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

arXiv: https://arxiv.org/abs/2210.15214 · HTML: https://mlsanity.com/paper/2210.15214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 65 references that are in the map.
- First posted on arXiv 27 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Fake News Early Detection: An Interdisciplinary Study](https://mlsanity.com/paper/1904.11679/) (2019)
- [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)
- [Who Will Retweet This? Automatically Identifying and Engaging Strangers on Twitter to Spread Information](https://mlsanity.com/paper/1405.3750/) (2014)
- [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, 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.