Classifying Misinformation of User Credibility in Social Media Using Supervised Learning

Asfand-e-yar, Muhammad; Hashir, Qadeer; Tanvir, Syed Hassan; Khalil, Wajeeha

doi:10.32604/cmc.2023.034741

Cited by 4 publications

(1 citation statement)

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“…The model can then be used to classify new, unseen users. The ML algorithms used for X user classification are supervised [8], unsupervised [9], and semi-supervised [10,11]. Classifying X users as trusted or untrusted using only ML models can be challenging due to high-dimensional and variable characteristics of big data [12].…”

Section: Introductionmentioning

confidence: 99%

Seeing and Believing: Evaluating the Trustworthiness of Twitter Users

Khan

Michalas

2021

IEEE Access

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Social networking and micro-blogging services, such as Twitter, play an important role in sharing digital information. Despite the popularity and usefulness of social media, there have been many instances where corrupted users found ways to abuse it, as for instance, through raising or lowering user's credibility. As a result, while social media facilitates an unprecedented ease of access to information, it also introduces a new challenge -that of ascertaining the credibility of shared information. Currently, there is no automated way of determining which news or users are credible and which are not. Hence, establishing a system that can measure the social media user's credibility has become an issue of great importance. Assigning a credibility score to a user has piqued the interest of not only the research community but also most of the big players on both sides -such as Facebook, on the side of industry, and political parties on the societal one. In this work, we created a model which, we hope, will ultimately facilitate and support the increase of trust in the social network communities. Our model collected data and analysed the behaviour of 50,000 politicians on Twitter. Influence score, based on several chosen features, was assigned to each evaluated user. Further, we classified the political Twitter users as either trusted or untrusted using random forest, multilayer perceptron, and support vector machine. An active learning model was used to classify any unlabelled ambiguous records from our dataset. Finally, to measure the performance of the proposed model, we used precision, recall, F1 score, and accuracy as the main evaluation metrics.

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