302 research outputs found
Fake accounts detection system based on bidirectional gated recurrent unit neural network
Online social networks have become the most widely used medium to interact with friends and family, share news and important events or publish daily activities. However, this growing popularity has made social networks a target for suspicious exploitation such as the spreading of misleading or malicious information, making them less reliable and less trustworthy. In this paper, a fake account detection system based on the bidirectional gated recurrent unit (BiGRU) model is proposed. The focus has been on the content of users’ tweets to classify twitter user profile as legitimate or fake. Tweets are gathered in a single file and are transformed into a vector space using the GloVe word embedding technique in order to preserve the semantic and syntax context. Compared with the baseline models such as long short-term memory (LSTM) and convolutional neural networks (CNN), the results are promising and confirm that using GloVe with BiGRU classifier outperforms with 99.44% for accuracy and 99.25% for precision. To prove the efficiency of our approach the results obtained with GloVe were compared to Word2vec under the same conditions. Results confirm that GloVe with BiGRU classifier performs the best results for detection of fake Twitter accounts using only tweets content feature
Multilevel User Credibility Assessment in Social Networks
Online social networks are one of the largest platforms for disseminating
both real and fake news. Many users on these networks, intentionally or
unintentionally, spread harmful content, fake news, and rumors in fields such
as politics and business. As a result, numerous studies have been conducted in
recent years to assess the credibility of users. A shortcoming of most of
existing methods is that they assess users by placing them in one of two
categories, real or fake. However, in real-world applications it is usually
more desirable to consider several levels of user credibility. Another
shortcoming is that existing approaches only use a portion of important
features, which downgrades their performance. In this paper, due to the lack of
an appropriate dataset for multilevel user credibility assessment, first we
design a method to collect data suitable to assess credibility at multiple
levels. Then, we develop the MultiCred model that places users at one of
several levels of credibility, based on a rich and diverse set of features
extracted from users' profile, tweets and comments. MultiCred exploits deep
language models to analyze textual data and deep neural models to process
non-textual features. Our extensive experiments reveal that MultiCred
considerably outperforms existing approaches, in terms of several accuracy
measures
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