Recent analyses have disclosed that existing rumor detection techniques, despite playing a pivotal role in countering the dissemination of misinformation on social media, are vulnerable to both white-box and surrogate-based black-box adversarial attacks. However, such attacks depend heavily on unrealistic assumptions, e.g., modifiable user data and white-box access to the rumor detection models, or appropriate selections of surrogate models, which are impractical in the real world. Thus, existing analyses fail to uncover the robustness of rumor detectors in practice. In this work, we take a further step towards the investigation about the robustness of existing rumor detection solutions. Specifically, we focus on the state-of-the-art rumor detectors, which leverage graph neural network based models to predict whether a post is rumor based on the Message Propagation Tree (MPT), a conversation tree with the post as its root and the replies to the post as the descendants of the root. We propose a novel black-box attack method, HMIA-LLM, against these rumor detectors, which uses the Large Language Model to generate malicious messages and inject them into the targeted MPTs. Our extensive evaluation conducted across three rumor detection datasets, four target rumor detectors, and three baselines for comparison demonstrates the effectiveness of our proposed attack method in compromising the performance of the state-of-the-art rumor detectors.

Sentiment analysis is an important and challenging task in natural language processing. It has been studied for a few decades. Recently, Bidirectional Encoder Representations from Transformer (BERT) model has been introduced to tackle this task and gain very promising results. However, most existing studies on fine-tuning BERT models for sentiment analysis focus on high-resource language (e.g., En-glish or Mandarin). This paper studies the sentiment analysis of Cantonese political posts on Hong Kong local forums. We first collected and labeled posts related to Anti-Extradition Law Amendment Bill (Anti-ELAB) movement in Hong Kong discussion forums. We then examined the performance of dictionary-based sentiment analysis, traditional machine learning-based, fine-tuned BERT and fine-tuned multilingual BERT (mBERT) models. Our results show that fine-tuned mBERT model achieves the best performance on our collected and labeled Cantonese dataset.

Preventing the spread of fake news is one of the most challenging issues in the age of social media. Traditional manual fact-checking (i.e., expert-based and crowd-sourced fact-checking) is time-consuming and labor-extensive, which cannot scale up with the unprecedented amount of dis- and mis-information on social media. Automated fact-checking based on machine learning is a promising strategy to address the scalability issues. Nevertheless, an end-to-end full automated fact-checking system without human supervision is still impractical. A more realistic solution will be developing an Artificial Intelligence (AI)-based system to facilitate the human fact-checkers during the fact-checking process. Therefore, this paper proposes a novel annotation system to facilitate human fact-checkers. With our designed procedures and schema, our developed system can help to improve the efficiency and effectiveness of human fact-checkers by automatically identifying worth-to-check news. We conduct a real-case study to demonstrate that our system can effectively identify worth-to-check news and ease the annotation process with the help of several automatic detection functions.

With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features.

We demonstrate OceanRT, a novel cloud-based infrastructure that performs online analytics in real time, over large-scale temporal data such as call logs from a telecommunication company. Apart from proprietary systems for which few details have been revealed, most existing big-data analytics systems are built on top of an offline, MapReduce-style infrastructure, which inherently limits their efficiency. In contrast, OceanRT employs a novel computing architecture consisting of interconnected Access Query Engines (AQEs), as well as a new storage scheme that ensures data locality and fast access for temporal data. Our preliminary evaluation shows that OceanRT can be up to 10x faster than Impala [10], 12x faster than Shark [5], and 200x faster than Hive [13]. The demo will show how OceanRT manages a real call log dataset (around 5TB per day) from a large mobile network operator in China. Besides presenting the processing of a few preset queries, we also allow the audience to issue ad hoc HiveQL [13] queries, watch how OceanRT answers them, and compare the speed of OceanRT with its competitors.