IronSense: Towards the Identification of Fake User-Profiles on Twitter Using Machine Learning

S3UCA: Soft-Margin Support Vector Machine-Based Social Network User Credibility Assessment Method

“…One paper [17] adopts the decision tree algorithm in machine learning to learn the user profile information quantitatively to achieve the purpose of user reliability evaluation. Another paper [19] characterizes user credibility by quantifying user-generated content information.…”

“…Paper [21] uses a PageRank algorithm to quantify user information, which comprehensively considers various types of user information to represent user credibility and avoids the problem of insufficient accuracy of user credibility evaluation results caused by sparse user information in papers [19,21]. Because papers [17], [19], and [21] evaluate user credibility in the linear summation dimension, there is a problem of user aliasing at the user classification threshold. It can be seen from Figure 3 that the user credibility evaluation method UCSSVM proposed in this paper is superior to the other three algorithms in relation to the user credibility evaluation results because our proposed method can allocate the corresponding weight reasonably according to the importance of user data.…”

“…To verify the rationality and effectiveness of the method in this paper, a comparative experiment with three other algorithms is set up. e three algorithms are derived from the literature [17,19,21] related to user credibility research at home and abroad. e experimental verification is based on Python and LIBSVM-related simulation platforms.…”

“…Machine learning algorithms are widely used in processing user information and quantitative evaluation [16], and researchers at home and abroad have made different research progress. Narayanan et al [17] and Van Der Walt et al [18] conducted quantitative research on the relevant profile information of both TNs and trusted users (TPs) in social networks through machine learning algorithms such as a decision tree algorithm and a PageRank algorithm. Slimi et al [19], through the study of user-generated content information, proposed to take the digital information and information distribution rule in user-generated content information as the quantitative object to measure the generated content information to achieve the evaluation of user credibility.…”

Detecting Fake Users on Social Media with a Graph Database

“…High accuracy was obtained. Narayanan et al (2018) have devised a browser plugin to recognize fake accounts on Twitter by also using the random forest classifier and features such as number of friends, followers, and statuses with a precision of 95%, recall of 94%, and accuracy of 94.1%. Gupta et al (2017) have applied 12 supervised machine learning classification algorithms on a Facebook dataset and evaluated them to find the best performing classifiers which are mostly decision tree and decision rules classifiers.…”