# A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms
###### Abstract  
In recent years, the ubiquity of social networks has transformed them into essential platforms for information dissemination. However, the unprecedented nature of social networks and the advent of advanced machine learning techniques, including generative models such as GPT and diffusion models, have facilitated the propagation of rumors, posing challenges to society. Detecting and countering these rumors to mitigate their adverse effects on individuals and society is imperative. Automatic rumor detection, typically framed as a binary classification problem, predominantly relies on supervised machine learning models, necessitating substantial labeled data; yet, the scarcity of labeled datasets due to the high cost of fact-checking and annotation hinders the application of machine learning for rumor detection. In this study, we address this challenge through active learning. We assess various query strategies across different machine learning models and datasets in order to offer a comparative analysis. Our findings reveal that active learning reduces labeling time and costs while achieving comparable rumor detection performance. Furthermore, we advocate for the use of machine learning models with nonlinear classification boundaries on complex environmental datasets for more effective rumor detection. rumor detection; active learning; active learning query strategy; social networks Article  
A Comparative Analysis of Active Learning for Rumor Detection on Social Media Platforms  
20231312098132023131209  
The spread of rumors can pose significant threats to the credibility of the internet and have far-reaching real-life consequences, including causing public panic, disrupting the social order, eroding government credibility, and even endangering national security [6]. A notable case of rampant rumor propagation occurred during the 2016 U.S. presidential election. During the election, as many as 529 different rumor stories pertaining to presidential candidates [PERSON] and [PERSON] were spreading on Twitter, instantly reaching millions of voters and potentially influencing the election's outcome [7]. A more recent example revolves around the plethora of rumors regarding the COVID-19 pandemic [8]. These rumors on social platforms have significantly undermined the credibility and reliability of information shared on these platforms, consequently diminishing users' willingness to turn to social media for information. A 2021 survey conducted by the Pew Research Center [2] further underscores this decline in trust and reliance on social media for news. It revealed a decrease in the percentage of adult American users who frequently or occasionally obtain news from social media platforms, dropping from 53% in 2020 to 48% in 2021. This decline coincides with mounting criticism directed at social media and technology companies for their perceived inadequacy in curbing the spread of misleading information on their platforms. Therefore, it is of paramount importance to detect rumors spreading on social media platforms as early as possible. Rumor detection has attracted significant attention from both social media platforms and researchers over the past decade. Typically, users on various social media platforms are encouraged to report or annotate suspicious posts as potential rumors. Subsequently, the accuracy of these possible rumors is verified with the assistance of human moderators and third-party fact-checkers. While this approach yields high-quality results, the substantial human effort required, including manual labeling and rumor verification, is challenging to reconcile with the sheer volume of emerging rumors. Therefore, there is a need for robust and efficient automated rumor detection approaches. Automatic rumor detection is normally deemed a binary classification task, in which classifiers are employed to distinguish between rumors and non-rumors.