# Seeing and Believing: Evaluating the Trustworthiness of Twitter Users

arXiv: https://arxiv.org/abs/2107.08027 · HTML: https://mlsanity.com/paper/2107.08027/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 61 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 9 of 9)

- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [modAL: A modular active learning framework for Python](https://mlsanity.com/paper/1805.00979/) (2018)
- [Disinformation and Social Bot Operations in the Run Up to the 2017 French Presidential Election](https://mlsanity.com/paper/1707.00086/) (2017)
- [TweetCred: Real-Time Credibility Assessment of Content on Twitter](https://mlsanity.com/paper/1405.5490/) (2014)
- [Measuring user influence on Twitter: A survey](https://mlsanity.com/paper/1508.07951/) (2015)
- [Understanding Types of Users on Twitter](https://mlsanity.com/paper/1406.1335/) (2014)
- [Retweet Us, We Will Retweet You: Spotting Collusive Retweeters Involved in Blackmarket Services](https://mlsanity.com/paper/1806.08979/) (2018)
- [An in-depth characterisation of Bots and Humans on Twitter](https://mlsanity.com/paper/1704.01508/) (2017)
- [University Twitter Engagement: Using Twitter Followers to Rank Universities](https://mlsanity.com/paper/1708.05790/) (2017)

## Built on by (most cited first, 2 of 2)

- [Trustworthiness of mathbbX Users: A One-Class Classification Approach](https://mlsanity.com/paper/2402.02066/) (2024)
- [To Vaccinate or not to Vaccinate? Analyzing mathbbX Power over the Pandemic](https://mlsanity.com/paper/2503.02563/) (2025)

## Abstract (arXiv)

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.