Am I typing fresh tweets: Detecting up-to-dateness and worth of categorical information in microblogs

Cingiz, Mustafa Özgür; Di̇ri̇, Banu; Biricik, Göksel

doi:10.1016/j.eswa.2015.02.025

Cited by 4 publications

(1 citation statement)

References 29 publications

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“…Then, a data set composed of 100 users, of which 36 were humans, 36 cyborgs, and 28 bots, was mounted. Although the amount of users might look small in comparison to the milions of active Twitter accounts, some of the literature has used about the same number of users for classification goals, as seen in [12]. In this paper, this was also possible due to the fact that the range of variation of written texts about a single subject is not that big.…”

Section: Methodsmentioning

confidence: 99%

Account classification in online social networks with LBCA and wavelets

Igawa

Barbon

Paulo

et al. 2016

Information Sciences

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We developed a wavelet-based approach for account classification that detects textual dissemination by bots on an Online Social Network (OSN). Its main objective is to match account patterns with humans, cyborgs or robots, improving the existing algorithms that automatically detect frauds. With a computational cost suitable for OSNs, the proposed approach analyses the distribution of key terms. The descriptors, a wavelet-based feature vector for each user's account, work in conjunction with a new weighting scheme, called Lexicon Based Coefficient Attenuation (LBCA) and serve as inputs to one of the classifiers tested: Random Forests and Multilayer Perceptrons. Experiments were performed using a set of posts crawled during the 2014 FIFA World Cup, obtaining accuracies within the range from 94 to 100%.

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Section: Methodsmentioning

confidence: 99%

Account classification in online social networks with LBCA and wavelets

Igawa

Barbon

Paulo

et al. 2016

Information Sciences

View full text Add to dashboard Cite

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Authorship verification applied to detection of compromised accounts on online social networks

Barbon

Igawa

Zarpelão

2016

Multimed Tools Appl

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Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

Barbon

Campos

Tavares

et al. 2018

ACM Trans. Multimedia Comput. Commun. Appl.

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Social interactions take place in environments that influence people’s behaviours and perceptions. Nowadays, the users of Online Social Network (OSN) generate a massive amount of content based on social interactions. However, OSNs wide popularity and ease of access created a perfect scenario to practice malicious activities, compromising their reliability. To detect automatic information broadcast in OSN, we developed a wavelet-based model that classifies users as being human, legitimate robot, or malicious robot, as a result of spectral patterns obtained from users’ textual content. We create the feature vector from the Discrete Wavelet Transform along with a weighting scheme called Lexicon-based Coefficient Attenuation. In particular, we induce a classification model using the Random Forest algorithm over two real Twitter datasets. The corresponding results show the developed model achieved an average accuracy of 94.47% considering two different scenarios: single theme and miscellaneous one.

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Am I typing fresh tweets: Detecting up-to-dateness and worth of categorical information in microblogs

Cited by 4 publications

References 29 publications

Account classification in online social networks with LBCA and wavelets

Account classification in online social networks with LBCA and wavelets

Authorship verification applied to detection of compromised accounts on online social networks

Detection of Human, Legitimate Bot, and Malicious Bot in Online Social Networks Based on Wavelets

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