Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users

• URL: http://arxiv.org/abs/2210.15214v1
• Date: Thu, 27 Oct 2022 06:57:19 GMT
• Title: Trust and Believe -- Should We? Evaluating the Trustworthiness of Twitter Users
• Authors: Tanveer Khan and Antonis Michalas
• Abstract summary: Fake news on social media is a major problem with far-reaching negative repercussions on both individuals and society. In this work, we create a model through which we hope to offer a solution that will instill trust in social network communities. Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user.
• Score: 5.695742189917657
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, they are regularly abused by corrupt users. One of these nefarious activities is so-called fake news -- a "virus" that has been spreading rapidly thanks to the hospitable environment provided by social media platforms. The extensive spread of fake news is now becoming a major problem with far-reaching negative repercussions on both individuals and society. Hence, the identification of fake news on social media is a problem of utmost importance that has attracted the interest not only of the research community but most of the big players on both sides - such as Facebook, on the industry side, and political parties on the societal one. In this work, we create a model through which we hope to be able to offer a solution that will instill trust in social network communities. Our model analyses the behaviour of 50,000 politicians on Twitter and assigns an influence score for each evaluated user based on several collected and analysed features and attributes. Next, we classify political Twitter users as either trustworthy or untrustworthy using random forest and support vector machine classifiers. An active learning model has been used to classify any unlabeled ambiguous records from our dataset. Finally, to measure the performance of the proposed model, we used accuracy as the main evaluation metric.

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