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, 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.
Highlights
An ever increasing usage and popularity of social media platforms has become the sign of our times – close to a half of the world’s population is connected through social media platforms
Since the Multi-Layer Perceptron (MLP) model outperformed the Logistic Regression (LR), we only present the findings for the MLP model
The modAL framework [65], an active learning framework for python is used for manually labeling the Twitter users
Summary
An ever increasing usage and popularity of social media platforms has become the sign of our times – close to a half of the world’s population is connected through social media platforms. Social media provide a platform through which users can freely share information simultaneously with a significantly larger audience than traditional media. As social media became ubiquitous in our daily lives, both its positive and negative impacts have become more pronounced. Successive studies have shown that extensive distribution of misinformation can play a significant role in the success or failure of an important event or a cause [1], [2]. Barring the dissemination and circulation of misleading information, social networks provide the mechanisms for corrupted users to perform an extensive range of illegitimate actions such as spam and political astroturfing [3], [4]. As a result, measuring the credibility of both the user and the text itself has become a major issue. We assign a credibility score to each Twitter user based on certain extracted features
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