Discovery of Newsworthy Events in Twitter

Duarte, Fernando; Pereira, Óscar Mortágua; Aguiar, Rui L.

doi:10.5220/0006712702440252

Cited by 3 publications

(1 citation statement)

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“…A little number of related tweets (value of false positive measure less than 10%). Duarte et al [18] introducing a new methodology developed for text analysis in English or other widely spoken languages, such as Portuguese using big data [19]. Javed et al [20] introduces what because of this type of shortcut, we can block such harmful websites by clicking on them as locker websites.…”

Section: Introductionmentioning

confidence: 99%

Social cyber-criminal, towards automatic real time recognition of malicious posts on Twitter

Ibrahim

Razek

El-Sherbeny

2022

IJEECS

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Easy access to the internet throughout the world has fully reformed the usage of social communication such as Facebook, Twitter, Linked In which are becoming a part of our life. Accordingly, cybercrime has become a vital problem, especially in developing countries. The dissemination of information with no risk of being discovered and fetched leads to an increase in cyber-criminal. Meanwhile, the huge amount of data continuously produced from Twitter made the discovery process of cyber-criminals is a tough assignment. This research will contribute in determined on the build the comparable vectors for (positive and negative classes) and then the classify incoming tweets to predicate his class (positive or negative). The proposed routines staring with the construct super comparable vectors (SCV) (positive and negative vectors), and the construct vector for the incoming tweet, and then calculate similarities with both SCV and compare calculated similarities to predicate class of incoming tweet. In this research, we used some common techniques for calculating the weight of terms in tweets to construct SCV. To ensure the successful operation of the proposed system, we performed a pilot analysis on a real example of an examination. Research Improves precision, recall, and F1 values by 87%, 59%, 69.99%, respectively.

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Section: Introductionmentioning

confidence: 99%