Machine Learning-based Automatic Annotation and Detection of COVID-19 Fake News

COVID-19 impacted every part of the world, although the misinformation about the outbreak traveled faster than the virus. Misinformation spread through online social networks (OSN) often misled people from following correct medical practices. In particular, OSN bots have been a primary source of disseminating false information and initiating cyber propaganda. Existing work neglects the presence of bots that act as a catalyst in the spread and focuses on fake news detection in 'articles shared in posts' rather than the post (textual) content. Most work on misinformation detection uses manually labeled datasets that are hard to scale for building their predictive models. In this research, we overcome this challenge of data scarcity by proposing an automated approach for labeling data using verified fact-checked statements on a Twitter dataset. In addition, we combine textual features with user-level features (such as followers count and friends count) and tweet-level features (such as number of mentions, hashtags and urls in a tweet) to act as additional indicators to detect misinformation. Moreover, we analyzed the presence of bots in tweets and show that bots change their behavior over time and are most active during the misinformation campaign. We collected 10.22 Million COVID-19 related tweets and used our annotation model to build an extensive and original ground truth dataset for classification purposes. We utilize various machine learning models to accurately detect misinformation and our best classification model achieves precision (82%), recall (96%), and false positive rate (3.58%). Also, our bot analysis indicates that bots generated approximately 10% of misinformation tweets. Our methodology results in substantial exposure of false information, thus improving the trustworthiness of information disseminated through social media platforms.

Intelligence at the Extreme Edge: A Survey on Reformable TinyML

The rapid miniaturization of Machine Learning (ML) for low powered processing has opened gateways to provide cognition at the extreme edge (E.g., sensors and actuators). Dubbed Tiny Machine Learning (TinyML), this upsurging research field proposes to democratize the use of Machine Learning (ML) and Deep Learning (DL) on frugal Microcontroller Units (MCUs). MCUs are highly energy-efficient pervasive devices capable of operating with less than a few Milliwatts of power. Nevertheless, many solutions assume that TinyML can only run inference. Despite this, growing interest in TinyML has led to work that makes them reformable, i.e., work that permits TinyML to improve once deployed. In line with this, roadblocks in MCU based solutions in general, such as reduced physical access and long deployment periods of MCUs, deem reformable TinyML to play a significant part in more effective solutions. In this work, we present a survey on reformable TinyML solutions with the proposal of a novel taxonomy for ease of separation. Here, we also discuss the suitability of each hierarchical layer in the taxonomy for allowing reformability. In addition to these, we explore the workflow of TinyML and analyze the identified deployment schemes and the scarcely available benchmarking tools. Furthermore, we discuss how reformable TinyML can impact a few selected industrial areas and discuss the challenges and future directions.