# Online User Profiling to Detect Social Bots on Twitter

arXiv: https://arxiv.org/abs/2203.05966 · HTML: https://mlsanity.com/paper/2203.05966/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 11 of 11)

- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [The paradigm-shift of social spambots: Evidence, theories, and tools for the arms race](https://mlsanity.com/paper/1701.03017/) (2017)
- [BotOrNot: A System to Evaluate Social Bots](https://mlsanity.com/paper/1602.00975/) (2016)
- [Deep Neural Networks for Bot Detection](https://mlsanity.com/paper/1802.04289/) (2018)
- [DNA-inspired online behavioral modeling and its application to spambot detection](https://mlsanity.com/paper/1602.00110/) (2016)
- [Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement](https://mlsanity.com/paper/2107.13595/) (2021)
- [Political Bots and the Manipulation of Public Opinion in Venezuela](https://mlsanity.com/paper/1507.07109/) (2015)
- [A Comprehensive Survey on Machine Learning Techniques and User Authentication Approaches for Credit Card Fraud Detection](https://mlsanity.com/paper/1912.02629/) (2019)
- [Code-Bridged Classifier (CBC): A Low or Negative Overhead Defense for Making a CNN Classifier Robust Against Adversarial Attacks](https://mlsanity.com/paper/2001.06099/) (2020)
- [Probabilistic Model of Narratives Over Topical Trends in Social Media: A Discrete Time Model](https://mlsanity.com/paper/2004.06793/) (2020)
- [The ABBE Corpus: Animate Beings Being Emotional](https://mlsanity.com/paper/2201.10618/) (2022)

## Built on by (most cited first, 7 of 7)

- [BotShape: A Novel Social Bots Detection Approach via Behavioral Patterns](https://mlsanity.com/paper/2303.10214/) (2023)
- [FakeSwarm: Improving Fake News Detection with Swarming Characteristics](https://mlsanity.com/paper/2305.19194/) (2023)
- [Exploring social bots: A feature-based approach to improve bot detection in social networks](https://mlsanity.com/paper/2411.06626/) (2024)
- [Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection](https://mlsanity.com/paper/2205.06177/) (2022)
- [Prevent Car Accidents by Using AI](https://mlsanity.com/paper/2206.11381/) (2022)
- [Machine Learning Application in Health](https://mlsanity.com/paper/2207.06228/) (2022)
- [Decoding the Alphabet Soup of Degrees in the United States Postsecondary Education System Through Hybrid Method: Database and Text Mining](https://mlsanity.com/paper/2309.13050/) (2023)

## Abstract (arXiv)

Social media platforms can expose influential trends in many aspects of everyday life. However, the movements they represent can be contaminated by disinformation. Social bots are one of the significant sources of disinformation in social media. Social bots can pose serious cyber threats to society and public opinion. This research aims to develop machine learning models to detect bots based on the extracted user's profile from a Tweet's text. Online users' profile shows the user's personal information, such as age, gender, education, and personality. In this work, the user's profile is constructed based on the user's online posts. This work's main contribution is three-fold: First, we aim to improve bot detection through machine learning models based on the user's personal information generated by the user's online comments. When comparing two online posts, the similarity of personal information makes it difficult to differentiate a bot from a human user. However, this research turns personal information similarity among two online posts into an advantage for the new bot detection model. The new proposed model for bot detection creates user profiles based on personal information such as age, personality, gender, education from users' online posts and introduces a machine learning model to detect social bots with high prediction accuracy based on personal information. Second, create a new public data set that shows the user's profile for more than 6900 Twitter accounts in the Cresci 2017 data set.