A study analyzes spam content on trending hashtags on Saudi Twitter, finding that approximately 75% of the total generated content is spam. The paper assesses the performance of previous spam detection systems on a newly gathered dataset and proposes an updated manual classification algorithm to improve accuracy. Adapted features are used to build a new data-driven detection system to respond to spammers' evolving techniques. Why it matters: The high prevalence of spam in Arabic content on Twitter necessitates the development of adaptive detection techniques to maintain the quality and trustworthiness of online information in the region.
MBZUAI Professor Preslav Nakov is researching methods to combat fake news and online disinformation through NLP techniques. His work focuses on detecting harmful memes and identifying the stance of individuals regarding disinformation. Four of Nakov’s recent papers on these topics were presented at NAACL 2022. Why it matters: This research aims to mitigate the impact of weaponized news and online manipulation, contributing to a more trustworthy information environment in the region and globally.
MBZUAI researchers introduce FAID, a fine-grained AI-generated text detection framework capable of classifying text as human-written, LLM-generated, or collaboratively written. FAID utilizes multi-level contrastive learning and multi-task auxiliary classification to capture authorship and model-specific characteristics, and can identify the underlying LLM family. The framework outperforms existing baselines, especially in generalizing to unseen domains and new LLMs, and includes a multilingual, multi-domain dataset called FAIDSet.
This paper introduces DetectLLM-LRR and DetectLLM-NPR, two novel zero-shot methods for detecting machine-generated text using log rank information. Experiments across three datasets and seven language models demonstrate improvements of up to 3.9 AUROC points over state-of-the-art methods. The code and data for both methods are available on Github.
MBZUAI researchers release LLM-DetectAIve, a tool for fine-grained detection of machine-generated text across four categories: human-written, machine-generated, machine-written then humanized, and human-written then machine-polished. The tool aims to address concerns about misuse of LLMs, especially in education and academia, by identifying attempts to obfuscate or polish content. LLM-DetectAIve is publicly accessible with code and a demonstration video provided.