al. 2023

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The reliance on Online Social Networks (OSN) for both formal and informal social interactions has dramatically changed the way people communicate. In this paper, a novel Social Behavioral Biometric (SBB), human micro-expression, is introduced for person identification. An emotion detection model is developed to extract emotion probability scores from person's writing samples posted on Twitter. The corresponding emotion-progression features are extracted using an original technique that turns users' microblogs into emotion-progression signals. Finally, a novel social behavioral biometric system that leverages rank-level weighted majority voting to achieve an accurate person identification is implemented. The proposed system is validated on a proprietary benchmark dataset consisting of 250 Twitter users. The experimental results convincingly demonstrate that the proposed social behavioral biometric, human microexpression, possesses a strong distinguishable ability and can be used for person identification. The study further reveals that the proposed social behavioral biometric outperforms all the original SBB traits.

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Temporal Pattern in Tweeting Behavior for Persons' Identity Verification

Cited by 7 publications

References 21 publications

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Score and Rank Level Fusion Algorithms for Social Behavioral Biometrics

Writer Identification Using Microblogging Texts for Social Media Forensics

Human Micro-Expression: A Novel Social Behavioral Biometric for Person Identification

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