Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2026
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866909038260781056 |
|---|---|
| author | Karnam, Sai Keerthana Kundu, Abhirup Arora, Jashn Jain, Manish Mukherjee, Animesh |
| author_facet | Karnam, Sai Keerthana Kundu, Abhirup Arora, Jashn Jain, Manish Mukherjee, Animesh |
| contents | Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short span, yet, amidst the stream of genuine information, fake news and rumors continue to spread. The need for effective detection models is becoming increasingly critical. Past user behavior and user engagement on a post are strong signals that SOTA approaches leverage for fake news detection and other post classification tasks. However, these approaches lean too heavily on knowing this past behavior, and thus suffer from a cold user problem, or users that are new or have minimal footprint on the platform. In this paper, we make three core contributions. We first establish the value of user behavior, both content and user-user interactions, in the task of fake news and rumor detection. We then establish the extensive prevalence of cold users in the real-world datasets, and show the need for newer algorithms considering cold users. We next propose a novel socially-aware context representation scheme - USER EVIDENCE NETWORK (UEN) - to detect the spread of misinformation and unverified information while efficiently navigating this cold user challenge. We introduce techniques that approximate missing or absent behavior data of a new user from existing users' interactions. By carefully addressing the cold user challenge, our work provides robust approaches targeting fake news and rumor detection for real-world platforms. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_12511 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users Karnam, Sai Keerthana Kundu, Abhirup Arora, Jashn Jain, Manish Mukherjee, Animesh Social and Information Networks Machine Learning Social media serves as a primary source of information in the current digital era. Many people consume a vast range of information in a very short span, yet, amidst the stream of genuine information, fake news and rumors continue to spread. The need for effective detection models is becoming increasingly critical. Past user behavior and user engagement on a post are strong signals that SOTA approaches leverage for fake news detection and other post classification tasks. However, these approaches lean too heavily on knowing this past behavior, and thus suffer from a cold user problem, or users that are new or have minimal footprint on the platform. In this paper, we make three core contributions. We first establish the value of user behavior, both content and user-user interactions, in the task of fake news and rumor detection. We then establish the extensive prevalence of cold users in the real-world datasets, and show the need for newer algorithms considering cold users. We next propose a novel socially-aware context representation scheme - USER EVIDENCE NETWORK (UEN) - to detect the spread of misinformation and unverified information while efficiently navigating this cold user challenge. We introduce techniques that approximate missing or absent behavior data of a new user from existing users' interactions. By carefully addressing the cold user challenge, our work provides robust approaches targeting fake news and rumor detection for real-world platforms. |
| title | Real-World Challenges in Fake News Detection: Dealing with Posts by Cold Users |
| topic | Social and Information Networks Machine Learning |
| url | https://arxiv.org/abs/2605.12511 |