What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
- URL: http://arxiv.org/abs/2402.00371v2
- Date: Thu, 4 Jul 2024 23:37:40 GMT
- Title: What Does the Bot Say? Opportunities and Risks of Large Language Models in Social Media Bot Detection
- Authors: Shangbin Feng, Herun Wan, Ningnan Wang, Zhaoxuan Tan, Minnan Luo, Yulia Tsvetkov,
- Abstract summary: We investigate the opportunities and risks of large language models in social bot detection.
We propose a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities.
Experiments show that instruction tuning on 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines.
- Score: 48.572932773403274
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Social media bot detection has always been an arms race between advancements in machine learning bot detectors and adversarial bot strategies to evade detection. In this work, we bring the arms race to the next level by investigating the opportunities and risks of state-of-the-art large language models (LLMs) in social bot detection. To investigate the opportunities, we design novel LLM-based bot detectors by proposing a mixture-of-heterogeneous-experts framework to divide and conquer diverse user information modalities. To illuminate the risks, we explore the possibility of LLM-guided manipulation of user textual and structured information to evade detection. Extensive experiments with three LLMs on two datasets demonstrate that instruction tuning on merely 1,000 annotated examples produces specialized LLMs that outperform state-of-the-art baselines by up to 9.1% on both datasets, while LLM-guided manipulation strategies could significantly bring down the performance of existing bot detectors by up to 29.6% and harm the calibration and reliability of bot detection systems.
Related papers
- BotSSCL: Social Bot Detection with Self-Supervised Contrastive Learning [6.317191658158437]
We propose a novel framework for social Bot detection with Self-Supervised Contrastive Learning (BotSSCL)
BotSSCL uses contrastive learning to distinguish between social bots and humans in the embedding space to improve linear separability.
We demonstrate BotSSCL's robustness against adversarial attempts to manipulate bot accounts to evade detection.
arXiv Detail & Related papers (2024-02-06T06:13:13Z) - Spotting LLMs With Binoculars: Zero-Shot Detection of Machine-Generated Text [98.28130949052313]
A score based on contrasting two closely related language models is highly accurate at separating human-generated and machine-generated text.
We propose a novel LLM detector that only requires simple calculations using a pair of pre-trained LLMs.
The method, called Binoculars, achieves state-of-the-art accuracy without any training data.
arXiv Detail & Related papers (2024-01-22T16:09:47Z) - A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions [39.36381851190369]
There is an imperative need to develop detectors that can detect LLM-generated text.
This is crucial to mitigate potential misuse of LLMs and safeguard realms like artistic expression and social networks from harmful influence of LLM-generated content.
The detector techniques have witnessed notable advancements recently, propelled by innovations in watermarking techniques, statistics-based detectors, neural-base detectors, and human-assisted methods.
arXiv Detail & Related papers (2023-10-23T09:01:13Z) - LMBot: Distilling Graph Knowledge into Language Model for Graph-less
Deployment in Twitter Bot Detection [41.043975659303435]
We propose a novel bot detection framework LMBot that distills the knowledge of graph neural networks (GNNs) into language models (LMs)
For graph-based datasets, the output of LMs provides input features for the GNN, enabling it to optimize for bot detection and distill knowledge back to the LM in an iterative, mutually enhancing process.
Our experiments demonstrate that LMBot achieves state-of-the-art performance on four Twitter bot detection benchmarks.
arXiv Detail & Related papers (2023-06-30T05:50:26Z) - Detecting Phishing Sites Using ChatGPT [2.3999111269325266]
We propose a novel system called ChatPhishDetector that utilizes Large Language Models (LLMs) to detect phishing sites.
Our system involves leveraging a web crawler to gather information from websites, generating prompts for LLMs based on the crawled data, and then retrieving the detection results from the responses generated by the LLMs.
The experimental results using GPT-4V demonstrated outstanding performance, with a precision of 98.7% and a recall of 99.6%, outperforming the detection results of other LLMs and existing systems.
arXiv Detail & Related papers (2023-06-09T11:30:08Z) - Red Teaming Language Model Detectors with Language Models [114.36392560711022]
Large language models (LLMs) present significant safety and ethical risks if exploited by malicious users.
Recent works have proposed algorithms to detect LLM-generated text and protect LLMs.
We study two types of attack strategies: 1) replacing certain words in an LLM's output with their synonyms given the context; 2) automatically searching for an instructional prompt to alter the writing style of the generation.
arXiv Detail & Related papers (2023-05-31T10:08:37Z) - LLMDet: A Third Party Large Language Models Generated Text Detection
Tool [119.0952092533317]
Large language models (LLMs) are remarkably close to high-quality human-authored text.
Existing detection tools can only differentiate between machine-generated and human-authored text.
We propose LLMDet, a model-specific, secure, efficient, and extendable detection tool.
arXiv Detail & Related papers (2023-05-24T10:45:16Z) - Can AI-Generated Text be Reliably Detected? [54.670136179857344]
Unregulated use of LLMs can potentially lead to malicious consequences such as plagiarism, generating fake news, spamming, etc.
Recent works attempt to tackle this problem either using certain model signatures present in the generated text outputs or by applying watermarking techniques.
In this paper, we show that these detectors are not reliable in practical scenarios.
arXiv Detail & Related papers (2023-03-17T17:53:19Z) - Detection of Novel Social Bots by Ensembles of Specialized Classifiers [60.63582690037839]
Malicious actors create inauthentic social media accounts controlled in part by algorithms, known as social bots, to disseminate misinformation and agitate online discussion.
We show that different types of bots are characterized by different behavioral features.
We propose a new supervised learning method that trains classifiers specialized for each class of bots and combines their decisions through the maximum rule.
arXiv Detail & Related papers (2020-06-11T22:59:59Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.