Seeing and Believing: Evaluating the Trustworthiness of Twitter Users

TL;DR

This paper develops a model to evaluate Twitter users' credibility, using behavioral data from 50,000 politicians and machine learning classifiers to distinguish trusted from untrusted users, aiming to enhance trust in social media.

Contribution

The study introduces a new credibility scoring model for Twitter users based on influence features and machine learning, including active learning for ambiguous cases.

Findings

The model achieved high accuracy in classifying trusted versus untrusted users.

Influence scores correlated with user credibility.

Active learning improved classification of ambiguous records.

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.